Editorial Cite This: J. Chem. Inf. Model. XXXX, XXX, XXX−XXX
pubs.acs.org/jcim
Materials Informatics
I
Initiative (https://www.mgi.gov/) and the Clean Energy Project (http://cleanenergy.molecularspace.org/). The information contained within these databases could be used to form the material hyperspace that needs to be navigated in search for new materials with desired properties. A second contributor to the development of materials informatics comes from highly related and historically welldeveloped disciplines such as chemo- and bioinformatics. Thus, this young field could draw from the experience and large pool of tools developed in the more established fields. Indeed, the excursion through the materials informatics literature suggests that the fingerprints of cheminformatics are clearly visible in many elements of materials informatics including the reliance on “best practices” for data processing workflows and the expressed need to develop novel materials descriptors and employ them for building computational models to accurately predict materials properties. Despite many methodological and conceptual similarities, materials informatics (and a closely associated area of nanomaterials informatics) faces its own unique challenges. These include the following: (1) The diversity of materials and the modeled end points. While this diversity is important for highlighting the scope of the field, it comes with a price tag related to the database uniformity and size. Indeed, some of the databases are difficult to mine either individually or in combinations with other databases, and some are too small to lend themselves to statistical modeling. (2) A second consequence of the diversity in materials and end points is the need to develop new and often unique procedures for data curation. (3) The nature of the materials to be modeled (e.g., crystals, alloys, composite compounds, mixtures) necessitates the development of unique descriptors some of which must be based on very expensive and relatively slow, quantum mechanics calculations. (4) The diversity of the material space characterized by these descriptors requires the development of new visualization techniques and new analytical (i.e., machine learning) tools. Clearly, rapid and impressive progress notwithstanding, a lot of work needs to be done to fully establish and expand the field of materials informatics. Mindful of the emergence of materials informatics and the natural connectivity between this field and that of cheminformatics, we propose to develop a special issue of this journal dedicated to materials and nanomaterials informatics. The purpose of this special issue would be to provide a rigorous “snapshot” of contemporary materials informatics. Thus, we welcome perspectives, review articles, application notes, and original research articles covering one or more of the following topics: (1) the development, implementation, and decimation of materials and nanomaterials databases, (2) the unification and curation of (nano)materials databases, (3) the development of (nano)materials-specific structural descriptors, (4) the visualization of the materials space, (5) the development of materials informatics specific workflows, (6) application of data
nformation science methodologies are key to bridging the gap between data creation, capture, and storage on one hand and the derivation and utilization of knowledge on the other, in any research area. Historically, application of such methodologies in many fields has led to the emergence of several new computational disciplines such as bioinformatics (biology), cheminformatics (chemistry), health informatics (health sciences) and more recently materials informatics (material science). The term “materials informatics” was first coined by J. R. Rodgers in 2003 in an abstract presented at the 226th ACS meeting1 and was quickly repeated in a 2004 paper by Q. Song in the Chinese Science Bulletin.2 In 2005 Krishna Rajan published his seminal work on materials informatics in Materials Today.3 Since then the field has evolved rapidly as evident by the constant growth in the number of publications that explicitly used the term “materials informatics” (Figure 1). Concomitant with this growth in volume came the growth in the diversity of topics and applications covered by and benefiting from materials informatics. Examples include household products, aviation products, cosmetics, catalysts, anticorrosives, nanomaterials, optical devices, solar cells, explosives, and many more. Moreover, within each field, multiple endpoints were modeled. The scientific maturity of materials informatics is evident by several recently published special issues, review articles, and monographs.4−10 Looking through the literature, we have also identified publications discussing the development of models (e.g., quantitative structure−property relationship (QSPR) models) for predicting various materials properties yet without using the term materials informatics specifically. Thus, we surmise that the number of materials informatics related paper is actually greater than what is suggested by Figure 1. By analogy with Jourdain’s statement in Moliere’s The Bourgeois Gentleman, “Well, what do you know about that! These forty years now, I’ve been speaking in prose without knowing it!”12 we may assert that some scientists may not realize that they are, in fact, practicing materials informatics. This observation highlights the importance of using standardized terminology for describing a particular concept such as materials informatics. We posit that the growth of the specialized literature on this topic suggests that the underlying term should be used widely and universally by all researchers employing data modeling techniques in the study of materials. Several factors have contributed to the development of materials informatics. First is the rapid growth of both publicly available and proprietary databases that include empirical information on structures and properties of materials obtained either experimentally (e.g., CrystMet (www.tothcanada.com), ICSD (www.fiz-karlsruhe.com/icsd.html), Pearson’s Crystal Data (www.crystalimpact.com/pcd/), MatWeb (www.matweb. com), MatBase (www.matbase.com), MatNavi (mits.nims.go. jp)) or computationally (e.g., AflowLib (http://www.aflowlib. org/)). The development of these databases was largely fueled by nationwide programs such as the Materials Genomics © XXXX American Chemical Society
A
DOI: 10.1021/acs.jcim.8b00016 J. Chem. Inf. Model. XXXX, XXX, XXX−XXX
Journal of Chemical Information and Modeling
Editorial
Figure 1. Growth of publications related to the topic of materials informatics in the past 15 years. Data is obtained by querying the core collection of the Web of Science with the term “materials informatics”.11 (5) Agrawal, A.; Choudhary, A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater. 2016, 4, 053208. (6) Jain, A.; Hautier, G.; Ong, S. P.; Persson, K. New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships. J. Mater. Res. 2016, 31, 977−994. (7) Le, T.; Epa, V. C.; Burden, F. R.; Winkler, D. A. Quantitative structure-property relationship modeling of diverse materials properties. Chem. Rev. 2012, 112, 2889−919. (8) Winkler, D. A. Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. Toxicol. Appl. Pharmacol. 2016, 299, 96−100. (9) Puzyn, T.; Leszczynska, D.; Leszczynski, J. Toward the development of ″nano-QSARs″: advances and challenges. Small 2009, 5, 2494−509. (10) Informatics for Materials Science and Engineering; Rajan, K., Ed.; Elsevier Inc., 2013. (11) http://apps.webofknowledge.com (accessed Nov 29, 2017). (12) Molière. The Bourgeois Gentleman in a new verse adaptation; adaptation by Mooney, T. http://moliere-in-english.com/bourgeois. html#adaptation (accessed Jan 2018).
mining and machine learning techniques in material informatics, (7) new frontiers in materials informatics and materials informatics-driven design of new experimental systems with desired properties. We expect that this special issue will serve the purpose of building and reinforcing the bridge between materials informatics and other information-based disciplines as well as between all practitioners, computational scientists, and experimentalists, of the field. We therefore invite all interested researchers to contribute to this unique endeavor. We ask interested scientists to submit manuscripts for ths special issue by June 15, 2018.
Hanoch Senderowitz*,† Alexander Tropsha*,‡ †
■
Department of Chemistry, Bar Ilan University, Ramat-Gan 5290002, Israel ‡ Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected] (H.S.). *E-mail:
[email protected] (A.T.). ORCID
Hanoch Senderowitz: 0000-0003-0076-1355 Alexander Tropsha: 0000-0003-3802-8896 Notes
Views expressed in this editorial are those of the authors and not necessarily the views of the ACS.
■
REFERENCES
(1) Rodgers, J. Materials informatics: Knowledge acquisition for materials design. Abst. Pap. Am. Chem. Soc. 2003, 226, U302−U303. (2) Song, Q. A preliminary investigation on materials informatics. Chin. Sci. Bull. 2004, 49, 210−214. (3) Rajan, K. Materials Informatics. Mater. Today 2005, 8, 38−45. (4) Rajan, K. Materials Informatics: The Materials “Gene” and Big Data. Annu. Rev. Mater. Res. 2015, 45, 153−169. B
DOI: 10.1021/acs.jcim.8b00016 J. Chem. Inf. Model. XXXX, XXX, XXX−XXX
