Looking Outwards from the âCentral Scienceâ: An Interdisciplinary

Chapter 4

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Looking Outwards from the “Central Science”: An Interdisciplinary Perspective on Graduate Education in Materials Chemistry Debra A. Fowler,*,1 Raymundo Arroyave,*,2 Joseph Ross,2,3 Richard Malak,4 and Sarbajit Banerjee*,2,5 1Center

for Teaching Excellence, Texas A&M University, College Station, Texas 77843-4246, United States 2Department of Materials Science & Engineering, Texas A&M University, College Station, Texas 77843, United States 3Department of Physics & Astronomy, Texas A&M University, College Station, Texas 77843, United States 4Department of Mechanical Engineering, Texas A&M University, College Station, Texas 77843, United States 5Department of Chemistry, Texas A&M University, College Station Texas 77843-3012, United States *E-mails: [email protected] (D.A. Fowler); [email protected] (R. Arroyave); [email protected] (S. Banerjee).

The centrality of chemical sciences has long been underpinned by the infusion of ideas from other disciplines. These ideas have contributed in large measure to advancing, accelerating, and expanding the scope of discovery. Recent advances in data analytics can potentially transform the discipline of chemistry and its practice. However, taking advantage of the “Big Data” paradigm requires a distinctive set of skills that are not thus far in the usual repertoire of chemistry or materials science graduate students who typically receive little formal training in handling large data sets. In this Chapter, we outline a recently initiated interdisciplinary graduate program focused on “Data-Enabled Discovery and Design of Energy Materials (D3EM)” that seeks to explore a novel model for interdisciplinary graduate education with a materials chemistry

© 2017 American Chemical Society Waterman and Feig; Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


and materials science core at Texas A&M University. D3EM’s overarching goal is to develop and institutionalize a new training model that produces scientists/engineers grounded in one discipline but who have the professional and technical skills to collaborate and lead interdisciplinary teams, throughout their academic careers and beyond. The contribution seeks to outline our motivations for designing an interdisciplinary program, describes the structure of the program, and provides specific case studies of research projects that have benefited from an interdisciplinary perspective. Some salient aspects of the program include: 1) learning outcomes aligned with critical skills identified by potential employers; 2) a comprehensive e-portfolio to enable students to internalize interdisciplinarity; 3) a capstone Materials Discovery and Design Studio to promote interdisciplinary approaches to the solution of complex materials development problems; 4) the creation of a faculty community of scholars to facilitate the internalization of interdisciplinarity within faculty participants; and 5) a first-year graduate training sequence featuring strong disciplinary grounding followed by an interdisciplinary immersion––to ensure that the student have a sufficient disciplinary foundation.

Introduction: Rethinking Graduate Education in Chemistry Cross-currents from other disciplines have enriched the chemical sciences and brought methods and strategies that over a period of time have become integral to the practice of chemistry. Tools such as nuclear magnetic resonance spectroscopy adapted from the physics community turned out to underpin decades of explosive growth in organic chemistry (1); more recently, sandwich immunoassays developed by biologists have found use for the high-throughput screening of catalysts for organic coupling reactions (2). The structural model of matter that now underpins most physical sciences relies extensively on crystallography, which evolved over several centuries through a curious juxtaposition and unification of optics, mathematical concepts such as group theory and symmetry, and chemical ideas of essential building blocks; indeed, many consider crystallography to be the first scientific “inter-discipline” (3). Of course, disciplinary entrenchment in itself is a relatively novel phenomenon, which paralleled the formalization of academic departments and the post-war evolution of research universities and funding agencies. The recent mapping of academic genealogies (e.g., academictree.org) is particularly striking in the interconnectedness of what are defined as physics, chemistry, biology, and “neuro” trees (4); this interconnectedness is extensive across multiple generations and supports a “continuum” view of science. These mappings show strong cross-fertilization to be the historical norm much more so than the organized––and siloed––academic departments that have become the prevailing paradigm since the beginning of the twentieth century. 66

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In contrast to the discipline-focused organization of the current system, modern research practice and industrial technology development both rely extensively on collaborative interdisciplinary teams (5–7). Indeed, in an influential commentary, Metzger and Zare note that “A substantial part of the history of U.S. research has been written by people who, against substantial cultural if not economic odds, have reached out to other fields, merging different perspectives and creating new ideas, even new fields” (5). Yet integrating interdisciplinarity within undergraduate and graduate curricula has been challenging given systemic and institutional cultures and frameworks. Disciplinary lines have become more sharply defined as an inevitable consequence of both undergraduate and graduate curricula becoming more formalized, in order to adapt to sometimes questionable driving forces for standardization, accreditation and documentation that have taken root across US research universities. An imperative for reconsidering existing chemistry curricula stems from the declining number of academic positions and the perceived inability of graduate programs to prepare students for complex modern professional careers that often place a premium on versatility, agility, and communication, rather on the highly specialized technical knowledge that naturally results from PhD studies (8). The seeds of disciplinary entrenchment are sown at an early stage. Ares has pointed out that the selection of an undergraduate major often locks-in students to a narrowly defined set of skills and perceived career paths with the major becoming a proxy for professional identity (9). Several federal funding agencies have sought to restructure undergraduate and graduate curricula to emphasize interdisciplinarity with varying degrees of success. In the physical sciences, the National Science Foundation Integrative Graduate Education and Research Traineeship (IGERT) program funded approximately 6,500 students from 1998 to 2012 with an emphasis on training students to solve complex problems that transcend traditional disciplinary boundaries, with the implicit expectation that IGERT trainees eventually become the faculty of the future. Studies of IGERT-funded programs have indicated that there have been some levels of success, but also some clear drawbacks. Students were reported to find interdisciplinary research intellectually invigorating and to enjoy the social dynamics and communities of practice, as well as the pursuit of meaningful and important research that has societal implications (5, 10). However, students reported struggles with truly internalizing the interdisciplinary research processs (11, 12). Additionally, IGERTs employing an interdisciplinary curriculum throughout failed to provide students with a sufficient disciplinary foundation (13) thereby rendering them less competitive for professional positions and also less effective at contributing to interdisciplinary endeavors. More importantly, the (implicit) emphasis on academia as the most likely terminal position led in many cases to professional development goals which could not be satisfied in the current university environment when many, many more PhD students graduate than the number of open academic positions. The NSF Research Traineeship (NRT) program has sought to explore new graduate education models that can address the shortcomings of IGERTs and that can better prepare students for modern professional STEM careers. In this Chapter, we outline a recently initiated NRT program focused on “Data-Enabled 67



Discovery and Design of Energy Materials (D3EM)” that seeks to explore a novel model for interdisciplinary graduate education with a materials chemistry and materials science core at Texas A&M University. D3EM’s overarching goal is to develop and institutionalize a new training model that produces scientists/engineers grounded in one discipline but who have the professional and technical skills to effectively communicate within their disciplines, as well as collaborate and lead interdisciplinary teams, throughout their academic careers and beyond. While the program is thematically organized around materials science and materials chemistry, D3EM seeks to equip trainees to transfer their knowledge and skills to other highly complex challenges that require creative, unconventional approaches. In subsequent sections, we shall seek to outline our motivations for designing an interdisciplinary program, describe the structure of the program, and provide specific case studies of research projects that have benefited from an interdisciplinary perspective. Longitudinal studies of the effectiveness of the program will be reported at a later date upon the conclusion of the first phase of the program and will include assessments performed independently by an external evaluator.

Motivation: Of Data Mirages and Data Oases… The advent of microprocessors revolutionized the discipline of analytical chemistry and in doing so empowered chemists of all hues by tremendously accelerating the design and identification of molecules and extended solid-state compounds alike. Advances in computing power underpinned the development of rigorous quantum chemical descriptors, enabled unambiguous structure determination of molecules and compounds of unprecedented complexity, and allowed for measurements to push the envelope in terms of spatial, temporal, and spectral resolution. As a discipline, chemistry today ubiquitously involves large data sets whether they be eigenstates outputted by a desktop quantum chemical program, stacks of intensity values across tens of thousands of pixels obtained from hyperspectral imaging, fragmentation data obtained from mass spectrometry, nucleic acid sequences obtained from organisms, or libraries of compounds generated from diversified synthesis, to provide a rather non-exhaustive list. Our increasing facility at generating large data sets has led to new challenges and opportunities. Analogous to the microprocessor-driven revolution in characterization, machine learning and data analytics can potentially transform the discipline of chemistry and greatly accelerate the exploration of multidimensional parameter space. The sheer volume of data generated by computations and experiments can overwhelm chemical intuition and yet mining the data in a chemically meaningful manner is oftentimes rather difficult. As a specific example, modern materials chemistry research has produced large databases of structures and properties increasingly powered by advances in high-throughput and combinatorial experimental research and simulations. However increasing complexity will clearly limit the ability of such databases to expand to encompass materials needs for future applications. For instance, only about 5% of 160,000 possible 68



ternary materials are known and more than 99% of the estimated 4,000,000 possible quaternaries remain completely unexplored (14). The unexplored compositional space potentially includes materials that are solutions to our abiding problems preventing us from sustainably meeting the energy needs of the planet: room-temperature superconductors, high-zT thermoelectrics, cathode materials and electrolytes for multivalent intercalation batteries, earth-abundant catalysts that split water into solar fuels, etc. Synergy between computational materials chemistry, mining of digital data, and targeted materials processing and measurement has the potential to accelerate discovery as well as progression from the initial design of a material to its integration in commercial technologies by orders of magnitude. Recognizing this challenge, the Materials Genome Initiative (MGI) (15) championed by the White House calls for the synergistic combination of experiments, simulation, and data in order to accelerate the discovery of new materials enabling transformative technologies Taking advantage of the “Big Data” paradigm requires a distinctive set of skills that are not thus far in the usual repertoire of chemistry or materials science graduate students who typically receive little formal training in handling large data sets. Data-cognizant scientists need to not just acquire data, but to meaningfully fuse complex and disparate data, identify trends, and design probabilistic machine learning models for the goal-oriented development of materials. In order to gauge the needs of potential employers, we undertook an extensive survey of critical skills sought by employers. The survey included 65 potential employers (12 academic, 28 private, 23 government, 2 nongovernmental entities); respondents were invited to list the technical and professional skills that were most critical for graduates they wished to hire (Table 1). Interestingly, the responses reflect the need to balance interdisciplinarity with core knowledge and to develop both technical and professional skills. In addition, to expanded technical skills, essential professional skills need to be better addressed in a graduate training model. The findings are further resonant with comments from former graduate students mentored by the authors. These students have heard explicitly from potential and eventual employers that experiences with interdisciplinary and industrial collaborations are particularly attractive characteristics; the students have further stated that their own involvement with interdisciplinary collaborations was particularly useful since it forced them to articulate their arguments free of jargon, listen to arguments from entirely different disciplines couched in a distinctly unfamiliar framework, and allowed them to adopt specific roles. Indeed, the literature likewise reports that higher-order cognition is a major benefit of cooperative learning (16). These findings corroborate ideas articulated in the current literature on interdisciplinary training in general (11), and IGERTs in particular (17). These results, along with other studies of IGERT programs described above, as well as previous experience with these programs within our own team have led to our primary hypothesis that students can make the most effective contributions to interdisciplinary research only when they are well trained in their disciplines. We posit therefore that interdisciplinarity must begin with grounding in a chosen traditional discipline and this approach forms one of the cornerstones of our program (17). 69



Table 1. Top desired professional and technical skills and experience identified by potential employers of D3EM trainees

The D3EM program at Texas A&M University is structured to address three main challenges. First, the need to train research professionals and entrepreneurs who can “seize the data moment” to accelerate the materials discovery-development-deployment cycle across academia, government, and industry (15, 18); secondly, the need to train scientists and engineers with the necessary skills to transform data into knowledge without being blinded by the ‘“Big Data” mirage—i.e., mistaking data for knowledge (19)—and to effectively utilize this knowledge in the discovery and design of novel energy materials; and finally, the need to educate scientists and engineers who truly internalize the interdisciplinary research process, a problematic issue for many IGERTs (11, 12). As established, D3EM seeks to train the participating students to become skilled at creating and applying innovative data-enabled approaches with sound informatics and engineering design foundations to the discovery, design, and deployment of advanced materials, in particular those that enable the efficient and sustainable generation, storage, or utilization of energy.

Goals and Desired Outcomes: Some Wishful Thinking... Based on external input, discussions engaging about 15 faculty members at Texas A&M, and a rigorous review of the literature, a wishlist of student outcomes has been developed. The D3EM program seeks to prepare students who 1) are grounded in their own disciplines; 2) are capable of applying tools and methods from other disciplines in their own fields; 3) are able to translate tools developed in their own disciplines to solve problems in other fields; 4) can communicate with experts in other fields; 5) can effectively contribute to interdisciplinary efforts while developing a comprehensive understanding of the potentials and limitations of their own as well as other disciplines; and 6) and have the skills necessary to thrive in their chosen career path. Through rigorous evaluation of the program as it proceeds and of student outcomes, we hope to develop a good understanding of the impact of disciplinary grounding on interdisciplinary research vis a vis the IGERT model. We further 70


expect to evaluate how direct reflection and early-academic-career interventions can influence interdisciplinary learning. Finally, a key aspect of the program is a “community of scholars” designed to connect and engage faculty mentors. We hope to be able to elucidate whether an explicitly constructed interdisciplinary faculty group enhances interdisciplinary learning of D3EM trainees.


Structure of the Program: Nuts and Bolts… The D3EM program was launched in 2016 funded by a NSF NRT grant with an initial cohort of 6 students and was designed to specifically address the needs outlined in preceding sections. Distinctive characteristics of the program include: 1) learning outcomes aligned with critical skills identified by potential employers (Table 1) (17, 20); 2) a highly structured and comprehensive e-portfolio to enable students to internalize and reflect on the learning process to explicitly promote metacognition; 3) a capstone Materials Discovery and Design Studio that develops strong collaboration skills and promotes interdisciplinary approaches to the solution of complex materials development problems; 4) the creation of a faculty community of scholars to facilitate the internalization of interdisciplinarity within faculty participants; 5) a one-of-a-kind international school in computational materials science/informatics/engineering design; 6) leadership experience gained through mentoring new cohorts as well as advising undergraduate design teams; 7) a first-year graduate training sequence featuring strong disciplinary grounding followed by an interdisciplinary immersion––to ensure that the student have a sufficient disciplinary foundation; and 8) a variety of self-selected programs and training modules offering insight and experience in entrepreneurship, business modeling, innovative product design, and customer development (Lean Startup/I-Corps). Figure 1 depicts the interdisciplinary framework, key desired outcomes, distinctive characteristics, and typical timeline for progression to a degree for the D3EM program. The major programmatic organization and the projected funding schemes for students are outlined in Figure 2. The program consists of a first academic year (Fall/Spring) of disciplinary grounding followed by a summer of technical and professional skill building (summer school in computational materials science, initiation of e-learning portfolios). The second year consists of interdisciplinary integration, followed by an internship. Ph.D. students continue their involvement with the program by participating in required activities related to mentorship, leadership, professional/career development, and/or energy/entrepreneurship academic activities. The D3EM program is designed to fit within departmental degree requirements, with elements of the interdisciplinary curriculum tailored to match elective requirements in participating departments. Therefore the program will not increase time to graduation. While only a subset of trainees receive stipends through the program (five trainees in Year 1 and nine in each subsequent year), the programmatic offerings are not restricted to fellowship recipients; indeed the program has been explicitly designed to accommodate 20 or more trainees each year for an expected minimum of 80 trainees impacted by D3EM during the funding period. A certificate program is in the process of 71 Waterman and Feig; Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


administrative approvals and participants (not just fellows) have the opportunity to obtain a distinctive certificate in data-enabled design of materials in addition to their graduate degrees.

Figure 1. Data-Enabled Discovery and Design of Energy Materials (D3EM) at a glance. Reproduced with permission from http://d3em.tamu.edu/. Copyright 2016 D3EM.

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Figure 2. Typical D3EM training timelines, integrating disciplinary grounding in the first year with a strong interdisciplinary curriculum starting in the second year with a number of components to build interdisciplinary and professional knowledge and skills––over the first summer––followed by interdisciplinary integration. Reproduced with permission from http://d3em.tamu.edu/curriculum/. Copyright 2016 D3EM.

Year 1: Getting the Basics Right As noted above a central hypothesis of our proposed interdisciplinary framework is that interdisciplinarity must begin with grounding in a chosen traditional discipline (17). Therefore, in their first year, the Ph.D. students will enroll in traditional disciplinary courses. At Texas A&M, chemistry graduate students are expected to take four formal courses in their first year. During this year, the students are funded through existing university mechanisms for entering graduate students, including teaching and graduate assistantships or fellowships awarded by the university or by other institutions. Although the students are expected to grow their expertise within their discipline during this first year, D3EM activities also begin to expose them to colleagues and scholars in adjacent disciplines. Conversations begin with monthly coffee discussions led by faculty in the varying disciplines related to topics such as setting goals and expectations, time management, and dealing with conflict, along with the D3EM colloquia focused on current research, allowing students to begin to know one another as a cohort, as well as the participating faculty, and to develop awareness of the language, methods, and current issues relevant to materials informatics. The first cohort has taken a leadership role in structuring these sessions and indeed students have taken the initiative to begin an independent student organization. During year 1, the students remain associated with their primary research advisors and begin graduate research focusing on topics wherein the confluence of data, design, and materials science can yield a significant contribution. The faculty Community of Scholars meetings meanwhile help in establishing interdisciplinary collaborations and allow faculty to identify areas of common interest, especially as it pertains to explicit application of informatics and engineering design tools to materials chemistry problems. 73



A D3EM formal student learning community cohort is organized during the spring semester of the disciplinary grounding year. Colleagues from the Center of Teaching Excellence have taken the lead in structuring this community, defining topics of interest, inviting experts from different areas, and generating a framework for instilling and internalizing interdisciplinarity. The learning community further provides a forum for students to explicitly reflect on the perspectives and limitations of their disciplines. By focusing on interdisciplinary communication early in their graduate careers, the community seeks to tackle perhaps the single largest impediment to interdisciplinary research. At the conclusion of the first academic year, D3EM trainees attend the Texas A&M University International Summer School in Computational Materials Science––which forms an essential element of the D3EM experience and provides trainees with a global perspective as approximately half of the participants usually come from institutions in other countries. This school has been offered annually since 2012 and has thus far trained over 75 students from more than 21 different institutions within the US and from 14 different countries. The school brings leading researchers from across the world and focuses on multi-scale computational materials chemistry, informatics, data analytics, and computer-aided materials design. Lecture videos and notes––equivalent to a 3 credit hour course––are made freely available through the school’s website and have been accessed thousands of times from locations around the world. In other words, the school serves as a “bootcamp” and within a very short time equips students with the toolset required to integrate “Big Data” analytics with chemical and materials research. Another critically important element that has been initiated in the first year is a structured self-reflection exercise (21). Prompted by thought-provoking questions and problems, the students are invited reflect on a regular basis and record thoughts in an electronic integrated learning portfolio (22) that documents and synthesizes learning experiences, serves as a core of their individual development plan (IDP), promotes metacognition, provides evidence of learning throughout the program, and provides a valuable resource as students seek employment (23). Key personnel with teaching and learning expertise are overseeing the portfolio creation process in which the students articulate what they have learned, why it matters, and how they will utilize the knowledge in the future. The structured electronic portfolio is thus a valuable and entirely scalable tool to improve internalization of the interdisciplinary research process.

Year 2: Stepping It Up a Notch... Beginning in the fall of their second year, the D3EM curriculum is designed to formally expose students to the content that forms the basis for interdisciplinarity in materials, engineering design, and informatics. D3EM’s curriculum includes a core of four courses required of all students. These supplement the disciplinary coursework required by individual departments. In particular, the curriculum exposes chemistry and materials science students to a pair of formal courses in informatics and engineering design. In its first iteration, the informatics course 74 Waterman and Feig; Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


has served as a valuable vehicle for equipping students with coding and data analytics skills oftentimes entirely absent from chemistry and materials curricula. Similarly, the advanced product design course has provided students valuable exposure to industrial design specifications and by using a project-based approach has allowed students to experience the metabolic processes of a fast-paced startup. Having obtained a good feel for both disciplinary and interdisciplinary research and having integrated themselves within the D3EM community, the students will further proceed at this point to put together their doctoral advisory committees, which will include multiple members from amongst D3EM faculty. In each case this committee will play a vital role in assisting the student to define their lines of inquiry and to ensure disciplinary rigor across the multiple disciplines. A linchpin of the D3EM program is a semester-long Materials Studio Course. This course emphasizes experiential, problem-based learning and is specifically focused on preparing trainees for the next stage of their careers as they begin to establish the direction of their research. The course is intended to be a targeted follow-on to the Advanced Product Design course and seeks to actively and explicitly promote interdisciplinarity. Students in interdisciplinary teams will work on real-world materials problems defined by industrial and governmental partners in consultation with D3EM faculty, or linked to existing interdisciplinary research at TAMU. This approach was inspired by the Harvey Mudd Clinic, which uses externally sponsored projects to improve learning (24). Two particularly effective laboratory courses (albeit in very different disciplines) implemented at the University of Wisconsin, Madison and the University of California, Santa Cruz further serve as models for the incorporation of authentic practice, interdisciplinary experiments, and cooperative learning (9, 25). This studio was also motivated by Hackett and Rhoten who established the difficulty experienced by many IGERT students in internalizing the interdisciplinary research process (11). The Design Studio combines weekly seminars with studio sessions involving project teams, advisors (usually members of previous D3EM cohorts), and faculty instructors. The course is team-taught by D3EM faculty and also features guest lectures. The seminar provides a forum for instruction on important topics including: 1) ethics and responsible conduct of research; 2) societal challenges and their relationships to materials design; 3) methods to formulate interdisciplinary research problems; 4) entrepreneurial and start-up methodologies; and 5) relevant technical issues in materials design. A major objective is to train students to formulate difficult problems and open questions arising in materials design. Each team tackles problems provided by industry and national laboratory partners with “real world” datasets and is to be mentored by D3EM faculty. The goal is to solve materials development problems by integrating computational/experimental approaches and by using design theories as well as informatics approaches. At the end, the teams are expected to present and document either i) a basic science research proposal; ii) an industry technical feasibility report; iii) small business innovation research (SBIR)-type proposal for technology development; or iv) NSF I-CORPS proposal, depending on the interests of teams and technology readiness level of specific projects. The resulting document will be reviewed by 75



a panel of researchers from academia, industry, or national laboratories. Short modules have been provided by the Texas A&M Mays Business School’s Center for New Ventures as part of the course to instill an appreciation for entrepreneurial approaches. A detailed description of the studio course including case studies of specific problems tackled and outcomes will be documented at the conclusion of the first phase of the program. Beyond the second year, PhD students enrolled in the program are encouraged to continue their participation through continuing professional/career development activities, mentoring of junior D3EM trainees/undergraduate students and/or by taking courses in entrepreneurship or energy-related issues through D3EM’s partnership with the Center for New Ventures. The role of more advanced students in the evaluation process will also be critical in evolving the D3EM program. By identifying particular impediments, the program will be restructured to better provide students with the relevant tools to succeed in interdisciplinary research. The students will complete an internship for a minimum of three months, which will provide them with a situated learning experience involving real-world materials design and informatics problems with societal value. Clear objectives and desired outcomes, tailored to student interests and host needs will be developed prior to the internship. After the internship, those objectives will be assessed. Students will be asked to submit a reflection of the internship experience and further encouraged to continue to document their intellectual growth using the e-portfolio.

A Walk in Her Footsteps: Progression through the Program A student obtaining a BS degree in Chemistry has been accepted to the D3EM trainee program and desires to complete an interdisciplinary Ph.D. in Chemistry supplemented by experience in Informatics, and Engineering Design. The student receives funding for the first year through a departmental teaching assistantship in chemistry and begins the traineeship through disciplinary grounding––e.g., by taking two foundational courses in materials and analytical chemistry offered by the Department of Chemistry in her first semester where she also selects a research advisor. She receives advice from D3EM faculty and starts designing her individual development plan. During the first year, she begins graduate research under the supervision of her primary advisor and becomes involved in ongoing research activities, perhaps related to new materials for batteries. The student begins to develop fundamental reflection and communication skills. She records her experience as entries into her electronic integrated learning portfolio (22). The reflections are shared with her D3EM colleagues through biweekly discussions where communication skills are further developed. Once a month during the first year, the student attends a colloquium presented or hosted by D3EM faculty followed by further discussion with trainee colleagues and reflection in her journal. During summer 1, the student joins an interdisciplinary learning community where experts address topics such as ethics, programming, databases, and desired topics identified by the trainees. Interdisciplinary collaboration and 76



critical thinking techniques are explored and practiced along with mentoring from the D3EM faculty, who themselves have been meeting throughout the year as a Community of Scholars. The student further attends informal coffee sessions, learns about the research of her colleagues from other disciplines, and gets involved in the student organization. The trainee attends the International Summer School in Computational Materials Science. Reflection continues and is documented in the learning journal and shared through social media. During this time, she gains more experience and focus in her research. Interdisciplinary coursework begins in the fall including D3EM core courses in Advanced Product Design and Materials Informatics, which set the stage for the capstone Materials Design Studio in the Spring. During the fall, the student further picks a doctoral advisory committee based on her research problem and sets up a preliminary meeting with the committee to better define lines of inquiry. The NRT trainee participates in an internship at a national laboratory during the second summer, capturing experiences and writing about how those experiences relate to her interdisciplinary framework and can be capitalized during future education and experiences. A rubric describes the performance goals and expectations of the internship and are reviewed with the trainee by the D3EM mentor and agreed upon by the employer prior to beginning the internship. A focused interdisciplinary research plan becomes the target goal in the fall of the third year while the trainee begins developing leadership skills through mentoring undergraduate research projects. She submits and defends her dissertation proposal wherein based on research under her primary advisor, as well as input from D3EM advisory committee members and internship supervisor (where appropriate), she formulates a plan for the completion of the dissertation research. The NRT trainee is also presented with the opportunity to add a concentration in entrepreneurship by taking courses offered by the Center for New Ventures and Entrepreneurship during Years 3—5 of her PhD. It is expected that the student then navigates the academic cultures of multiple disciplines to publish meaningful research in premier peer-reviewed journals that provide a jargon-free understanding of the opportunities available at the “big data” and chemistry interface. It is expected that upon successful completion of her PhD dissertation, the trainee is well equipped for a variety of career paths and is able to navigate her way across changing landscapes. In addition to her technical skills, she has acquired leadership skills, coupled with a broad knowledge base.

Evaluating Progress: A Yardstick for Measuring Interdisciplinarity Structural aspects of the program including assessments and curricular activities are correlated to desired student learning outcomes as shown in Table 2. Each learning outcome was further defined using desired performance criteria expected across developmental levels as the student progresses (competency rubrics). These competency rubrics are then used for assessment and self-auditing purposes. Outcomes in the table have also been matched to desired skills identified 77 Waterman and Feig; Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


in Table 1. A means of assessment has been identified for each learning outcome following a specific curricular activity. These outcomes will be variously assessed by D3EM faculty and implementation team members, the doctoral dissertation committee, peer students, and/or the external evaluator. One example of a learning outcome assessment concerns the materials science core disciplinary component of the desired interdisciplinarity. Assessment involves the faculty advisor measuring the knowledge and skill development of a student. The faculty advisor identifies the current strengths as well as gaps in knowledge and skills for each of several performance indicators defined in the competency rubric, and suggests recommendations for improvement if gaps exist (Figure 3). This assessment opens the opportunity for dialogue around one of the key disciplinary components and the expectation is that the assessment will lead to better understanding and shared expectations among the faculty and students. Several professional skills were identified by potential employers as important for a graduate’s success and these skills are not always easy to measure. A competency rubric for each skill was carefully crafted starting with examples from best practices in the literature and edited by the D3EM key personnel. Specific curricular activities and assessments for each professional skill outcome are listed in Table 2. The overall program evaluation includes surveys of the faculty participating in the community of scholars as well as surveys completed by the students participating in the program.



Table 2. Learning outcomes for D3EM trainees, with corresponding technical (ts) and professional (ps) skill



Figure 3. A rubric designed to address strengths and gaps in student understanding of materials science and materials chemistry.


Case Studies in Interdisciplinarity


While the D3EM program is barely a year old, we present here a brief synopsis of several research findings that it has enabled. These short case studies provide a glimpse of bringing to bear data science and engineering tools to chemical and materials design problems. The size of the data sets in this initial set of studies is relatively modest in comparison to truly large sets used for instance in analysis of retail preferences or diagnostics of genome information. It is anticipated that with refinement of data analytics tools as well as the development of high-throughput approaches for data acquisition, much larger data sets will become available and be amenable to similar analysis. Example 1. Reversing the Process−Structure Paradigm: An Inverse Design Framework The materials discovery/development cycle essentially comprises the exploration of a material design space in order to identify the chemistry/processing conditions that result in optimal (micro)structural and/or morphological features that in turn yield targeted/desirable property/performance metrics. Due to the highly complex relationships between processing conditions and (micro)structure and morphology, the determination of optimal materials processing schedules relied extensively on prior work, extensive Edisonian exploration of the materials design space, and/or intuition built through decades of experience. This traditional approach has clearly been effective in both materials chemistry and materials science, as can be attested by the vast number of technologies enabled by materials discovery over the past centuries. There are, however, limits to this approach, particularly when one considers that technology development often occurs at time-scales that can be several times faster than the normal materials development cycle. In recent years, there have been significant efforts towards the acceleration of the materials development cycle. As mentioned above, the advent of the Materials Genome Initiative has energized this area of research even further (15). D3EM seeks to explore different aspects of this challenge through the development of concepts and tools organized along the process—structure—property—performance (PSPP) paradigm (Figure 4) (26). Within the PSPP paradigm, considerable effort has been spent on the forward problem connecting processing to structure, structure to properties or properties to performance. On the computational front, much work has in fact been invested on identifying the response/properties of specific chemical structures (materials chemistry) or microstructures (materials science). While there has been some progress on the connection of property/performance metrics to (optimal) microstructures (27), not much work has been carried out towards solving the inverse problem of connecting desired/optimal microstructures (or structural motifs) to their corresponding processing schedules. In the remainder of this section, we will briefly describe some efforts carried out by faculty and students of the D3EM collaboration towards the solution to these types of problems (26). Here, we discuss the specific case of materials 81 Waterman and Feig; Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


microstructural features characterized by the presence of nanoscale secondary phases within a matrix. The secondary phases in this type of microstructural motif usually form through precipitation reactions from a metastable solution phase. The precipitation of secondary phases from a metastable matrix can be understood essentially as one involving nucleation and growth kinetics (28). Since nucleation events keep occurring as long as the thermodynamic force for the formation of a secondary phase is not exhausted, the resulting microstructure consists of a matrix enclosing a population of secondary particles following a size distribution that depends not only on nucleation and growth but also on the coarsening of individual particles as the system seeks to minimize excess interfacial energies by growing larger particles at the expense of the smaller ones (29). Since precipitation is a highly non-linear process, the final shape of the size distribution in a population of precipitates is very sensitive to specific processing conditions (i.e., time—temperature histories). In the context of computational materials science, this specific problem has already been formalized as the so-called numerical Kampmann—Wagner model (29). Within the NKW framework, the population of precipitates is organized in terms of bins corresponding to the time interval during which a population of particles nucleated. The model follows each (sub)population of particles by growing them according to local growth laws derived from fundamental kinetic theories and also accounts for coarsening as a result from curvature-driven differences in chemical potentials between different sub-populations of particles (30). The actual solution to the NKW model is mathematically that of a system of non-linear ordinary differential equations and for a given time-temperature history it is possible to predict a distribution of particle sizes.

Figure 4. The forward and inverse problems in materials science development. A much more difficult problem is that of identifying the temperature—time history necessary to produce a specific, pre-defined distribution of particle sizes. This is in essence an inverse problem in that there may be a vast number of solutions––i.e., temperature histories––that yield very similar precipitate populations. Mathematically, the problem can be framed in terms of three major components: (1) implementation of the NKW model to predict nucleation, growth and coarsening of secondary precipitate phases as a function of arbitrary thermal 82 Waterman and Feig; Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


histories; (2) development of a unique metric that is able to provide an indication on how close/far is a given precipitate distribution; and (3) the search/optimization framework necessary to navigate the materials design space. In Figure 5 we illustrate the framework developed by D3EM faculty and students to address this issue as applied to the problem of engineering precipitate distributions in NiTi-based shape memory alloys, a very important class of active materials whose response can be tailored through microstructural design. Figure 6 illustrates the performance of the proposed framework against synthetic (i.e., target) precipitate distributions. Preliminary results suggest that the framework is able to develop robust solutions to this very specific structure-processing inverse problem.

Figure 5. Proposed framework for the prescription of heat treatments yielding specific precipitate distributions in heterogeneous NiTi-based shape memory alloys.

Figure 6. Precipitate size distributions of two distinct thermal histories attempting to match distribution marked as square markers. Reprinted from Materials & Design, 107, Johnson, L.; Arroyave, R., An inverse design framework for prescribing precipitation heat treatments from a target microstructure, 7-17, Copyright (2016), with permission from Elsevier. 83 Waterman and Feig; Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Example 2. Tracking Li-Ion Diffusion in a Cathode Material The second case study focuses on understanding the mechanisms by which a potential cathode material for Li-ion batteries is transformed upon the reversible insertion of Li-ions. V2O5 is a layered transition metal oxide that serves as a classical intercalation host for cations (31–33). The insertion of Li-ions results in reduction of the pentavalent vanadium sites with the intercalated ions residing in interstitial sites between the layers where they are coordinated by the oxide anions from [VO5] polyhedra. Initial intercalation events result in localization of electron density and the formation of small polarons, thereby establishing lithiation gradients within particles (34). Subsequent intercalation of Li-ions is accompanied by a series of structural transformations as the structure seeks to make room for a higher concentration of Li-ions by expanding the interlayer spacing and puckering as well as sliding of the individual layers (35, 36). Each phase is stable for a specific range of Li-ion concentrations x in LixV2O5. In an electrochemical cell, Li-ion intercalation proceeds rather heterogeneously with different particles lithiated at different rates depending on their size, defect density, and distance from the electrode. This heterogeneity is responsible in large measure for many electrode materials not reaching their theoretical capacity. The analysis of a lithiated sample is thus rather complex since it is almost always a phase mixture of materials with different Li-ion stoichiometries oftentimes also crystallized in different phases. In past work, we have shown that even individual nanowires can show considerable heterogeneity of lithiation across their cross-sections (34). Analyzing a complex heterogeneous mixture by spectroscopy or imaging methods thus represents a formidable challenge. However, recognizing that each phase of LixV2O5 with a specific stoichiometry x will have its own distinct spectroscopic signature provides a means to follow intercalation/deintercalation processes, which of course is of tremendous importance for mechanistic elucidation and cathode design. The challenge therefore is to chemometrically deconvolute measured spectra to chemically meaningful combinations of spectral signatures. X-ray absorption spectroscopy is an excellent element-specific probe of both local geometric and electronic structure of transition metal oxides (37, 38). By using data analytics, specifically principal component analysis and multivariate curve resolution in conjunction with first-principles density functional theory calculations, we have identified V K-edge X-ray absorption spectral signatures of different phases of LixV2O5 (Figure 7). A chemical lithiation process, modeling Li-ion intercalation within a battery, is then analyzed using V K-edge X-ray absorption spectroscopy (39). Deconvolution of the spectral data suggest that at low concentrations of Li-ions, the V2O5 nanowires are homogeneously converted to a low-lithium-content (x ~ 0.1) α-phase; with a further increase of lithium concentration, a high Li-content ε-phase is nucleated. Increasing the Li-ion concentration thereon brings about an increase in the concentration of the ε-phase at the expense of the α-phase with a clear two-phase progression observed in this concentration regime. Although the changes in the pre-edge features are subtle, the application of deconvolution methods to spectra acquired in triplicate allows for detailed insight into the structural progression induced by lithiation without 84 Waterman and Feig; Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate ... ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


need for standard samples (Figure 7G). Data analytics further inform speciation of lithiated species visualized from scanning transmission X-ray microscopy acquired at V L- and O K-edges (Figure 7C and D). In this measurement, transmission X-ray spectra are acquired at each ca. 30 x 30 nm pixel yielding an image stack of hyperspectral data. Singular value decomposition of the image stack using specific spectral components identified by region of interest analysis allows for mapping of the weight of each spectral component at each pixel as illustrated for the spectra in Figures 7E and F in Figures 7C and D, respectively. The spectral mapping clearly illustrates phase segregation of Li-rich ( ε-phase) and Li-poor (α-phase) domains. In other words, data analytics allows for the construction of a chemically meaningful model of structural progression from complex and highly heterogeneous spectral and imaging data (39).

Figure 7. An example of the application of data analytics to examine the mechanism of lithiation of V2O5. A) V K-edge XANES spectra of LixV2O5 with varying extents of incorporated lithium are treated with a multivariate curve resolution approach that allows for the identification of three components as illustrated in (B). MCR spectra represent the total variances in the spectral data and do not directly correspond to specific spectral signatures but the components can be assigned as being characteristic of specific vanadium oxidation states and symmetries. Component 3 has spectral features characteristic of unlithiated V2O5; component 2 corresponds to low-lithiated α-LixV2O5; whereas component 1 is characteristic of a relatively highly lithiated ε- or δ-phase. (C) and (D) represent scanning transmission X-ray microscopy intensity maps for the spectral components shown in (E) and (F), respectively; the X-ray absorption spectral components are identified based on singular value decomposition of spectral stacks. The images suggest a two-phase behavior with the interior 85



corresponding to a higher extent of lithiation. (G) Schematic illustration of lithiation mechanism. The V2O5 nanowires are initially homogeneously lithiated to the low-lithium-ion α-phase. Subsequently, further insertion of Li ions results in supersaturation of the low-concentration phase, resulting in nucleation of the high-Li-ion concentration phase at the nanowire edges (as discernible in Figure 7C and D). This high-lithium-ion phase then grows at the expense of the low-lithiated phase until full conversion is achieved. Adapted from Reference (39). Copyright 2016 American Chemical Society. In ongoing work, spectral imaging of large data stacks is being used to develop a multiscale model for intercalation/deintercalation of Li-ions from a model cathode material. Machine learning is further being used to delineate synthetic conditions for hydrothermal synthesis of metastable vanadium oxide compounds by rapidly mapping multidimensional parameter space. Detailed mechanistic elucidation is further permitting the design of novel cathode materials and cathode architectures specifically designed to mitigate bottlenecks to Li-ion intercalation and diffusion.

Prospects and Some Concluding Remarks The chemical sciences have continuously evolved to embrace the opportunities made available by adjacent disciplines. The emergence of data science and its intersection with the physical sciences provides tremendous opportunities for addressing the inherent complexities of chemical and materials design particularly from the perspective of inverse design directed at meeting functional requirements as needed for specific technological applications. The D3EM program at Texas A&M is taking aim at piloting a new interdisciplinary model for STEM doctoral education that produces scientists grounded in one discipline but who have the professional and technical skills to effectively communicate, collaborate, and lead interdisciplinary teams, as is increasingly required in both academia and industry. As tectonic shifts of the global economy dramatically alter the landscape of desired skills and the trajectories of professional careers, we are seeking to emphasize versatility, agility, and communication, combined with deep disciplinary grounding. The D3EM model has been designed based on detailed discussions and surveys of potential employers, a review of the strengths and failings of past models for interdisciplinary graduate education, and is inspired by feedback provided by former graduate students mentored by the authors. A rigorous formative and summative evaluation framework has been designed to ensure that the model can be continuously refined to meet the overarching objectives of preparing students who can effectively communicate beyond disciplinary boundaries and who are well positioned to take advantage of tools developed in adjacent disciplines. An important ongoing endeavor is to consider opportunities and challenges for scaling this model beyond the cohorts funded by the NRT grant. Indeed, the introduction of a certificate program has allowed extension of the model to students not directly supported by the grant. Several components of the program, for instance, courses in materials informatics and the studio 86


course have already received widespread interest across campus and have allowed graduate students to access several of these components on an a la carte basis. Ongoing efforts are directed at expanding the scope of the program with the help of industrial, state, and federal support. While this contribution seeks to outline the motivation and design of the program and to highlight its specific components, future contributions will report on our qualitative and quantitative learnings and the success of this model in instilling interdisciplinarity both in the students and involved faculty.


Acknowledgments The authors acknowledge support of the Data-Enabled Discovery and Design of Energy Materials (D3EM) program by the National Science Foundation under Award DGE-1545403. SB acknowledges the Research Corporation for Science Advancement for Cottrell Scholar and Scialog Awards that have allowed for experimentation with interdisciplinary research and education. We thank Jodie Lutkenhaus, Doug Allaire, Patrick Shamberger, Ed Dougherty, Miladin Radovic, and Hong-Cai Zhou for their thoughtful comments and contributions to designing this program.

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