REFERENCES

1. Schleder GR, Padilha ACM, Acosta CM, Costa M, Fazzio A. From DFT to machine learning: recent approaches to materials science-a review. J Phys Mater 2019;2:032001.

2. Agrawal A, Choudhary A. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL Mater 2016;4:053208.

3. Wang H, Xiang X, Zhang L. On the data-driven materials innovation infrastructure. Engineering 2020;6:609-11.

4. Jain A, Hautier G, Moore CJ, et al. A high-throughput infrastructure for density functional theory calculations. Comput Mater Sci 2011;50:2295-310.

5. Curtarolo S, Hart GL, Nardelli MB, Mingo N, Sanvito S, Levy O. The high-throughput highway to computational materials design. Nat Mater 2013;12:191-201.

6. Castelli IE, Olsen T, Datta S, et al. Computational screening of perovskite metal oxides for optimal solar light capture. Energy Environ Sci 2012;5:5814-9.

7. Potyrailo R, Rajan K, Stoewe K, Takeuchi I, Chisholm B, Lam H. Combinatorial and high-throughput screening of materials libraries: review of state of the art. ACS Comb Sci 2011;13:579-633.

8. Kohanoff J, Gidopoulos N I. 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, Gill PMW, Pople JA. The performance of a family of density functional methods. J Chem Phys 1993;98:5612-26.

10. Ghiringhelli LM, Carbogno C, Levchenko S, et al. Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats. npj Comput Mater 2017:3.

11. Jain A, Ong SP, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Materials 2013;1:011002.

12. Kirklin S, Saal JE, Meredig B, et al. The open quantum materials database (OQMD): assessing the accuracy of DFT formation energies. npj Comput Mater 2015:1.

13. Curtarolo S, Setyawan W, Hart GL, et al. AFLOW: an automatic framework for high-throughput materials discovery. Comput Mater Sci 2012;58:218-26.

14. Rajan K. Materials informatics: the materials “gene” and big data. Annu Rev Mater Res 2015;45:153-69.

15. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data 2016;3:160018.

16. Scheffler M, Aeschlimann M, Albrecht M, et al. FAIR data enabling new horizons for materials research. Nature 2022;604:635-42.

17. Vangay P, Burgin J, Johnston A, et al. Microbiome metadata standards: report of the national microbiome data collaborative’s workshop and follow-on activities. mSystems 2021;6:e01194-20.

18. Gennari JH, König M, Misirli G, Neal ML, Nickerson DP, Waltemath D. OMEX metadata specification (version 1.2). J Integr Bioinform 2021:18.

19. Wierling A, Schwanitz VJ, Altinci S, et al. FAIR metadata standards for low carbon energy research - a review of practices and how to advance. Energies 2021;14:6692.

20. Coudert F. Materials databases: the need for open, interoperable databases with standardized data and rich metadata. Adv Theory Simul 2019;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, Yoshida R, Liu C, et al. Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning. Phys Rev Materials 2021:5.

24. Ju S, Shiomi J. Materials informatics for heat transfer: recent progresses and perspectives. Nanoscale Microscale Thermophys Eng 2019;23:157-72.

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

26. Kresse G, Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B Condens Matter 1996;54:11169-86.

27. Giannozzi P, Baroni S, Bonini N, et al. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J Phys Condens Matter 2009;21:395502.

28. Pickard CJ, Mauri F. All-electron magnetic response with pseudopotentials: NMR chemical shifts. Phys Rev B 2001:63.

29. Neese F. The ORCA program system. WIREs Comput Mol Sci 2012;2:73-8.

30. Chaput L, Togo A, Tanaka I, Hug G. Phonon-phonon interactions in transition metals. Phys Rev B 2011:84.

31. Li W, Lindsay L, Broido DA, Stewart DA, Mingo N. Thermal conductivity of bulk and nanowire Mg2SixSn1-x alloys from first principles. Phys Rev B 2012:86.

32. Tadano T, Gohda Y, Tsuneyuki S. Anharmonic force constants extracted from first-principles molecular dynamics: applications to heat transfer simulations. J Phys Condens Matter 2014;26:225402.

33. Carrete J, Vermeersch B, Katre A, et al. almaBTE : A solver of the space-time dependent Boltzmann transport equation for phonons in structured materials. Comp Phys Commun 2017;220:351-62.

34. Togo A, Chaput L, Tanaka I. Distributions of phonon lifetimes in Brillouin zones. Phys Rev B 2015:91.

35. Li W, Carrete J, A. Katcho N, Mingo N. ShengBTE: a solver of the Boltzmann transport equation for phonons. Comp Phys Commun 2014;185:1747-58.

36. Gu X, Li S, Bao H. Thermal conductivity of silicon at elevated temperature: role of four-phonon scattering and electronic heat conduction. Int J Heat Mass Transfer 2020;160:120165.

37. Liao B, Qiu B, Zhou J, Huberman S, Esfarjani K, Chen G. Significant reduction of lattice thermal conductivity by the electron-phonon interaction in silicon with high carrier concentrations: a first-principles study. Phys Rev Lett 2015;114:115901.

38. Fulkerson W, Moore JP, Williams RK, Graves RS, Mcelroy DL. Thermal conductivity, electrical resistivity, and seebeck coefficient of silicon from 100 to 1300°K. Phys Rev 1968;167:765-82.

Journal of Materials Informatics
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