Machine learning-based discovery of vibrationally stable materials

Tawfik, Sherif Abdulkader; Rashid, Mahad; Gupta, Sunil; Russo, Salvy P.; Walsh, Tiffany R.; Venkatesh, Svetha

doi:10.1038/s41524-022-00943-z

Machine learning-based discovery of vibrationally stable materials

Journal Article · Tue Jan 10 23:00:00 EST 2023 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-022-00943-z· OSTI ID:2422994

^[1]; Rashid, Mahad ^[2]; Gupta, Sunil ^[2]; ^[3]; Walsh, Tiffany R. ^[2]; Venkatesh, Svetha ^[2]

Deakin University, Geelong, VIC (Australia); Royal Melbourne Institute of Technology, VIC (Australia)
Deakin University, Geelong, VIC (Australia)
Royal Melbourne Institute of Technology, VIC (Australia)

The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials. Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials, namely thermodynamic stability. However, the vibrational stability, which is another aspect of synthesizability, of new materials is not known. Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable. Here, a dataset of vibrational stability for ~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials. This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2422994

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 9; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (37)

Predicting Thermal Properties of Crystals Using Machine Learning Tawfik, Sherif Abdulkader; Isayev, Olexandr; Spencer, Michelle J. S. Advanced Theory and Simulations, Vol. 3, Issue 2 https://doi.org/10.1002/adts.201900208	journal	December 2019
Toward Interpretable Machine Learning Models for Materials Discovery Mikulskis, Paulius; Alexander, Morgan R.; Winkler, David Alan Advanced Intelligent Systems, Vol. 1, Issue 8 https://doi.org/10.1002/aisy.201900045	journal	October 2019
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
Phonon instability and ideal strength of silicene under tension Yang, Chuanghua; Yu, Zhongyuan; Lu, Pengfei Computational Materials Science, Vol. 95 https://doi.org/10.1016/j.commatsci.2014.07.046	journal	December 2014
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
Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage Thornton, Aaron W.; Simon, Cory M.; Kim, Jihan Chemistry of Materials, Vol. 29, Issue 7 https://doi.org/10.1021/acs.chemmater.6b04933	journal	March 2017
How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids Legrain, Fleur; Carrete, Jesús; van Roekeghem, Ambroise Chemistry of Materials, Vol. 29, Issue 15 https://doi.org/10.1021/acs.chemmater.7b00789	journal	July 2017
Vibrational Properties of Metastable Polymorph Structures by Machine Learning Legrain, Fleur; van Roekeghem, Ambroise; Curtarolo, Stefano Journal of Chemical Information and Modeling, Vol. 58, Issue 12 https://doi.org/10.1021/acs.jcim.8b00279	journal	September 2018
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
Ground State and Transition State Contributions to the Rates of Intramolecular and Enzymatic Reactions Bruice, Thomas C.; Lightstone, Felice C. Accounts of Chemical Research, Vol. 32, Issue 2 https://doi.org/10.1021/ar960131y	journal	February 1999
Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints Isayev, Olexandr; Fourches, Denis; Muratov, Eugene N. Chemistry of Materials, Vol. 27, Issue 3 https://doi.org/10.1021/cm503507h	journal	January 2015
Generalized transition state theory. Bond energy-bond order method for canonical variational calculations with application to hydrogen atom transfer reactions Garrett, Bruce C.; Truhlar, Donald G. Journal of the American Chemical Society, Vol. 101, Issue 16 https://doi.org/10.1021/ja00510a019	journal	August 1979
Accelerated search for materials with targeted properties by adaptive design Xue, Dezhen; Balachandran, Prasanna V.; Hogden, John Nature Communications, Vol. 7, Issue 1 https://doi.org/10.1038/ncomms11241	journal	April 2016
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
Emerging role of machine learning in light-matter interaction Zhou, Jiajia; Huang, Bolong; Yan, Zheng Light: Science & Applications, Vol. 8, Issue 1 https://doi.org/10.1038/s41377-019-0192-4	journal	September 2019
Network analysis of synthesizable materials discovery Aykol, Muratahan; Hegde, Vinay I.; Hung, Linda Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10030-5	journal	May 2019
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
High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses Choudhary, Kamal; Garrity, Kevin F.; Sharma, Vinit npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-0337-2	journal	May 2020
Exploring and machine learning structural instabilities in 2D materials Manti, Simone; Svendsen, Mark Kamper; Knøsgaard, Nikolaj R. npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-00977-x	journal	March 2023
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
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
Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces Epa, V. Chandana; Yang, Jing; Mei, Ying Journal of Materials Chemistry, Vol. 22, Issue 39 https://doi.org/10.1039/c2jm34782b	journal	January 2012
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
The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals Haastrup, Sten; Strange, Mikkel; Pandey, Mohnish 2D Materials, Vol. 5, Issue 4 https://doi.org/10.1088/2053-1583/aacfc1	journal	September 2018
Special points for Brillouin-zone integrations Monkhorst, Hendrik J.; Pack, James D. Physical Review B, Vol. 13, Issue 12, p. 5188-5192 https://doi.org/10.1103/PhysRevB.13.5188	journal	June 1976
Ab initio molecular dynamics for open-shell transition metals Kresse, G.; Hafner, J. Physical Review B, Vol. 48, Issue 17 https://doi.org/10.1103/PhysRevB.48.13115	journal	November 1993
Ideal strength and phonon instability in single-layer MoS 2 Li, Tianshu Physical Review B, Vol. 85, Issue 23 https://doi.org/10.1103/PhysRevB.85.235407	journal	June 2012
Ideal strength and phonon instability of strained monolayer materials Isaacs, Eric B.; Marianetti, Chris A. Physical Review B, Vol. 89, Issue 18 https://doi.org/10.1103/PhysRevB.89.184111	journal	May 2014
High-throughput first-principles search for new ferroelectrics Garrity, Kevin F. Physical Review B, Vol. 97, Issue 2 https://doi.org/10.1103/PhysRevB.97.024115	journal	January 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
Phonon Instabilities and the Ideal Strength of Aluminum Clatterbuck, D. M.; Krenn, C. R.; Cohen, Marvin L. Physical Review Letters, Vol. 91, Issue 13 https://doi.org/10.1103/PhysRevLett.91.135501	journal	September 2003
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Behler, Jörg; Parrinello, Michele Physical Review Letters, Vol. 98, Issue 14 https://doi.org/10.1103/PhysRevLett.98.146401	journal	April 2007
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
Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors Tawfik, Sherif Abdulkader; Russo, Salvy P. Journal of Cheminformatics, Vol. 14, Issue 1 https://doi.org/10.1186/s13321-022-00658-9	journal	November 2022
SMOTE: Synthetic Minority Over-sampling Technique Chawla, N. V.; Bowyer, K. W.; Hall, L. O. Journal of Artificial Intelligence Research, Vol. 16 https://doi.org/10.1613/jair.953	journal	January 2002
High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses Choudhary, Kamal figshare https://doi.org/10.6084/m9.figshare.11916720.v1	dataset	January 2020

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