Accelerating materials science with high-throughput computations and machine learning

Ong, Shyue Ping

doi:10.1016/j.commatsci.2019.01.013

Title: Accelerating materials science with high-throughput computations and machine learning

Journal Article · Fri Feb 01 00:00:00 EST 2019 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2019.01.013· OSTI ID:1528949

^[1]

University of California San Diego, La Jolla, CA (United States). Materials Virtual Laboratory

With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. In this report we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California San Diego, La Jolla, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); US Department of the Navy, Office of Naval Research (ONR)

Grant/Contract Number:: SC0012583; SC0012118; 1436976; N00014-15-1-0030; 1411192; N00014-16-1-2621; 1640899

OSTI ID:: 1528949

Alternate ID(s):: OSTI ID: 1529421

Journal Information:: Computational Materials Science, Vol. 161, Issue C; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 51 works

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References (90)

Self-Consistent Equations Including Exchange and Correlation Effects Kohn, W.; Sham, L. J. Physical Review, Vol. 140, Issue 4A, p. A1133-A1138 https://doi.org/10.1103/PhysRev.140.A1133	journal	November 1965
Inhomogeneous Electron Gas Hohenberg, P.; Kohn, W. Physical Review, Vol. 136, Issue 3B, p. B864-B871 https://doi.org/10.1103/PhysRev.136.B864	journal	November 1964
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav Computational Materials Science, Vol. 68 https://doi.org/10.1016/j.commatsci.2012.10.028	journal	February 2013
FireWorks: a dynamic workflow system designed for high-throughput applications: FireWorks: A Dynamic Workflow System Designed for High-Throughput Applications Jain, Anubhav; Ong, Shyue Ping; Chen, Wei Concurrency and Computation: Practice and Experience, Vol. 27, Issue 17 https://doi.org/10.1002/cpe.3505	journal	May 2015
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows Mathew, Kiran; Montoya, Joseph H.; Faghaninia, Alireza Computational Materials Science, Vol. 139 https://doi.org/10.1016/j.commatsci.2017.07.030	journal	November 2017
Phosphates as Lithium-Ion Battery Cathodes: An Evaluation Based on High-Throughput ab Initio Calculations Hautier, Geoffroy; Jain, Anubhav; Ong, Shyue Ping Chemistry of Materials, Vol. 23, Issue 15 https://doi.org/10.1021/cm200949v	journal	August 2011
Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations Hautier, Geoffroy; Jain, Anubhav; Chen, Hailong Journal of Materials Chemistry, Vol. 21, Issue 43 https://doi.org/10.1039/c1jm12216a	journal	January 2011
Phase stability, electrochemical stability and ionic conductivity of the Li _10±1 MP ₂ X ₁₂ (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family of superionic conductors Ong, Shyue Ping; Mo, Yifei; Richards, William Davidson Energy Environ. Sci., Vol. 6, Issue 1 https://doi.org/10.1039/C2EE23355J	journal	January 2013
Computational high-throughput screening of electrocatalytic materials for hydrogen evolution Greeley, Jeff; Jaramillo, Thomas F.; Bonde, Jacob Nature Materials, Vol. 5, Issue 11, p. 909-913 https://doi.org/10.1038/nmat1752	journal	October 2006
A search model for topological insulators with high-throughput robustness descriptors Yang, Kesong; Setyawan, Wahyu; Wang, Shidong Nature Materials, Vol. 11, Issue 7 https://doi.org/10.1038/nmat3332	journal	May 2012
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid Hachmann, Johannes; Olivares-Amaya, Roberto; Atahan-Evrenk, Sule The Journal of Physical Chemistry Letters, Vol. 2, Issue 17 https://doi.org/10.1021/jz200866s	journal	August 2011
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
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) Saal, James E.; Kirklin, Scott; Aykol, Muratahan JOM, Vol. 65, Issue 11 https://doi.org/10.1007/s11837-013-0755-4	journal	September 2013
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.002	journal	June 2012
Mastering the game of Go without human knowledge Silver, David; Schrittwieser, Julian; Simonyan, Karen Nature, Vol. 550, Issue 7676 https://doi.org/10.1038/nature24270	journal	October 2017
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images Christiansen, Eric M.; Yang, Samuel J.; Ando, D. Michael Cell, Vol. 173, Issue 3 https://doi.org/10.1016/j.cell.2018.03.040	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
Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness Nishita, Michiru; Park, Seung-Yeol; Nishio, Tadashi Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-016-0028-x	journal	January 2017
van der Waals Interactions in Layered Lithium Cobalt Oxides Aykol, Muratahan; Kim, Soo; Wolverton, C. The Journal of Physical Chemistry C, Vol. 119, Issue 33 https://doi.org/10.1021/acs.jpcc.5b06240	journal	August 2015
Elastic properties of bulk and low-dimensional materials using van der Waals density functional Choudhary, Kamal; Cheon, Gowoon; Reed, Evan Physical Review B, Vol. 98, Issue 1 https://doi.org/10.1103/PhysRevB.98.014107	journal	July 2018
Assessment of van der Waals inclusive density functional theory methods for layered electroactive materials Lozano, Ariel; Escribano, Bruno; Akhmatskaya, Elena Physical Chemistry Chemical Physics, Vol. 19, Issue 15 https://doi.org/10.1039/C7CP00284J	journal	January 2017
Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional Sun, Jianwei; Remsing, Richard C.; Zhang, Yubo Nature Chemistry, Vol. 8, Issue 9 https://doi.org/10.1038/nchem.2535	journal	June 2016
Surface energies of elemental crystals Tran, Richard; Xu, Zihan; Radhakrishnan, Balachandran Scientific Data, Vol. 3, Issue 1 https://doi.org/10.1038/sdata.2016.80	journal	September 2016
High-throughput computational X-ray absorption spectroscopy Mathew, Kiran; Zheng, Chen; Winston, Donald Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.151	journal	July 2018
Automated generation and ensemble-learned matching of X-ray absorption spectra Zheng, Chen; Mathew, Kiran; Chen, Chi npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0067-x	journal	March 2018
Elastic Properties of Alkali Superionic Conductor Electrolytes from First Principles Calculations Deng, Zhi; Wang, Zhenbin; Chu, Iek-Heng Journal of The Electrochemical Society, Vol. 163, Issue 2 https://doi.org/10.1149/2.0061602jes	journal	November 2015
Aqueous Stability of Alkali Superionic Conductors from First-Principles Calculations Radhakrishnan, Balachandran; Ong, Shyue Ping Frontiers in Energy Research, Vol. 4 https://doi.org/10.3389/fenrg.2016.00016	journal	April 2016
Progress and prospective of solid-state lithium batteries Takada, Kazunori Acta Materialia, Vol. 61, Issue 3, p. 759-770 https://doi.org/10.1016/j.actamat.2012.10.034	journal	February 2013
Inorganic solid Li ion conductors: An overview Knauth, Philippe Solid State Ionics, Vol. 180, Issue 14-16, p. 911-916 https://doi.org/10.1016/j.ssi.2009.03.022	journal	June 2009
A lithium superionic conductor Kamaya, Noriaki; Homma, Kenji; Yamakawa, Yuichiro Nature Materials, Vol. 10, Issue 9, p. 682-686 https://doi.org/10.1038/nmat3066	journal	July 2011
Superionic glass-ceramic electrolytes for room-temperature rechargeable sodium batteries Hayashi, Akitoshi; Noi, Kousuke; Sakuda, Atsushi Nature Communications, Vol. 3, Issue 1 https://doi.org/10.1038/ncomms1843	journal	January 2012
Design principles for solid-state lithium superionic conductors Wang, Yan; Richards, William Davidson; Ong, Shyue Ping Nature Materials, Vol. 14, Issue 10 https://doi.org/10.1038/nmat4369	journal	August 2015
Rational Composition Optimization of the Lithium-Rich Li ₃ OCl _{1– x} Br _x Anti-Perovskite Superionic Conductors Deng, Zhi; Radhakrishnan, Balachandran; Ong, Shyue Ping Chemistry of Materials, Vol. 27, Issue 10 https://doi.org/10.1021/acs.chemmater.5b00988	journal	May 2015
Role of Na ⁺ Interstitials and Dopants in Enhancing the Na ⁺ Conductivity of the Cubic Na ₃ PS ₄ Superionic Conductor Zhu, Zhuoying; Chu, Iek-Heng; Deng, Zhi Chemistry of Materials, Vol. 27, Issue 24 https://doi.org/10.1021/acs.chemmater.5b03656	journal	December 2015
Room-Temperature All-solid-state Rechargeable Sodium-ion Batteries with a Cl-doped Na3PS4 Superionic Conductor Chu, Iek-Heng; Kompella, Christopher S.; Nguyen, Han Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep33733	journal	September 2016
Li ₃ Y(PS ₄ ) ₂ and Li ₅ PS ₄ Cl ₂ : New Lithium Superionic Conductors Predicted from Silver Thiophosphates using Efficiently Tiered Ab Initio Molecular Dynamics Simulations Zhu, Zhuoying; Chu, Iek-Heng; Ong, Shyue Ping Chemistry of Materials, Vol. 29, Issue 6 https://doi.org/10.1021/acs.chemmater.6b04049	journal	November 2016
Insights into the Performance Limits of the Li ₇ P ₃ S ₁₁ Superionic Conductor: A Combined First-Principles and Experimental Study Chu, Iek-Heng; Nguyen, Han; Hy, Sunny ACS Applied Materials & Interfaces, Vol. 8, Issue 12 https://doi.org/10.1021/acsami.6b00833	journal	March 2016
Data-Driven First-Principles Methods for the Study and Design of Alkali Superionic Conductors Deng, Zhi; Zhu, Zhuoying; Chu, Iek-Heng Chemistry of Materials, Vol. 29, Issue 1 https://doi.org/10.1021/acs.chemmater.6b02648	journal	September 2016
Experimental and Computational Evaluation of a Sodium-Rich Anti-Perovskite for Solid State Electrolytes Nguyen, Han; Hy, Sunny; Wu, Erik Journal of The Electrochemical Society, Vol. 163, Issue 10 https://doi.org/10.1149/2.0091610jes	journal	January 2016
Divalent-doped Na3Zr2Si2PO12 natrium superionic conductor: Improving the ionic conductivity via simultaneously optimizing the phase and chemistry of the primary and secondary phases Samiee, Mojtaba; Radhakrishnan, Balachandran; Rice, Zane Journal of Power Sources, Vol. 347 https://doi.org/10.1016/j.jpowsour.2017.02.042	journal	April 2017
Probing Solid–Solid Interfacial Reactions in All-Solid-State Sodium-Ion Batteries with First-Principles Calculations Tang, Hanmei; Deng, Zhi; Lin, Zhuonan Chemistry of Materials, Vol. 30, Issue 1 https://doi.org/10.1021/acs.chemmater.7b04096	journal	December 2017
Superionic Conductivity in Lithium-Rich Anti-Perovskites Zhao, Yusheng; Daemen, Luke L. Journal of the American Chemical Society, Vol. 134, Issue 36 https://doi.org/10.1021/ja305709z	journal	August 2012
Ultimate Limits to Intercalation Reactions for Lithium Batteries Whittingham, M. Stanley Chemical Reviews, Vol. 114, Issue 23 https://doi.org/10.1021/cr5003003	journal	October 2014
Can Multielectron Intercalation Reactions Be the Basis of Next Generation Batteries? Whittingham, M. Stanley; Siu, Carrie; Ding, Jia Accounts of Chemical Research, Vol. 51, Issue 2 https://doi.org/10.1021/acs.accounts.7b00527	journal	January 2018
Effect of Structure on the Fe[sup 3+]∕Fe[sup 2+] Redox Couple in Iron Phosphates Padhi, A. K. Journal of The Electrochemical Society, Vol. 144, Issue 5 https://doi.org/10.1149/1.1837649	journal	January 1997
Thermodynamics, Kinetics and Structural Evolution of ε-LiVOPO ₄ over Multiple Lithium Intercalation Lin, Yuh-Chieh; Wen, Bohua; Wiaderek, Kamila M. Chemistry of Materials, Vol. 28, Issue 6 https://doi.org/10.1021/acs.chemmater.5b04880	journal	February 2016
Comparison of the polymorphs of VOPO ₄ as multi-electron cathodes for rechargeable alkali-ion batteries Lin, Yuh-Chieh; Hidalgo, Marc F. V.; Chu, Iek-Heng Journal of Materials Chemistry A, Vol. 5, Issue 33 https://doi.org/10.1039/C7TA04558A	journal	January 2017
Uniform second Li ion intercalation in solid state ϵ-LiVOPO4 Wangoh, Linda W.; Sallis, Shawn; Wiaderek, Kamila M. Applied Physics Letters, Vol. 109, Issue 5 https://doi.org/10.1063/1.4960452	journal	August 2016
Molybdenum Substituted Vanadyl Phosphate ε-VOPO ₄ with Enhanced Two-Electron Transfer Reversibility and Kinetics for Lithium-Ion Batteries Wen, Bohua; Wang, Qi; Lin, Yuhchieh Chemistry of Materials, Vol. 28, Issue 9 https://doi.org/10.1021/acs.chemmater.6b00891	journal	April 2016
KVOPO ₄ : A New High Capacity Multielectron Na-Ion Battery Cathode Ding, Jia; Lin, Yuh-Chieh; Liu, Jue Advanced Energy Materials, Vol. 8, Issue 21 https://doi.org/10.1002/aenm.201800221	journal	May 2018
A revolution in lighting Pust, Philipp; Schmidt, Peter J.; Schnick, Wolfgang Nature Materials, Vol. 14, Issue 5 https://doi.org/10.1038/nmat4270	journal	April 2015
Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes Wang, Zhenbin; Ha, Jungmin; Kim, Yoon Hwa Joule, Vol. 2, Issue 5 https://doi.org/10.1016/j.joule.2018.01.015	journal	May 2018
The inorganic crystal structure data base Bergerhoff, G.; Hundt, R.; Sievers, R. Journal of Chemical Information and Modeling, Vol. 23, Issue 2 https://doi.org/10.1021/ci00038a003	journal	May 1983
Electronic Structure Descriptor for the Discovery of Narrow-Band Red-Emitting Phosphors Wang, Zhenbin; Chu, Iek-Heng; Zhou, Fei Chemistry of Materials, Vol. 28, Issue 11 https://doi.org/10.1021/acs.chemmater.6b01496	journal	May 2016
Elucidating Structure–Composition–Property Relationships of the β-SiAlON:Eu ²⁺ Phosphor Wang, Zhenbin; Ye, Weike; Chu, Iek-Heng Chemistry of Materials, Vol. 28, Issue 23 https://doi.org/10.1021/acs.chemmater.6b03555	journal	November 2016
Understanding the links between composition, polyhedral distortion, and luminescence properties in green-emitting β-Si_6−zAl_zO_zN_8−z:Eu²⁺ phosphors Cozzan, Clayton; Laurita, Geneva; Gaultois, Michael W. Journal of Materials Chemistry C, Vol. 5, Issue 38 https://doi.org/10.1039/C7TC03039H	journal	January 2017
Thermal properties of graphite, molybdenum and tantalum to their destruction temperatures Rasor, N. S.; McClelland, J. D. Journal of Physics and Chemistry of Solids, Vol. 15, Issue 1-2 https://doi.org/10.1016/0022-3697(60)90095-0	journal	August 1960
Nanostructured high-strength molybdenum alloys with unprecedented tensile ductility Liu, G.; Zhang, G. J.; Jiang, F. Nature Materials, Vol. 12, Issue 4 https://doi.org/10.1038/nmat3544	journal	January 2013
Toughening of brittle materials by grain boundary engineering Watanabe, Tadao; Tsurekawa, Sadahiro Materials Science and Engineering: A, Vol. 387-389 https://doi.org/10.1016/j.msea.2004.01.140	journal	December 2004
Computational study of metallic dopant segregation and embrittlement at molybdenum grain boundaries Tran, Richard; Xu, Zihan; Zhou, Naixie Acta Materialia, Vol. 117 https://doi.org/10.1016/j.actamat.2016.07.005	journal	September 2016
Bismuth-induced embrittlement of copper grain boundaries Duscher, Gerd; Chisholm, Matthew F.; Alber, Uwe Nature Materials, Vol. 3, Issue 9 https://doi.org/10.1038/nmat1191	journal	August 2004
Molybdenum Silicide Based Materials and Their Properties Yao, Z.; Stiglich, J.; Sudarshan, T. S. Journal of Materials Engineering and Performance, Vol. 8, Issue 3 https://doi.org/10.1361/105994999770346837	journal	June 1999
Pesting of the high-temperature intermetallic MoSi2 Chou, T. C.; Nieh, T. G. JOM, Vol. 45, Issue 12 https://doi.org/10.1007/BF03222509	journal	December 1993
Mo-Si-B Alloys for Ultrahigh-Temperature Structural Applications Lemberg, J. A.; Ritchie, R. O. Advanced Materials, Vol. 24, Issue 26 https://doi.org/10.1002/adma.201200764	journal	June 2012
Improvement in the ductility of molybdenum alloys due to grain boundary segregation Miller, M. K.; Kenik, E. A.; Mousa, M. S. Scripta Materialia, Vol. 46, Issue 4 https://doi.org/10.1016/S1359-6462(01)01242-8	journal	February 2002
Effect of Zr, B and C additions on the ductility of molybdenum Miller, M. K.; Bryhan, A. J. Materials Science and Engineering: A, Vol. 327, Issue 1 https://doi.org/10.1016/S0921-5093(01)01880-9	journal	April 2002
Ductilization of Mo–Si solid solutions manufactured by powder metallurgy Saage, H.; Krüger, M.; Sturm, D. Acta Materialia, Vol. 57, Issue 13 https://doi.org/10.1016/j.actamat.2009.04.040	journal	August 2009
Role of Zr in strengthening MoSi2 from density functional theory calculations Zheng, Hui; Tran, Richard; Li, Xiang-Guo Acta Materialia, Vol. 145 https://doi.org/10.1016/j.actamat.2017.12.017	journal	February 2018
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials Thompson, A. P.; Swiler, L. P.; Trott, C. R. Journal of Computational Physics, Vol. 285 https://doi.org/10.1016/j.jcp.2014.12.018	journal	March 2015
Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals Li, Xiang-Guo; Hu, Chongze; Chen, Chi Physical Review B, Vol. 98, Issue 9 https://doi.org/10.1103/PhysRevB.98.094104	journal	September 2018
Accurate force field for molybdenum by machine learning large materials data Chen, Chi; Deng, Zhi; Tran, Richard Physical Review Materials, Vol. 1, Issue 4 https://doi.org/10.1103/PhysRevMaterials.1.043603	journal	September 2017
On representing chemical environments Bartók, Albert P.; Kondor, Risi; Csányi, Gábor Physical Review B, Vol. 87, Issue 18 https://doi.org/10.1103/PhysRevB.87.184115	journal	May 2013
The equilibrium diagram of the system molybdenum-nickel Casselton, R. E. W.; Hume-Rothery, W. Journal of the Less Common Metals, Vol. 7, Issue 3 https://doi.org/10.1016/0022-5088(64)90068-2	journal	September 1964
Calculating phase diagrams using PANDAT and panengine Chen, S. -L.; Zhang, F.; Xie, F. -Y. JOM, Vol. 55, Issue 12 https://doi.org/10.1007/s11837-003-0010-5	journal	December 2003
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
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
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
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
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
Universal fragment descriptors for predicting properties of inorganic crystals Isayev, Olexandr; Oses, Corey; Toher, Cormac Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms15679	journal	June 2017
Predicting the thermodynamic stability of perovskite oxides using machine learning models Li, Wei; Jacobs, Ryan; Morgan, Dane Computational Materials Science, Vol. 150 https://doi.org/10.1016/j.commatsci.2018.04.033	journal	July 2018
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
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
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
Theoretical approaches to x-ray absorption fine structure Rehr, J. J.; Albers, R. C. Reviews of Modern Physics, Vol. 72, Issue 3 https://doi.org/10.1103/RevModPhys.72.621	journal	July 2000
Ab initio theory and calculations of X-ray spectra Rehr, John J.; Kas, Joshua J.; Prange, Micah P. Comptes Rendus Physique, Vol. 10, Issue 6 https://doi.org/10.1016/j.crhy.2008.08.004	journal	July 2009
Band-structure calculations for the 3 $d$ transition metal oxides in $G W$ Lany, Stephan Physical Review B, Vol. 87, Issue 8 https://doi.org/10.1103/PhysRevB.87.085112	journal	February 2013
Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms Choudhary, Kamal; Zhang, Qin; Reid, Andrew C. E. Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.82	journal	May 2018
Efficient implementation of core-excitation Bethe–Salpeter equation calculations Gilmore, K.; Vinson, John; Shirley, E. L. Computer Physics Communications, Vol. 197 https://doi.org/10.1016/j.cpc.2015.08.014	journal	December 2015
Bethe-Salpeter equation calculations of core excitation spectra Vinson, J.; Rehr, J. J.; Kas, J. J. Physical Review B, Vol. 83, Issue 11 https://doi.org/10.1103/PhysRevB.83.115106	journal	March 2011

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Title: Accelerating materials science with high-throughput computations and machine learning

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