Editorial pubs.acs.org/cm
Computational Design of Functional Materials
R
storage, there has been much hope placed in such computational prediction. MoO3 is an interesting wide-band gap semiconductor that, when appropriately doped, can serve, for instance, as a transparent n-type contact in organic photovoltaic devices. Chabinyc, Van de Walle, and co-workers9 have studied the nature of a number of different dopant atoms in MoO3 to establish new doping strategies to optimize function. Two of the contributions in this special issue address magnetic materials, including a search by Miller, Fokwa, and co-workers10 for harder magnetic materials and an investigation by Pecharsky, Mudring, and co-workers11 for clues as to why some magnetic materials display large magnetocaloric entropy changes while others do not. Tehrani and Brgoch12 have contributed to the understanding of ultraincompressible, superhard materials and pointed to the need to avoid anion vacancies in the preparation in order to fully realize the promise of these materials. In a tour de force of modeling metal−organic framework (MOF) materials, Snurr, Sholl,13 and co-workers have employed DFT to optimize the structure of over 800 experimentally known MOF materials, finding that the reported structures are often different from the DFT-optimized ones, potentially as a result of residual (albeit poorly crystallographically-located) solvent molecules. They then employ grand-canonical Monte Carlo techniques to study the adsorption of CH4 and CO2 and track the difference that the inclusion of the correct, optimized crystal structure could make to the sorption capacity. InMnO3 in the YAlO3 structural aristotype is potentially a multiferroic material because of the concurrent presence of magnetic Mn3+ and the possibility for so-called geometric ferroelectricity arising from certain tilt patterns in the structural polyhedra. Selbach, Spaldin, and co-workers14 have studied the nature of defects and structural instabilities in this compound, to understand whether it would display coupled order parameters. The prospects for certain kinds of rotational degeneracies in the potential energy landscape of the material have also resulted in this publication being accompanied by a most interesting tableof-contents graphic. The manner in which organic semiconductors such as pentacene and derivatives thereof pack in the solid state can be hugely important to key properties such as the mobility. Anthony, Risko, and co-workers15 have compared structural polymorphs of pentacene derivatives to unravel the different driving forces for distinct packing modes. In an interesting but somewhat rare (for this journal) contribution, Sickafus, Uberuaga, and co-workers16 have studied the pyrochlore A2B2O7 structure type where A is a large, trivalent cation and B is quadrivalent Ti, Sn, Zr, or Hf. These materials are well-known for their use as thermal barrier coatings and in storing various radioactive waste species. In such applications, resistance toward amorphization (or metamiction, to use a term
ecent advances in theory, algorithms, and computational power, and innovations in the ability to scan and handle data usefully have made computers increasingly critical to the understanding, search, and discovery of new functional materials. Chemistry of Materials has for many years now clearly understood the critical role of computations to aid materials-by-design, even as larger initiatives have been advanced, such as the Obama White House-led Materials Genome Initiative and the European Materials Modeling Council. In this special issue of Chemistry of Materials on the subject of Computational Design of Functional Materials, we have invited a spectrum of researchers, ranging from early career scientists to established experts in the field, to showcase the use of computational techniques and address a variety of problems in materials chemistry. The nature of the materials studied covers the gamut from 2D transition metal dichalcogenides to oxides to hybrid framework materials and clathrate compounds. The material functions followed also span the range from semiconducting properties to mechanical properties to radiation stability, magnetic materials, and materials for energy storage applications. A particularly pleasing and somewhat unanticipated aspect of this special issue is that so many of the contributions combine experimental inputs in ways that have allowed for theory/experimental materials co-development. The era of theory versus experiment has clearly transitioned to an era of theory and experiment. It is also appropriate to mark the recent passing of Walter Kohn (1923−2016), whose seminal papers resulted in the development of Density Functional Theory (DFT).1,2 DFT now plays a critical role in the expanded use of ab initio techniques for the understanding and screening of functional materials. Many of the contributions in this special issue primarily rely on DFT techniques. However, beyond using established codes and techniques, a number of contributions also strive to advance the state-of-the art in computational methodologies for research on advanced materials. The set of invited articles opens with the work of Tkatchenko and co-workers,3 who address the challenging problem of accurately describing van der Waals interactions to study a number of carbon-based composite materials. A variety of techniques ranging from molecular mechanics and molecular dynamics to Frenkel exciton theory are employed by Shuai and co-workers,4 to understand the optical properties of organic nanoparticles. Prezhdo and co-workers5 exploit time-dependent techniques to understand excited-state properties of layered transition metal dichalcogenide materials. Three of the contributions address aspects of the important problem of electrochemical energy storage. Ceder and coworkers6 consider mechanisms of so-called “lithium-excess” materials. Leung7 has studied the dissolution of Mn2+ into the electrolyte from spinel surfaces, which is an important technological problem that often afflicts Mn-based electrode materials. Ong and co-workers8 propose new solid-state Li-ion conductors for Li-ion batteries. Since it is widely believed that high-performance solid-state electrolytes can lead to safer energy © 2017 American Chemical Society
Special Issue: Computational Design of Functional Materials Published: March 28, 2017 2399
DOI: 10.1021/acs.chemmater.7b00990 Chem. Mater. 2017, 29, 2399−2401
Chemistry of Materials
Editorial §
from geochemistry) is an important design parameter; the authors employ a combination of DFT techniques and machine learning to establish trends that would suggest which materials are more effective as long-term hosts of radioactive constituents. The next contributions represent aspects of understanding and designing more robust semiconductors. Galli and co-workers17 have demonstrated that hydrogen treatment (for example, by annealing in an atmosphere of hydrogen) can serve as a means of neutralizing detrimental trap states in lead chalcogenide semiconductors. Hautier and co-workers18 examined a computational database of 3787 oxide, 817 sulfide, 332 nitride, and 171 phosphide compounds to suggest zinc blende boron phosphide as a potential new transparent conducting material. Rondinelli and co-workers19 have examined mixed-valence compounds of Bi3+ and Bi5+ to propose new ferroelectrics that have strong visible light absorption as a consequence of the mixed-valence character. Scanlon and co-workers20 have examined the electronic structures of MCuP (where M is a divalent cation) structures for disperse valence bands, which would suggest a propensity to effective p-type conduction, in conjunction with a large enough band gap, which would imply that the materials can be effective transparent conductors. The DFT studies have been complemented with experimental verification. Singh, Ma, and coworkers21 have employed the now-well-known particle swarm techniques to search the space of SnnP2O5+n (n = 2, 3, 4, 5) structures and point to new transparent p-type semiconductors. Wager, Zunger, Keszler, and co-workers22 have proposed that dto-d optical transitions help greatly enhance the optical light absorption in the semiconductors, CuTaS3; in this work as well, DFT-based predictions are backed up with experimental verification. The final contribution in this section, from Loo, Clancy, and co-workers, 23 is an unusual computational examination of solvation of Pb2+ species in solution that can help understand the solution-based deposition of the photovoltaic halide perovskite CH3NH3PbI3 and related materials. The final three articles in this special issue are on the subject of thermoelectric materials. This is an area where modeling is famously challenging but that has also seen a lot of effort. Draxl and co-workers24 have examined some Si/Al clathrate materials for various aspects of their thermoelectric performance. Ozoliņs,̌ Wolverton, and co-workers25 have proposed a new thermoelectric material in the complex palladium oxide Bi2PdO4. Finally, Stevanović, Toberer, and co-workers26 have developed a simple proxy for estimating the anharmonic contribution to the lattice thermal conductivity of a range of compounds, in an approach that is likely to prove very useful in the search for new thermoelectric materials. Assembling this special issue has been exciting and rewarding, in no small part due to the alacrity with which authors accepted our invitations and submitted manuscripts in a timely manner. We hope that you will find this collection of work useful and inspiring.
■
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States ∥ Materials Department, Department of Chemistry and Biochemistry, and Materials Research Laboratory, University of California, Santa Barbara, California 93106, United States
AUTHOR INFORMATION
Corresponding Authors
*(J.-L.B.) E-mail:
[email protected]. *(K.P.) E-mail:
[email protected]. *(R.S.) E-mail:
[email protected]. ORCID
Jean-Luc Brédas: 0000-0001-7278-4471 Ram Seshadri: 0000-0001-5858-4027 Notes
Views expressed in this editorial are those of the authors and not necessarily the views of the ACS.
■
RELATED READINGS
(1) Hohenberg, P.; Kohn, W. Inhomogeneous Electron Gas. Phys. Rev. 1964, 136, B864−B871. (2) Kohn, W.; Sham, L. J. Self-Consistent Equations Including Exchange and Correlation Effects. Phys. Rev. 1965, 140, A1133−A1138. (3) Chattopadhyaya, M.; Hermann, J.; Poltavsky, I.; Tkatchenko, A. Intermolecular Interactions with Nanostructured Environments. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04190. (4) Li, W.; Peng, Q.; Ma, H.; Wen, J.; Ma, J.; Peteanu, L. A.; Shuai, Z. Theoretical Investigations on the Roles of Intramolecular Structure Distortion versus Irregular Intermolecular Packing in Optical Spectra of 6T Nanoparticles. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04210. (5) Li, L.; Long, R.; Prezhdo, O. V. Charge Separation and Recombination in Two-Dimensional MoS2/WS2: Time-Domain ab Initio Modeling. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b03727. (6) Twu, N.; Metzger, M.; Balasubramanian, M.; Marino, C.; Li, X.; Chen, H.; Gasteiger, H.; Ceder, G. Understanding the Origins of Higher Capacities at Faster Rates in Lithium-Excess LixNi2−4x/3Sbx/3O2. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04691. (7) Leung, K. First-Principles Modeling of Mn(II) Migration above and Dissolution from LixMn2O4 (001) Surfaces. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04429. (8) Zhu, Z.; Chu, I.-H.; Ong, S. P. Li3Y(PS4)2 and Li5PS4Cl2: New Lithium Superionic Conductors Predicted from Silver Thiophosphates using Efficiently Tiered Ab Initio Molecular Dynamics Simulations. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04049. (9) Peelaers, H.; Chabinyc, M. L.; Van de Walle, C. G. Controlling nType Doping in MoO3 . Chem. Mater. 2017, DOI: 10.1021/ acs.chemmater.6b04479. (10) Zhang, Y.; Miller, G. J.; Fokwa, B. P. T. Computational Design of Rare-Earth-Free Magnets with the Ti3Co5B2-Type Structure. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04114. (11) Bigun, I.; Steinberg, S.; Smetana, V.; Mudryk, Y.; Kalychak, Y.; Havela, L.; Pecharsky, V.; Mudring, A.-V. Magnetocaloric Behavior in Ternary Europium Indides EuT5In: Probing the Design Capability of First-Principles-Based Methods on the Multifaceted Magnetic Materials. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04782. (12) Tehrani, A. M.; Brgoch, J. Mechanical, Impact of Vacancies on the Mechanical Properties of Ultraincompressible Hard Rhenium Subnitrides: Re2N and Re3N. Chem. Mater. 2017, DOI: 10.1021/ acs.chemmater.6b04408. (13) Nazarian, D.; Camp, J. S.; Chung, Y. G.; Snurr, R. Q.; Sholl, D. S. Large-Scale Refinement of Metal-Organic Framework Structures Using Density Functional Theory. Chem. Mater. 2017, DOI: 10.1021/ acs.chemmater.6b04226.
Jean-Luc Brédas,*,† Associate Editor Kristin Persson,*,‡,§ Associate Editor Ram Seshadri,*,∥ Associate Editor †
Center for Organic Photonics and Electronics and School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States ‡ Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States 2400
DOI: 10.1021/acs.chemmater.7b00990 Chem. Mater. 2017, 29, 2399−2401
Chemistry of Materials
Editorial
(14) Griffin, S. M.; Reidulff, M.; Selbach, S. M.; Spaldin, N. A. Defect Chemistry as a Crystal Structure Design Parameter: Intrinsic Point Defects and Ga Substitution in InMnO3. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04207. (15) Thorley, K. J.; Finn, T. W.; Jarolimek, K.; Anthony, J. E.; Risko, C. Theory-Driven Insight into the Crystal Packing of Trialkylsilylethynyl Pentacenes. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04211. (16) Pilania, G.; Whittle, K. R.; Jiang, C.; Grimes, R. W.; Stanek, C. R.; Sickafus, K. E.; Uberuaga, B. P. Using Machine Learning to Identify Factors That Govern Amorphization of Irradiated Pyrochlores. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04666. (17) Vörös, M.; Brawand, N. P.; Galli, G. Hydrogen Treatment as a Detergent of Electronic Trap States in Lead Chalcogenide Nanoparticles. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04126. (18) Varley, J. B.; Miglio, A.; Ha, V.-A.; Setten, M. J.; Rignanese, G.-M.; Hautier, G. High-Throughput Design of Non-oxide p-Type Transparent Conducting Materials: Data Mining Search Strategy and Identification of Boron Phosphide. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04663. (19) He, J.; Franchini, C.; Rondinelli, J. M. Ferroelectric Oxides with Strong Visible-Light Absorption from Charge Ordering. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b03486. (20) Williamson, B. A. D.; Buckeridge, J.; Brown, J.; Ansbro, S.; Palgrave, R. G.; Scanlon, D. O. Engineering Valence Band Dispersion for High Mobility p-Type Semiconductors. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b03306. (21) Xu, Q.; Li, Y.; Zhang, L.; Zheng, W.; Singh, D. J.; Ma, Y. Sn(II)Containing Phosphates as Optoelectronic Materials. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b03669. (22) Heo, J.; Yu, L.; Altschul, E.; Waters, B. E.; Wager, J. F.; Zunger, A.; Keszler, D. A. Semiconductors, CuTaS3: Intermetal d-d Transitions Enable High Solar Absorption. Chem. Mater. 2017, DOI: 10.1021/ acs.chemmater.6b04730. (23) Stevenson, J.; Sorenson, B.; Subramaniam, V. H.; Raiford, J.; Khlyabich, P. P.; Loo, Y.-L.; Clancy, P. Mayer Bond Order as a Metric of Complexation Effectiveness in Lead Halide Perovskite Solutions. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04327. (24) Troppenz, M.; Rigamonti, S.; Draxl, C. Thermoelectrics, Predicting Ground-State Configurations and Electronic Properties of the Thermoelectric Clathrates Ba8AlxSi46−x and Sr8AlxSi46−x. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b05027. (25) He, J.; Hao, S.; Xia, Y.; Naghavi, S. S.; Ozoliņs,̌ V.; Wolverton, C. Thermoelectrics, Bi2PdO4: A Promising Thermoelectric Oxide with High Power Factor and Low Lattice Thermal Conductivity. Chem. Mater. 2017, DOI: 10.1021/acs.chemmater.6b04230. (26) Miller, S. A.; Gorai, P.; Ortiz, B. R.; Goyal, A.; Gao, D.; Barnett, S. A.; Mason, T. O.; Snyder, G. J.; Lv, Q.; Stevanović, V.; Toberer, E. S. Capturing Anharmonicity in a Lattice Thermal Conductivity Model for High-Throughput Predictions. Chem. Mater. 2017, DOI: 10.1021/ acs.chemmater.6b04179.
2401
DOI: 10.1021/acs.chemmater.7b00990 Chem. Mater. 2017, 29, 2399−2401
