A metadata schema for lattice thermal conductivity from first-principles calculations

Abstract

Materials genome engineering databases represent fundamental infrastructures for data-driven materials design, in which the data resources should satisfy the FAIR (Findable, Accessible, Interoperable and Reusable) principles. However, a variety of challenges, such as data standardization, veracity and longevity, still impede the progress of data-driven materials science, including both high-throughput experiments and simulations. In this work, we propose a metadata schema for lattice thermal conductivity from first-principles calculations. The calculation workflow for lattice thermal conductivity includes structural optimization and the calculation of interatomic force constants and lattice thermal conductivity. The data generated during the calculation process corresponds to the virtual sample information, virtual source data and processed data, respectively, as specified in the General rule for materials genome engineering data of the Chinese Society for Testing and Materials. Following this general rule, the metadata structure and schema for each action are systematically defined and all metadata elements can be collected completely. Although this metadata schema is specific to lattice thermal conductivity calculations, it provides general rules and insights for other computational materials data in materials genome engineering.

Keywords

Materials genome engineering / metadata schema / first-principles calculations / lattice thermal conductivity

References

[1] Schleder GR,Acosta CM,Fazzio A.From DFT to machine learning: recent approaches to materials science-a review.J Phys Mater2019;2:032001

[2] Agrawal A.Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science.APL Mater2016;4:053208

[3] Wang H,Zhang L.On the data-driven materials innovation infrastructure.Engineering2020;6:609-11

[4] Jain A,Moore CJ.A high-throughput infrastructure for density functional theory calculations.Comput Mater Sci2011;50:2295-310

[5] Curtarolo S,Nardelli MB,Sanvito S.The high-throughput highway to computational materials design.Nat Mater2013;12:191-201

[6] Castelli IE,Datta S.Computational screening of perovskite metal oxides for optimal solar light capture.Energy Environ Sci2012;5:5814-9

[7] Potyrailo R,Stoewe K,Chisholm B.Combinatorial and high-throughput screening of materials libraries: review of state of the art.ACS Comb Sci2011;13:579-633

[8] Kohanoff J. Density functional theory: basics, new trends and applications. handbook of molecular physics and quantum chemistry 2003;2:532-568. Available from: https://bbs.sciencenet.cn/blog/admin/images/upfiles/20071017221454599631.pdf [Last accessed on 27 Oct 2022]

[9] Johnson BG,Pople JA.The performance of a family of density functional methods.J Chem Phys1993;98:5612-26

[10] Ghiringhelli LM,Levchenko S.Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats.npj Comput Mater2017;3

[11] Jain A,Hautier G.Commentary: the materials project: a materials genome approach to accelerating materials innovation.APL Materials2013;1:011002

[12] Kirklin S,Meredig B.The open quantum materials database (OQMD): assessing the accuracy of DFT formation energies.npj Comput Mater2015;1

[13] Curtarolo S,Hart GL.AFLOW: an automatic framework for high-throughput materials discovery.Comput Mater Sci2012;58:218-26

[14] Rajan K.Materials informatics: the materials “gene” and big data.Annu Rev Mater Res2015;45:153-69

[15] Wilkinson MD,Aalbersberg IJ.The FAIR guiding principles for scientific data management and stewardship.Sci Data2016;3:160018 PMCID:PMC4792175

[16] Scheffler M,Albrecht M.FAIR data enabling new horizons for materials research.Nature2022;604:635-42

[17] Vangay P,Johnston A.Microbiome metadata standards: report of the national microbiome data collaborative’s workshop and follow-on activities.mSystems2021;6:e01194-20 PMCID:PMC8573954

[18] Gennari JH,Misirli G,Nickerson DP.OMEX metadata specification (version 1.2).J Integr Bioinform2021;18 PMCID:PMC8560343

[19] Wierling A,Altinci S.FAIR metadata standards for low carbon energy research - a review of practices and how to advance.Energies2021;14:6692

[20] Coudert F.Materials databases: the need for open, interoperable databases with standardized data and rich metadata.Adv Theory Simul2019;2:1900131

[21] Chinese Society for Testing and Materials (CSTM). T/CSTM 00120-2019: General rule for materials genome engineering data. Available from: http://www.cstm.com.cn/article/details/ef49a444-80ca-4e71-99eb-e1e76c039d9f [Last accessed on 27 Oct 2022]

[22] Chinese Society for Testing and Materials (CSTM). T/CSTM 00837-2022: materials genome engineering data-Metadata standardization principle and method. Available from: http://www.cstm.com.cn/article/details/390ce11f-41a2-4d01-8544-04012bb13782 [Last accessed on 27 Oct 2022]

[23] Ju S,Liu C.Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning.Phys Rev Materials2021;5

[24] Ju S.Materials informatics for heat transfer: recent progresses and perspectives.Nanoscale Microscale Thermophys Eng2019;23:157-72

[25] Frisch M J,Schlegel H B. Gaussian 16, revision C.01. Available from: https://gaussian.com/ [Last accessed on 27 Oct 2022]

[26] Kresse G.Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.Phys Rev B Condens Matter1996;54:11169-86

[27] Giannozzi P,Bonini N.QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials.J Phys Condens Matter2009;21:395502

[28] Pickard CJ.All-electron magnetic response with pseudopotentials: NMR chemical shifts.Phys Rev B2001;63

[29] Neese F.The ORCA program system.WIREs Comput Mol Sci2012;2:73-8

[30] Chaput L,Tanaka I.Phonon-phonon interactions in transition metals.Phys Rev B2011;84

[31] Li W,Broido DA,Mingo N.Thermal conductivity of bulk and nanowire Mg₂Si_xSn_1-x alloys from first principles.Phys Rev B2012;86

[32] Tadano T,Tsuneyuki S.Anharmonic force constants extracted from first-principles molecular dynamics: applications to heat transfer simulations.J Phys Condens Matter2014;26:225402

[33] Carrete J,Katre A.almaBTE : A solver of the space-time dependent Boltzmann transport equation for phonons in structured materials.Comp Phys Commun2017;220:351-62

[34] Togo A,Tanaka I.Distributions of phonon lifetimes in Brillouin zones.Phys Rev B2015;91

[35] Li W,A .Katcho N, Mingo N. ShengBTE: a solver of the Boltzmann transport equation for phonons.Comp Phys Commun2014;185:1747-58

[36] Gu X,Bao H.Thermal conductivity of silicon at elevated temperature: role of four-phonon scattering and electronic heat conduction.Int J Heat Mass Transfer2020;160:120165

[37] Liao B,Zhou J,Esfarjani K.Significant reduction of lattice thermal conductivity by the electron-phonon interaction in silicon with high carrier concentrations: a first-principles study.Phys Rev Lett2015;114:115901

[38] Fulkerson W,Williams RK,Mcelroy DL.Thermal conductivity, electrical resistivity, and seebeck coefficient of silicon from 100 to 1300°K.Phys Rev1968;167:765-82