Review of computational approaches to predict the thermodynamic stability of inorganic solids

Bartel, Christopher J.

doi:10.1007/s10853-022-06915-4

Review of computational approaches to predict the thermodynamic stability of inorganic solids

Journal Article · Mon Jan 31 23:00:00 EST 2022 · Journal of Materials Science

DOI:https://doi.org/10.1007/s10853-022-06915-4· OSTI ID:1865532

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Univ. of California, Berkeley, CA (United States); Stony Brook University

Improvements in the efficiency and availability of quantum chemistry codes, supercomputing centers, and open materials databases have transformed the accessibility of computational materials design approaches. Thermodynamic stability predictions play a central role in the efficacy of these approaches and should be considered carefully. This review covers the fundamentals of calculating thermodynamic stability using first-principles methods. Stability is delineated into two main topics — stability with respect to decomposition into competing phases and stability with respect to phase transition into alternative structures at fixed composition. For each topic, a summary of the state-of-the-art is provided along with a tutorial overview of practical considerations. The application of machine learning to both kinds of stability predictions is also covered. Finally, the limitations of thermodynamic stability predictions are discussed within the context of predicting the synthesizability of materials.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of California, Berkeley, CA (United States); Energy Frontier Research Centers (EFRC) (United States). A Next Generation Synthesis Center (GENESIS)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0019212

OSTI ID:: 1865532

Journal Information:: Journal of Materials Science, Journal Name: Journal of Materials Science; ISSN 0022-2461

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

References (166)

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor Advanced Materials, Vol. 31, Issue 46 https://doi.org/10.1002/adma.201902765	journal	September 2019
Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis Miura, Akira; Bartel, Christopher J.; Goto, Yosuke Advanced Materials https://doi.org/10.1002/adma.202100312	journal	May 2021
Ab Initio Thermochemistry of Solid-State Materials Stoffel, Ralf Peter; Wessel, Claudia; Lumey, Marck-Willem Angewandte Chemie International Edition, Vol. 49, Issue 31 https://doi.org/10.1002/anie.200906780	journal	June 2010
An Unusual Phase Transition Driven by Vibrational Entropy Changes in a Hybrid Organic-Inorganic Perovskite Wei, Wenjuan; Li, Wei; Butler, Keith T. Angewandte Chemie International Edition, Vol. 57, Issue 29 https://doi.org/10.1002/anie.201803176	journal	June 2018
The Limited Predictive Power of the Pauling Rules George, Janine; Waroquiers, David; Di Stefano, Davide Angewandte Chemie International Edition, Vol. 59, Issue 19 https://doi.org/10.1002/anie.202000829	journal	March 2020
High‐throughput computational design of halide perovskites and beyond for optoelectronics Li, Yuheng; Yang, Kesong WIREs Computational Molecular Science, Vol. 11, Issue 3 https://doi.org/10.1002/wcms.1500	journal	August 2020
Electrochemical Equilibria Pourbaix, Marcel; Staehle, Roger W.; Pourbaix, Marcel Lectures on Electrochemical Corrosion, p 83-183 https://doi.org/10.1007/978-1-4684-1806-4_4	book	January 1973
A chemical scale for crystal-structure maps Pettifor, D. G. Solid State Communications, Vol. 51, Issue 1 https://doi.org/10.1016/0038-1098(84)90765-8	journal	July 1984
The alloy theoretic automated toolkit: A user guide van de Walle, A.; Asta, M.; Ceder, G. Calphad, Vol. 26, Issue 4 https://doi.org/10.1016/S0364-5916(02)80006-2	journal	December 2002
The compound energy formalism Hillert, Mats Journal of Alloys and Compounds, Vol. 320, Issue 2 https://doi.org/10.1016/S0925-8388(00)01481-X	journal	May 2001
Computational thermodynamics and its applications Liu, Zi-Kui Acta Materialia, Vol. 200 https://doi.org/10.1016/j.actamat.2020.08.008	journal	November 2020
Thermodynamics of high entropy oxides McCormack, Scott J.; Navrotsky, Alexandra Acta Materialia, Vol. 202 https://doi.org/10.1016/j.actamat.2020.10.043	journal	January 2021
Metric-driven search for structurally stable inorganic compounds Villarreal, R.; Singh, P.; Arroyave, R. Acta Materialia, Vol. 202 https://doi.org/10.1016/j.actamat.2020.10.055	journal	January 2021
FactSage thermochemical software and databases, 2010–2016 Bale, C. W.; Bélisle, E.; Chartrand, P. Calphad, Vol. 54 https://doi.org/10.1016/j.calphad.2016.05.002	journal	September 2016
A high-throughput infrastructure for density functional theory calculations Jain, Anubhav; Hautier, Geoffroy; Moore, Charles J. Computational Materials Science, Vol. 50, Issue 8 https://doi.org/10.1016/j.commatsci.2011.02.023	journal	June 2011
AFLOW: An automatic framework for high-throughput materials discovery Curtarolo, Stefano; Setyawan, Wahyu; Hart, Gus L. W. Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.005	journal	June 2012
AiiDA: automated interactive infrastructure and database for computational science Pizzi, Giovanni; Cepellotti, Andrea; Sabatini, Riccardo Computational Materials Science, Vol. 111 https://doi.org/10.1016/j.commatsci.2015.09.013	journal	January 2016
A computational framework for automation of point defect calculations Goyal, Anuj; Gorai, Prashun; Peng, Haowei Computational Materials Science, Vol. 130 https://doi.org/10.1016/j.commatsci.2016.12.040	journal	April 2017
USPEX—Evolutionary crystal structure prediction Glass, Colin W.; Oganov, Artem R.; Hansen, Nikolaus Computer Physics Communications, Vol. 175, Issue 11-12 https://doi.org/10.1016/j.cpc.2006.07.020	journal	December 2006
Computational Screening of Cathode Coatings for Solid-State Batteries Xiao, Yihan; Miara, Lincoln J.; Wang, Yan Joule, Vol. 3, Issue 5 https://doi.org/10.1016/j.joule.2019.02.006	journal	May 2019
Inverse Design of Solid-State Materials via a Continuous Representation Noh, Juhwan; Kim, Jaehoon; Stein, Helge S. Matter, Vol. 1, Issue 5 https://doi.org/10.1016/j.matt.2019.08.017	journal	November 2019
Accelerating materials discovery with Bayesian optimization and graph deep learning Zuo, Yunxing; Qin, Mingde; Chen, Chi Materials Today, Vol. 51 https://doi.org/10.1016/j.mattod.2021.08.012	journal	December 2021
Vibrational thermodynamics of materials Fultz, Brent Progress in Materials Science, Vol. 55, Issue 4 https://doi.org/10.1016/j.pmatsci.2009.05.002	journal	May 2010
First principles phonon calculations in materials science Togo, Atsushi; Tanaka, Isao Scripta Materialia, Vol. 108 https://doi.org/10.1016/j.scriptamat.2015.07.021	journal	November 2015
Progress toward Solid State Synthesis by Design Chamorro, Juan R.; McQueen, Tyrel M. Accounts of Chemical Research, Vol. 51, Issue 11 https://doi.org/10.1021/acs.accounts.8b00382	journal	October 2018
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices Wang, Anthony Yu-Tung; Murdock, Ryan J.; Kauwe, Steven K. Chemistry of Materials, Vol. 32, Issue 12 https://doi.org/10.1021/acs.chemmater.0c01907	journal	May 2020
Phase Behavior in Rhombohedral NaSiCON Electrolytes and Electrodes Deng, Zeyu; Sai Gautam, Gopalakrishnan; Kolli, Sanjeev Krishna Chemistry of Materials, Vol. 32, Issue 18 https://doi.org/10.1021/acs.chemmater.0c02695	journal	August 2020
Nanoparticle Size Effects on Phase Stability for Molybdenum and Tungsten Carbides Shrestha, Anukriti; Gao, Xutao; Hicks, Jason C. Chemistry of Materials, Vol. 33, Issue 12 https://doi.org/10.1021/acs.chemmater.1c01120	journal	June 2021
Bulk Synthesis, Structure, and Electronic Properties of Magnesium Zirconium Nitride Solid Solutions Rom, Christopher L.; Fallon, M. Jewels; Wustrow, Allison Chemistry of Materials, Vol. 33, Issue 13 https://doi.org/10.1021/acs.chemmater.1c01450	journal	June 2021
Predictive Synthesis Kovnir, Kirill Chemistry of Materials, Vol. 33, Issue 13 https://doi.org/10.1021/acs.chemmater.1c01484	journal	July 2021
Interface Stability in Solid-State Batteries Richards, William D.; Miara, Lincoln J.; Wang, Yan Chemistry of Materials, Vol. 28, Issue 1 https://doi.org/10.1021/acs.chemmater.5b04082	journal	December 2015
Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning Schmidt, Jonathan; Shi, Jingming; Borlido, Pedro Chemistry of Materials, Vol. 29, Issue 12 https://doi.org/10.1021/acs.chemmater.7b00156	journal	May 2017
Thermodynamic Routes to Novel Metastable Nitrogen-Rich Nitrides Sun, Wenhao; Holder, Aaron; Orvañanos, Bernardo Chemistry of Materials, Vol. 29, Issue 16 https://doi.org/10.1021/acs.chemmater.7b02399	journal	August 2017
Particle Morphology and Lithium Segregation to Surfaces of the Li ₇ La ₃ Zr ₂ O ₁₂ Solid Electrolyte Canepa, Pieremanuele; Dawson, James A.; Sai Gautam, Gopalakrishnan Chemistry of Materials, Vol. 30, Issue 9 https://doi.org/10.1021/acs.chemmater.8b00649	journal	April 2018
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals Chen, Chi; Ye, Weike; Zuo, Yunxing Chemistry of Materials, Vol. 31, Issue 9 https://doi.org/10.1021/acs.chemmater.9b01294	journal	April 2019
Defect-Accommodating Intermediates Yield Selective Low-Temperature Synthesis of YMnO ₃ Polymorphs Todd, Paul K.; Wustrow, Allison; McAuliffe, Rebecca D. Inorganic Chemistry, Vol. 59, Issue 18 https://doi.org/10.1021/acs.inorgchem.0c02023	journal	August 2020
Synthesis and Characterization of MgCr ₂ S ₄ Thiospinel as a Potential Magnesium Cathode Wustrow, Allison; Key, Baris; Phillips, Patrick J. Inorganic Chemistry, Vol. 57, Issue 14 https://doi.org/10.1021/acs.inorgchem.8b01417	journal	June 2018
Teaching the Concept of Gibbs Energy Minimization through Its Application to Phase-Equilibrium Calculation Privat, Romain; Jaubert, Jean-Noël; Berger, Etienne Journal of Chemical Education, Vol. 93, Issue 9 https://doi.org/10.1021/acs.jchemed.6b00205	journal	July 2016
Investigation of a Quantum Monte Carlo Protocol To Achieve High Accuracy and High-Throughput Materials Formation Energies Saritas, Kayahan; Mueller, Tim; Wagner, Lucas Journal of Chemical Theory and Computation, Vol. 13, Issue 5 https://doi.org/10.1021/acs.jctc.6b01179	journal	April 2017
Modeling the Phase-Change Memory Material, Ge ₂ Sb ₂ Te ₅ , with a Machine-Learned Interatomic Potential Mocanu, Felix C.; Konstantinou, Konstantinos; Lee, Tae Hoon The Journal of Physical Chemistry B, Vol. 122, Issue 38 https://doi.org/10.1021/acs.jpcb.8b06476	journal	September 2018
Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery Tong, Qunchao; Gao, Pengyue; Liu, Hanyu The Journal of Physical Chemistry Letters, Vol. 11, Issue 20 https://doi.org/10.1021/acs.jpclett.0c02357	journal	September 2020
Computational Approach for Epitaxial Polymorph Stabilization through Substrate Selection Ding, Hong; Dwaraknath, Shyam S.; Garten, Lauren ACS Applied Materials & Interfaces, Vol. 8, Issue 20 https://doi.org/10.1021/acsami.6b01630	journal	May 2016
High-Throughput Equilibrium Analysis of Active Materials for Solar Thermochemical Ammonia Synthesis Bartel, Christopher J.; Rumptz, John R.; Weimer, Alan W. ACS Applied Materials & Interfaces, Vol. 11, Issue 28 https://doi.org/10.1021/acsami.9b01242	journal	March 2019
Energy, Phonon, and Dynamic Stability Criteria of Two-Dimensional Materials Malyi, Oleksandr I.; Sopiha, Kostiantyn V.; Persson, Clas ACS Applied Materials & Interfaces, Vol. 11, Issue 28 https://doi.org/10.1021/acsami.9b01261	journal	April 2019
Generative Adversarial Networks for Crystal Structure Prediction Kim, Sungwon; Noh, Juhwan; Gu, Geun Ho ACS Central Science, Vol. 6, Issue 8 https://doi.org/10.1021/acscentsci.0c00426	journal	July 2020
Li−Fe−P−O ₂ Phase Diagram from First Principles Calculations Ong, Shyue Ping; Wang, Lei; Kang, Byoungwoo Chemistry of Materials, Vol. 20, Issue 5 https://doi.org/10.1021/cm702327g	journal	February 2008
The Principles Determining the Structure of Complex Ionic Crystals Pauling, Linus Journal of the American Chemical Society, Vol. 51, Issue 4 https://doi.org/10.1021/ja01379a006	journal	April 1929
Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning Jang, Jidon; Gu, Geun Ho; Noh, Juhwan Journal of the American Chemical Society, Vol. 142, Issue 44 https://doi.org/10.1021/jacs.0c07384	journal	October 2020
Machine Learning Guided Synthesis of Multinary Chevrel Phase Chalcogenides Singstock, Nicholas R.; Ortiz-Rodríguez, Jessica C.; Perryman, Joseph T. Journal of the American Chemical Society, Vol. 143, Issue 24 https://doi.org/10.1021/jacs.1c02971	journal	June 2021
In Situ Mechanistic Studies of Two Divergent Synthesis Routes Forming the Heteroanionic BiOCuSe McClain, Rebecca; Malliakas, Christos D.; Shen, Jiahong Journal of the American Chemical Society, Vol. 143, Issue 31 https://doi.org/10.1021/jacs.1c03947	journal	July 2021
Rational Solid-State Synthesis Routes for Inorganic Materials Aykol, Muratahan; Montoya, Joseph H.; Hummelshøj, Jens Journal of the American Chemical Society, Vol. 143, Issue 24 https://doi.org/10.1021/jacs.1c04888	journal	June 2021
Selectivity in Yttrium Manganese Oxide Synthesis via Local Chemical Potentials in Hyperdimensional Phase Space Todd, Paul K.; McDermott, Matthew J.; Rom, Christopher L. Journal of the American Chemical Society, Vol. 143, Issue 37 https://doi.org/10.1021/jacs.1c06229	journal	September 2021
Cu–In Halide Perovskite Solar Absorbers Zhao, Xin-Gang; Yang, Dongwen; Sun, Yuanhui Journal of the American Chemical Society, Vol. 139, Issue 19 https://doi.org/10.1021/jacs.7b02120	journal	May 2017
Crystals from first principles Maddox, John Nature, Vol. 335, Issue 6187 https://doi.org/10.1038/335201a0	journal	September 1988
Beware of plausible predictions of fantasy materials Zunger, Alex Nature, Vol. 566, Issue 7745 https://doi.org/10.1038/d41586-019-00676-y	journal	February 2019
Computational predictions of energy materials using density functional theory Jain, Anubhav; Shin, Yongwoo; Persson, Kristin A. Nature Reviews Materials, Vol. 1, Issue 1 https://doi.org/10.1038/natrevmats.2015.4	journal	January 2016
Computationally guided discovery of thermoelectric materials Gorai, Prashun; Stevanović, Vladan; Toberer, Eric S. Nature Reviews Materials, Vol. 2, Issue 9 https://doi.org/10.1038/natrevmats.2017.53	journal	August 2017
Machine-learning-assisted materials discovery using failed experiments Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F. Nature, Vol. 533, Issue 7601 https://doi.org/10.1038/nature17439	journal	May 2016
Towards the computational design of solid catalysts Nørskov, J.; Bligaard, T.; Rossmeisl, J. Nature Chemistry, Vol. 1, Issue 1, p. 37-46 https://doi.org/10.1038/nchem.121	journal	April 2009
Entropy-stabilized oxides Rost, Christina M.; Sachet, Edward; Borman, Trent Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms9485	journal	September 2015
Crystal structure prediction from first principles Woodley, Scott M.; Catlow, Richard Nature Materials, Vol. 7, Issue 12 https://doi.org/10.1038/nmat2321	journal	December 2008
Instilling defect tolerance in new compounds Walsh, Aron; Zunger, Alex Nature Materials, Vol. 16, Issue 10 https://doi.org/10.1038/nmat4973	journal	September 2017
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies Kirklin, Scott; Saal, James E.; Meredig, Bryce npj Computational Materials, Vol. 1, Issue 1 https://doi.org/10.1038/npjcompumats.2015.10	journal	December 2015
Computational understanding of Li-ion batteries Urban, Alexander; Seo, Dong-Hwa; Ceder, Gerbrand npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.2	journal	March 2016
A general-purpose machine learning framework for predicting properties of inorganic materials Ward, Logan; Agrawal, Ankit; Choudhary, Alok npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.28	journal	August 2016
Understanding crystallization pathways leading to manganese oxide polymorph formation Chen, Bor-Rong; Sun, Wenhao; Kitchaev, Daniil A. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-04917-y	journal	June 2018
Deep neural networks for accurate predictions of crystal stability Ye, Weike; Chen, Chi; Wang, Zhenbin Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-06322-x	journal	September 2018
Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry Bartel, Christopher J.; Millican, Samantha L.; Deml, Ann M. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-06682-4	journal	October 2018
Non-equilibrium crystallization pathways of manganese oxides in aqueous solution Sun, Wenhao; Kitchaev, Daniil A.; Kramer, Denis Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-08494-6	journal	February 2019
Predicting materials properties without crystal structure: deep representation learning from stoichiometry Goodall, Rhys E. A.; Lee, Alpha A. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19964-7	journal	December 2020
A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis McDermott, Matthew J.; Dwaraknath, Shyam S.; Persson, Kristin A. Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-23339-x	journal	May 2021
Efficient first-principles prediction of solid stability: Towards chemical accuracy Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0065-z	journal	March 2018
Entropy contributions to phase stability in binary random solid solutions Manzoor, Anus; Pandey, Shubham; Chakraborty, Debajit npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0102-y	journal	August 2018
The role of decomposition reactions in assessing first-principles predictions of solid stability Bartel, Christopher J.; Weimer, Alan W.; Lany, Stephan npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-018-0143-2	journal	January 2019
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
A critical examination of compound stability predictions from machine-learned formation energies Bartel, Christopher J.; Trewartha, Amalie; Wang, Qi npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00362-y	journal	July 2020
Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm Dunn, Alexander; Wang, Qi; Ganose, Alex npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00406-3	journal	September 2020
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design Choudhary, Kamal; Garrity, Kevin F.; Reid, Andrew C. E. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00440-1	journal	November 2020
Compositionally restricted attention-based network for materials property predictions Wang, Anthony Yu-Tung; Kauwe, Steven K.; Murdock, Ryan J. npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00545-1	journal	May 2021
The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides Bianchini, Matteo; Wang, Jingyang; Clément, Raphaële J. Nature Materials, Vol. 19, Issue 10 https://doi.org/10.1038/s41563-020-0688-6	journal	May 2020
Computational materials design Shevlin, Stephen; Castro, Bruno; Li, Xin Nature Materials, Vol. 20, Issue 6 https://doi.org/10.1038/s41563-021-01038-8	journal	May 2021
Inverse design in search of materials with target functionalities Zunger, Alex Nature Reviews Chemistry, Vol. 2, Issue 4 https://doi.org/10.1038/s41570-018-0121	journal	March 2018
Accelerating the discovery of materials for clean energy in the era of smart automation Tabor, Daniel P.; Roch, Loïc M.; Saikin, Semion K. Nature Reviews Materials, Vol. 3, Issue 5 https://doi.org/10.1038/s41578-018-0005-z	journal	April 2018
Structure prediction drives materials discovery Oganov, Artem R.; Pickard, Chris J.; Zhu, Qiang Nature Reviews Materials, Vol. 4, Issue 5 https://doi.org/10.1038/s41578-019-0101-8	journal	April 2019
Understanding interface stability in solid-state batteries Xiao, Yihan; Wang, Yan; Bo, Shou-Hang Nature Reviews Materials, Vol. 5, Issue 2 https://doi.org/10.1038/s41578-019-0157-5	journal	December 2019
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
A complete catalogue of high-quality topological materials Vergniory, M. G.; Elcoro, L.; Felser, Claudia Nature, Vol. 566, Issue 7745 https://doi.org/10.1038/s41586-019-0954-4	journal	February 2019
Origins of structural and electronic transitions in disordered silicon Deringer, Volker L.; Bernstein, Noam; Csányi, Gábor Nature, Vol. 589, Issue 7840 https://doi.org/10.1038/s41586-020-03072-z	journal	January 2021
Text-mined dataset of inorganic materials synthesis recipes Kononova, Olga; Huo, Haoyan; He, Tanjin Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0224-1	journal	October 2019
Materials Cloud, a platform for open computational science Talirz, Leopold; Kumbhar, Snehal; Passaro, Elsa Scientific Data, Vol. 7, Issue 1 https://doi.org/10.1038/s41597-020-00637-5	journal	September 2020
OPTIMADE, an API for exchanging materials data Andersen, Casper W.; Armiento, Rickard; Blokhin, Evgeny Scientific Data, Vol. 8, Issue 1 https://doi.org/10.1038/s41597-021-00974-z	journal	August 2021
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition Jha, Dipendra; Ward, Logan; Paul, Arindam Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-35934-y	journal	December 2018
A framework for quantifying uncertainty in DFT energy corrections Wang, Amanda; Kingsbury, Ryan; McDermott, Matthew Scientific Reports, Vol. 11, Issue 1 https://doi.org/10.1038/s41598-021-94550-5	journal	July 2021
Promises and perils of computational materials databases Horton, M. K.; Dwaraknath, S.; Persson, K. A. Nature Computational Science, Vol. 1, Issue 1 https://doi.org/10.1038/s43588-020-00016-5	journal	January 2021
High-throughput density-functional perturbation theory phonons for inorganic materials Petretto, Guido; Dwaraknath, Shyam; P. C. Miranda, Henrique Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.65	journal	May 2018
Design of Li _1+2x Zn _1−x PS ₄ , a new lithium ion conductor Richards, William D.; Wang, Yan; Miara, Lincoln J. Energy & Environmental Science, Vol. 9, Issue 10 https://doi.org/10.1039/C6EE02094A	journal	January 2016
Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface Tong, Qunchao; Xue, Lantian; Lv, Jian Faraday Discussions, Vol. 211 https://doi.org/10.1039/C8FD00055G	journal	January 2018
Crystal structure prediction: reflections on present status and challenges Oganov, Artem R. Faraday Discussions, Vol. 211 https://doi.org/10.1039/C8FD90033G	journal	January 2018
Relative stability of FeS ₂ polymorphs with the random phase approximation approach Zhang, Min-Ye; Cui, Zhi-Hao; Jiang, Hong Journal of Materials Chemistry A, Vol. 6, Issue 15 https://doi.org/10.1039/C8TA00759D	journal	January 2018
Selective metathesis synthesis of MgCr ₂ S ₄ by control of thermodynamic driving forces Miura, Akira; Ito, Hiroaki; Bartel, Christopher J. Materials Horizons, Vol. 7, Issue 5 https://doi.org/10.1039/C9MH01999E	journal	January 2020
Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity Zimmermann, Nils E. R.; Jain, Anubhav RSC Advances, Vol. 10, Issue 10 https://doi.org/10.1039/C9RA07755C	journal	January 2020
Polymorph exploration of bismuth stannate using first-principles phonon mode mapping Rahim, Warda; Skelton, Jonathan M.; Savory, Christopher N. Chemical Science, Vol. 11, Issue 30 https://doi.org/10.1039/D0SC02995E	journal	January 2020
Visualizing defect energetics Anand, Shashwat; Male, James P.; Wolverton, Chris Materials Horizons, Vol. 8, Issue 7 https://doi.org/10.1039/D1MH00397F	journal	January 2021
Crystal structure and chemical constitution Goldschmidt, V. M. Transactions of the Faraday Society, Vol. 25 https://doi.org/10.1039/TF9292500253	journal	January 1929
Jacob’s ladder of density functional approximations for the exchange-correlation energy Perdew, John P. Density functional theory and its application to materials, AIP Conference Proceedings https://doi.org/10.1063/1.1390175	conference	January 2001
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Perspective: Crystal structure prediction at high pressures Wang, Yanchao; Ma, Yanming The Journal of Chemical Physics, Vol. 140, Issue 4 https://doi.org/10.1063/1.4861966	journal	January 2014
Perspective: Fifty years of density-functional theory in chemical physics Becke, Axel D. The Journal of Chemical Physics, Vol. 140, Issue 18 https://doi.org/10.1063/1.4869598	journal	May 2014
Influence of the exchange-correlation functional on the quasi-harmonic lattice dynamics of II-VI semiconductors Skelton, Jonathan M.; Tiana, Davide; Parker, Stephen C. The Journal of Chemical Physics, Vol. 143, Issue 6 https://doi.org/10.1063/1.4928058	journal	August 2015
Perspective: Machine learning potentials for atomistic simulations Behler, Jörg The Journal of Chemical Physics, Vol. 145, Issue 17 https://doi.org/10.1063/1.4966192	journal	November 2016
SchNet – A deep learning architecture for molecules and materials Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5019779	journal	June 2018
In situ studies of a platform for metastable inorganic crystal growth and materials discovery Shoemaker, D. P.; Hu, Y. -J.; Chung, D. Y. Proceedings of the National Academy of Sciences, Vol. 111, Issue 30 https://doi.org/10.1073/pnas.1406211111	journal	July 2014
Combined computational and experimental investigation of the La ₂ CuO _{4– x} S _x (0 ≤ x ≤ 4) quaternary system He, Hua; Yee, Chuck-Hou; McNally, Daniel E. Proceedings of the National Academy of Sciences, Vol. 115, Issue 31 https://doi.org/10.1073/pnas.1800284115	journal	July 2018
The Inorganic Crystal Structure Database (ICSD)—Present and Future Hellenbrandt, Mariette Crystallography Reviews, Vol. 10, Issue 1 https://doi.org/10.1080/08893110410001664882	journal	January 2004
The structures of binary compounds. I. Phenomenological structure maps Pettifor, D. G. Journal of Physics C: Solid State Physics, Vol. 19, Issue 3 https://doi.org/10.1088/0022-3719/19/3/002	journal	January 1986
Ab initio random structure searching Pickard, Chris J.; Needs, R. J. Journal of Physics: Condensed Matter, Vol. 23, Issue 5 https://doi.org/10.1088/0953-8984/23/5/053201	journal	January 2011
The 2019 materials by design roadmap Alberi, Kirstin; Nardelli, Marco Buongiorno; Zakutayev, Andriy Journal of Physics D: Applied Physics, Vol. 52, Issue 1 https://doi.org/10.1088/1361-6463/aad926	journal	October 2018
Phase stability of TiO ₂ polymorphs from diffusion Quantum Monte Carlo Luo, Ye; Benali, Anouar; Shulenburger, Luke New Journal of Physics, Vol. 18, Issue 11 https://doi.org/10.1088/1367-2630/18/11/113049	journal	November 2016
Learning physical descriptors for materials science by compressed sensing Ghiringhelli, Luca M.; Vybiral, Jan; Ahmetcik, Emre New Journal of Physics, Vol. 19, Issue 2 https://doi.org/10.1088/1367-2630/aa57bf	journal	February 2017
Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO Ouyang, Runhai; Ahmetcik, Emre; Carbogno, Christian Journal of Physics: Materials, Vol. 2, Issue 2 https://doi.org/10.1088/2515-7639/ab077b	journal	March 2019
From DFT to machine learning: recent approaches to materials science–a review Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera Journal of Physics: Materials, Vol. 2, Issue 3 https://doi.org/10.1088/2515-7639/ab084b	journal	May 2019
Materials discovery through machine learning formation energy Peterson, Gordon G. C.; Brgoch, Jakoah Journal of Physics: Energy, Vol. 3, Issue 2 https://doi.org/10.1088/2515-7655/abe425	journal	March 2021
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations Miksch, April M.; Morawietz, Tobias; Kästner, Johannes Machine Learning: Science and Technology, Vol. 2, Issue 3 https://doi.org/10.1088/2632-2153/abfd96	journal	July 2021
Training machine-learning potentials for crystal structure prediction using disordered structures Hong, Changho; Choi, Jeong Min; Jeong, Wonseok Physical Review B, Vol. 102, Issue 22 https://doi.org/10.1103/PhysRevB.102.224104	journal	December 2020
Ab initio calculation of phonon dispersions in semiconductors Giannozzi, Paolo; de Gironcoli, Stefano; Pavone, Pasquale Physical Review B, Vol. 43, Issue 9 https://doi.org/10.1103/PhysRevB.43.7231	journal	March 1991
Dynamical matrices, Born effective charges, dielectric permittivity tensors, and interatomic force constants from density-functional perturbation theory Gonze, Xavier; Lee, Changyol Physical Review B, Vol. 55, Issue 16 https://doi.org/10.1103/PhysRevB.55.10355	journal	April 1997
Cu-Au, Ag-Au, Cu-Ag, and Ni-Au intermetallics: First-principles study of temperature-composition phase diagrams and structures Ozoliņš, V.; Wolverton, C.; Zunger, Alex Physical Review B, Vol. 57, Issue 11 https://doi.org/10.1103/PhysRevB.57.6427	journal	March 1998
First-principles calculations of defects in oxygen-deficient silica exposed to hydrogen Blöchl, Peter E. Physical Review B, Vol. 62, Issue 10 https://doi.org/10.1103/PhysRevB.62.6158	journal	September 2000
Composition, structure, and stability of RuO 2 ( 110 ) as a function of oxygen pressure Reuter, Karsten; Scheffler, Matthias Physical Review B, Vol. 65, Issue 3 https://doi.org/10.1103/PhysRevB.65.035406	journal	December 2001
First-principles calculations of lithium ordering and phase stability on Li x NiO 2 Arroyo y. de Dompablo, M. E.; Van der Ven, A.; Ceder, G. Physical Review B, Vol. 66, Issue 6 https://doi.org/10.1103/PhysRevB.66.064112	journal	August 2002
Oxidation energies of transition metal oxides within the GGA + U framework Wang, Lei; Maxisch, Thomas; Ceder, Gerbrand Physical Review B, Vol. 73, Issue 19 https://doi.org/10.1103/PhysRevB.73.195107	journal	May 2006
Semiconductor thermochemistry in density functional calculations Lany, Stephan Physical Review B, Vol. 78, Issue 24 https://doi.org/10.1103/PhysRevB.78.245207	journal	December 2008
Ab initio up to the melting point: Anharmonicity and vacancies in aluminum Grabowski, B.; Ismer, L.; Hickel, T. Physical Review B, Vol. 79, Issue 13 https://doi.org/10.1103/PhysRevB.79.134106	journal	April 2009
Formation enthalpies by mixing GGA and GGA + U calculations Jain, Anubhav; Hautier, Geoffroy; Ong, Shyue Ping Physical Review B, Vol. 84, Issue 4 https://doi.org/10.1103/PhysRevB.84.045115	journal	July 2011
Lattice dynamics of anharmonic solids from first principles Hellman, O.; Abrikosov, I. A.; Simak, S. I. Physical Review B, Vol. 84, Issue 18 https://doi.org/10.1103/PhysRevB.84.180301	journal	November 2011
Temperature-driven phase transitions from first principles including all relevant excitations: The fcc-to-bcc transition in Ca Grabowski, B.; Söderlind, P.; Hickel, T. Physical Review B, Vol. 84, Issue 21 https://doi.org/10.1103/PhysRevB.84.214107	journal	December 2011
Correcting density functional theory for accurate predictions of compound enthalpies of formation: Fitted elemental-phase reference energies Stevanović, Vladan; Lany, Stephan; Zhang, Xiuwen Physical Review B, Vol. 85, Issue 11 https://doi.org/10.1103/PhysRevB.85.115104	journal	March 2012
Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability Hautier, Geoffroy; Ong, Shyue Ping; Jain, Anubhav Physical Review B, Vol. 85, Issue 15 https://doi.org/10.1103/PhysRevB.85.155208	journal	April 2012
Prediction of solid-aqueous equilibria: Scheme to combine first-principles calculations of solids with experimental aqueous states Persson, Kristin A.; Waldwick, Bryn; Lazic, Predrag Physical Review B, Vol. 85, Issue 23 https://doi.org/10.1103/PhysRevB.85.235438	journal	June 2012
Evolution of crystal structures in metallic elements Togo, Atsushi; Tanaka, Isao Physical Review B, Vol. 87, Issue 18 https://doi.org/10.1103/PhysRevB.87.184104	journal	May 2013
Combinatorial screening for new materials in unconstrained composition space with machine learning Meredig, B.; Agrawal, A.; Kirklin, S. Physical Review B, Vol. 89, Issue 9 https://doi.org/10.1103/PhysRevB.89.094104	journal	March 2014
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning Podryabinkin, Evgeny V.; Tikhonov, Evgeny V.; Shapeev, Alexander V. Physical Review B, Vol. 99, Issue 6 https://doi.org/10.1103/PhysRevB.99.064114	journal	February 2019
Big Data of Materials Science: Critical Role of the Descriptor Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V. Physical Review Letters, Vol. 114, Issue 10 https://doi.org/10.1103/PhysRevLett.114.105503	journal	March 2015
Strongly Constrained and Appropriately Normed Semilocal Density Functional Sun, Jianwei; Ruzsinszky, Adrienn; Perdew, John P. Physical Review Letters, Vol. 115, Issue 3 https://doi.org/10.1103/PhysRevLett.115.036402	journal	July 2015
Machine Learning Energies of 2 Million Elpasolite ( A B C 2 D 6 ) Crystals Faber, Felix A.; Lindmaa, Alexander; von Lilienfeld, O. Anatole Physical Review Letters, Vol. 117, Issue 13 https://doi.org/10.1103/PhysRevLett.117.135502	journal	September 2016
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties Xie, Tian; Grossman, Jeffrey C. Physical Review Letters, Vol. 120, Issue 14 https://doi.org/10.1103/PhysRevLett.120.145301	journal	April 2018
Data-Driven Learning of Total and Local Energies in Elemental Boron Deringer, Volker L.; Pickard, Chris J.; Csányi, Gábor Physical Review Letters, Vol. 120, Issue 15 https://doi.org/10.1103/PhysRevLett.120.156001	journal	April 2018
Generalized Gradient Approximation Made Simple Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868 https://doi.org/10.1103/PhysRevLett.77.3865	journal	October 1996
First-Principles Determination of the Soft Mode in Cubic ZrO 2 Parlinski, K.; Li, Z. Q.; Kawazoe, Y. Physical Review Letters, Vol. 78, Issue 21 https://doi.org/10.1103/PhysRevLett.78.4063	journal	May 1997
Predicting kinetics of polymorphic transformations from structure mapping and coordination analysis Stevanović, Vladan; Trottier, Ryan; Musgrave, Charles Physical Review Materials, Vol. 2, Issue 3 https://doi.org/10.1103/PhysRevMaterials.2.033802	journal	March 2018
Predictions of new AB O 3 perovskite compounds by combining machine learning and density functional theory Balachandran, Prasanna V.; Emery, Antoine A.; Gubernatis, James E. Physical Review Materials, Vol. 2, Issue 4 https://doi.org/10.1103/PhysRevMaterials.2.043802	journal	April 2018
Performance of the strongly constrained and appropriately normed density functional for solid-state materials Isaacs, Eric B.; Wolverton, Chris Physical Review Materials, Vol. 2, Issue 6 https://doi.org/10.1103/PhysRevMaterials.2.063801	journal	June 2018
SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates Ouyang, Runhai; Curtarolo, Stefano; Ahmetcik, Emre Physical Review Materials, Vol. 2, Issue 8 https://doi.org/10.1103/PhysRevMaterials.2.083802	journal	August 2018
Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery Park, Cheol Woo; Wolverton, Chris Physical Review Materials, Vol. 4, Issue 6 https://doi.org/10.1103/PhysRevMaterials.4.063801	journal	June 2020
Exploring the High-Pressure Materials Genome Amsler, Maximilian; Hegde, Vinay I.; Jacobsen, Steven D. Physical Review X, Vol. 8, Issue 4 https://doi.org/10.1103/PhysRevX.8.041021	journal	November 2018
The effect of lattice vibrations on substitutional alloy thermodynamics van de Walle, A.; Ceder, G. Reviews of Modern Physics, Vol. 74, Issue 1 https://doi.org/10.1103/RevModPhys.74.11	journal	January 2002
The thermodynamic scale of inorganic crystalline metastability Sun, Wenhao; Dacek, Stephen T.; Ong, Shyue Ping Science Advances, Vol. 2, Issue 11 https://doi.org/10.1126/sciadv.1600225	journal	November 2016
Thermodynamic limit for synthesis of metastable inorganic materials Aykol, Muratahan; Dwaraknath, Shyam S.; Sun, Wenhao Science Advances, Vol. 4, Issue 4 https://doi.org/10.1126/sciadv.aaq0148	journal	April 2018
New tolerance factor to predict the stability of perovskite oxides and halides Bartel, Christopher J.; Sutton, Christopher; Goldsmith, Bryan R. Science Advances, Vol. 5, Issue 2 https://doi.org/10.1126/sciadv.aav0693	journal	February 2019
High-throughput search for magnetic and topological order in transition metal oxides Frey, Nathan C.; Horton, Matthew K.; Munro, Jason M. Science Advances, Vol. 6, Issue 50 https://doi.org/10.1126/sciadv.abd1076	journal	December 2020
Ab initio Force Constant Approach to Phonon Dispersion Relations of Diamond and Graphite Kresse, G.; Furthmüller, J.; Hafner, J. Europhysics Letters (EPL), Vol. 32, Issue 9 https://doi.org/10.1209/0295-5075/32/9/005	journal	December 1995
Generalized chemical potential diagram and its applications to chemical reactions at interfaces between dissimilar materials Yokokawa, H. Journal of Phase Equilibria, Vol. 20, Issue 3 https://doi.org/10.1361/105497199770335794	journal	May 1999
Symbolic regression in materials science Wang, Yiqun; Wagner, Nicholas; Rondinelli, James M. MRS Communications, Vol. 9, Issue 3 https://doi.org/10.1557/mrc.2019.85	journal	June 2019
NOMAD: The FAIR concept for big data-driven materials science Draxl, Claudia; Scheffler, Matthias MRS Bulletin, Vol. 43, Issue 9 https://doi.org/10.1557/mrs.2018.208	journal	September 2018
First-Principles Atomistic Thermodynamics and Configurational Entropy Sutton, Christopher; Levchenko, Sergey V. Frontiers in Chemistry, Vol. 8 https://doi.org/10.3389/fchem.2020.00757	journal	December 2020
High-throughput Density-Functional Perturbation Theory phonons for inorganic materials [Supplemental Data] Petretto, Guido; Dwaraknath, Shyam; Miranda, Henrique P. C. figshare-Supplementary information for journal article at DOI: 10.1038/sdata.2018.65, 3 files https://doi.org/10.6084/m9.figshare.c.3938023.v1	collection	April 2018

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