Integrating computational materials science and materials informatics for the modeling of phase stability

Abstract

With rapid developments in big data and artificial intelligence technologies, materials informatics has become a new paradigm of materials science and engineering. In this review, the progress of modeling studies of phase stability in alloys is presented, with particular attention given to the development of the paradigm from traditional computational materials science (CMS) to materials informatics. The features of CMS models for phase stability studies are compared with those of data-driven approaches. The advantages of data-driven modeling in the framework of materials informatics are revealed. The approaches for developing interpretable machine learning, which has been mainly integrated with the developed CMS models and material science theories, are also discussed. Finally, the prospects for data-driven materials design based on the stability control of the dominant phases with regards to performance are proposed.

Keywords

Phase stability / computational materials science / materials informatics / databases / machine learning / data-driven materials design

References

[1] Gleiter H.Nanocrystalline materials..Prog Mater Sci1989;33:223-315

[2] Wu Z,Gao Y,Stocks G.Phase stability, physical properties and strengthening mechanisms of concentrated solid solution alloys..Curr Opin Solid State Mater Sci2017;21:267-84

[3] Peng HR,Chen YZ.Thermal stability of nanocrystalline materials: thermodynamics and kinetics..Int Mater Rev2016;62:303-33

[4] Andrievski RA.Review of thermal stability of nanomaterials..J Mater Sci2014;49:1449-60

[5] Duan W.Computational materials science..Curr Opin Solid State Mater Sci2006;10:1-1

[6] Seaman L.Development of computational models for microstructural features..AIP Conf Proc1982;78:118-129

[7] Moriwake H,Fisher CA.First-principles calculations of lithium-ion migration at a coherent grain boundary in a cathode material, LiCoO(2)..Adv Mater2013;25:618-22

[8] Derlet P.Million-atom molecular dynamics simulations of magnetic iron..Prog Mater Sci2007;52:299-318

[9] Xiao P.Kinetic monte carlo study of Li intercalation in LiFePO₄..ACS Nano2018;12:844-51

[10] Gránásy L,Warren JA.Phase-field modeling of crystal nucleation in undercooled liquids - a review..Prog Mater Sci2019;106:100569

[11] Abe T,Saengdeejimg A.Computational phase diagrams for the Nd-based magnets based on the combined ab initio/CALPHAD approach..Scripta Materialia2018;154:305-10

[12] Liu Z.Computational thermodynamics and its applications..Acta Materialia2020;200:745-92

[13] Wang J,Zorn JA.Strain anisotropy and magnetic domain structures in multiferroic heterostructures: High-throughput finite-element and phase-field studies..Acta Materialia2019;176:73-83

[14] Wang P,Zhang Q.A brief introduction of mechanoinformations..Chin J Nat2018;40:313-22(in Chinese)

[15] Ramakrishna S,Lu W.Materials informatics..J Intell Manuf2019;30:2307-26

[16] Xiong J,Zhang T.A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys..2020;187:108378

[17] Xiong J,Zhang T.Machine learning prediction of glass-forming ability in bulk metallic glasses..Comp Mater Sci2021;192:110362

[18] Xiong J,Shi S.Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses..MRS Communications2019;9:576-85

[19] Wei Q,Sun S.Multi-objective machine learning of four mechanical properties of steels..Sci Sin-Tech2021;51:722-36

[20] Zhang T,Wang J.Machine learning prediction of the hardness of tool and mold steels..Sci Sin-Tech2019;49:1148-58

[21] Wang WY,Liaw PK.Editorial: data-driven integrated computational materials engineering for high-entropy materials..Front Mater2021;8:664829

[22] Zou C,Wang WY.Integrating data mining and machine learning to discover high-strength ductile titanium alloys..Acta Materialia2021;202:211-21

[23] Li R,Wang WY,Zhang Y.High-throughput calculations for high-entropy alloys: a brief review..Front Mater2020;7:290

[24] Yi Wang W,Liu W.Integrated computational materials engineering for advanced materials: a brief review..Comp Mater Sci2019;158:42-8

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

[26] de Pablo JJ,Webb MA.New frontiers for the materials genome initiative..npj Comput Mater2019;5:

[27] de Pablo JJ,Kovacs CL,Ramirez AP.The materials genome Initiative, the interplay of experiment, theory and computation..Curr Opin Solid State Mater Sci2014;18:99-117

[28] Himanen L,Foster AS.Data-driven materials science: status, challenges, and perspectives..Adv Sci2019;6:1900808

[29] Wang H,Xiang XD.Materials genome enables research and development revolution..Sci Tech Rev2015;33:13-9(in Chinese)

[30] Shi SQ,Cui YH.Multiscale materials computational methods..Sci Tech Rev2015;33:20-30(in Chinese)

[31] Wang H,Zhang LT.Data + AI: the core of materials genomic engineering..Sci Tech Rev2018;36:15-21(in Chinese)

[32] Su YJ,Bai Y,Xie JX.Progress in materials genome engineering in China..Acta Metall Sin2020;56:1313-23(in Chinese)

[33] Wang WY,Lin D.DID code: a bridge connecting the materials genome engineering database with inheritable integrated intelligent manufacturing..Engineering2020;6:612-20

[34] Wang WY,Lin D.A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: insight into atomic and electronic building blocks..J Mater Res2020;35:872-89

[35] Chen C,Ye W,Deng Z.A critical review of machine learning of energy materials..Adv Energy Mater2020;10:1903242

[36] Yang F,Wang Q.Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus..npj Comput Mater2020;6:101

[37] Möller JJ,Krugel G,Elsässer C.Compositional optimization of hard-magnetic phases with machine-learning models..Acta Materialia2018;153:53-61

[38] Jinnouchi R.predicting catalytic activity of nanoparticles by a DFT-aided machine-learning algorithm..J Phys Chem Lett2017;8:4279-83

[39] Ling J,Sheng Y,Zhang L.A MGI-oriented investigation of the Young’s modulus and its application to the development of a novel Ti-Nb-Zr-Cr bio-alloy..Mater Sci Eng C Mater Biol Appl2020;106:110265

[40] Liu Y,Ju W.Materials discovery and design using machine learning..Journal of Materiomics2017;3:159-77

[41] Wei J,Sun X.Machine learning in materials science..InfoMat2019;1:338-58

[42] Liu S,Yin H.An infrastructure with user-centered presentation data model for integrated management of materials data and services..npj Comput Mater2021;7:88

[43] Yılmaz B.Critical review of machine learning applications in perovskite solar research..Nano Energy2021;80:105546

[44] Ludwig A.Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods..npj Comput Mater2019;5:

[45] Gutfleisch O,Brück E,Sankar SG.Magnetic materials and devices for the 21st century: stronger, lighter, and more energy efficient..Adv Mater2011;23:821-42

[46] Song X,Lu N.Crystal structures and magnetic performance of nanocrystalline Sm-Co compounds..Front Mater Sci2012;6:207-15

[47] Zhang Z,Qiao Y.A nanocrystalline Sm-Co compound for high-temperature permanent magnets..Nanoscale2013;5:2279-84

[48] Xu W,Lu N.Thermodynamic and experimental study on phase stability in nanocrystalline alloys..Acta Materialia2010;58:396-407

[49] Zhang Z,Xu W.Phase evolution and its effects on the magnetic performance of nanocrystalline SmCo₇ alloy..Acta Materialia2011;59:1808-17

[50] Luo J,Rao GH.Formation, crystal structure and magnetic properties of rare-earth transition-metal intermetallic compounds (I)..J Chin Rare Earth Soc2012;30:385-402(in Chinese)

[51] Luo J,Rao FH.Formation, crystal structure and magnetic properties of rare-earth transition-metal intermetallic compounds (II)..J Chin Rare Earth Soc2012;30:513-24(in Chinese)

[52] Shen B,Baker AA.Chemical synthesis of magnetically hard and strong rare earth metal based nanomagnets..Angew Chem Int Ed Engl2019;58:602-6

[53] Li X,Song W.Novel bimorphological anisotropic bulk nanocomposite materials with high energy products..Adv Mater2017;29:1606430

[54] Liu S.Sm-Co high-temperature permanent magnet materials..Chinese Phys B2019;28:017501

[55] Mccallum R,Skomski R,Anderson I.Practical aspects of modern and future permanent magnets..Annu Rev Mater Res2014;44:451-77

[56] Okamoto H.Co-Sm (Cobalt-Samarium)..J Phase Equilib Diffus2011;32:165-6

[57] Liu XM,Li DP,Song XY.Preparation and properties of polycrystalline and nanocrystalline Sm₃Co alloys..Acta Phys Sin2014;63:098102(in Chinese)

[58] Li D,Zhang Z,Qiao Y.Preparation and properties of single-phase Sm₅Co₂ nanocrystalline alloy..Acta Metallurgica Sinica2012;48:1248

[59] Song X,Seyring M.Abnormal crystal structure stability of nanocrystalline Sm₂Co₁₇ permanent magnet..Appl Phys Lett2009;94:023102

[60] Lu N,Zhang J.Crystal structure and magnetic properties of ultrafine nanocrystalline SmCo₃ compound..Nanotechnology2010;21:115708

[61] Zhang Z,Xu W,Rettenmayr M.Crystal structure and magnetic performance of single-phase nanocrystalline SmCo₇ alloy..Scripta Materialia2010;62:594-7

[62] Zhang Z,Xu W,Liu X.Crystal structure and magnetic performance of nanocrystalline SmCo_9.8 alloy..J Appl Phys2011;110:124318

[63] Yue M,Liu JP.Fabrication of bulk nanostructured permanent magnets with high energy density: challenges and approaches..Nanoscale2017;9:3674-97

[64] Xu WW,Zhang ZX.multiphase equilibrium, phase stability and phase transformation in nanocrystalline alloy systems..NANO2012;07:1250012

[65] Mchale JM.Surface energies and thermodynamic phase stability in nanocrystalline aluminas..Science1997;277:788-91

[66] Ikeda Y,Körmann F.Ab initio phase stabilities and mechanical properties of multicomponent alloys: a comprehensive review for high entropy alloys and compositionally complex alloys..Materials Characterization2019;147:464-511

[67] Xu WW,Li ED,Li LM.Phase configuration and stability in the nanocrystalline Sm-Co system..Acta Phys Sin2009;58:3280-6(in Chinese)

[68] Zhang H.Thermodynamic analysis of phase stability of nanocrystalline titania..J Mater Chem1998;8:2073-6

[69] Song X,Li L,Liu G.Correlation of thermodynamics and grain growth kinetics in nanocrystalline metals..Acta Materialia2006;54:5541-50

[70] Guinea F,Smith JR.Scaling relations in the equation of state, thermal expansion, and melting of metals..Appl Phys Lett1984;44:53-5

[71] Xu W,Zhang Z.Thermodynamic study on metastable phase: from polycrystalline to nanocrystalline system..Appl Phys Lett2010;97:181911

[72] Xu WW,Li ED,Zhang JX.Thermodynamic study on phase stability in nanocrystalline Sm-Co alloy system..J Appl Phys2009;105:104310

[73] Xu W,Zhang Z.Experimental and modeling studies on phase stability of nanocrystalline magnetic Sm₂Co₇..Materials Science and Engineering: B2013;178:971-6

[74] Xu W,Lu N,Rettenmayr M.Nanoscale thermodynamic study on phase transformation in the nanocrystalline Sm₂Co₁₇ alloy..Nanoscale2009;1:238-44

[75] Meng Q,Rong Y,Hsu T.Size effect on the Fe nanocrystalline phase transformation..Acta Materialia2002;50:4563-70

[76] Hu B,Zhang J,Du Y.Progress in interfacial thermodynamics and grain boundary complexion diagram..Acta Metall Sinica2021;57:1199-214

[77] Song X,Yue M.Technique for preparing ultrafine nanocrystalline bulk material of pure rare-earth metals..Adv Mater2006;18:1210-5

[78] Fecht HJ.Intrinsic instability and entropy stabilization of grain boundaries..Phys Rev Lett1990;65:610-3

[79] Wagner M.Structure and thermodynamic properties of nanocrystalline metals..Phys Rev B Condens Matter1992;45:635-9

[80] Yang CC.Size-dependent phase stability of silver nanocrystals..J Phys Chem C2008;112:16400-4

[81] Johnson CA.Generalization of the Gibbs-Thomson equation..Surface Science1965;3:429-44

[82] Guo K,Mao F.How non-ferromagnetic Mn enhances the magnetization of SmCo₇ based alloys..Nanoscale2020;12:5567-77

[83] Yao Z.Structure and magnetic properties of SmCo_xTi_0.4-1:7 ribbons..J Magn Magn Mater2008;320:1073-7

[84] Zhou J,Chen C,Sellmyer DJ.Sm-Co-Cu-Ti high-temperature permanent magnets..Appl Phys Lett2000;77:1514-6

[85] Luo J,Guo YQ.Crystal structure and magnetic properties of SmCo_5.85Si_0.90 compound..Appl Phys Lett2004;84:3094-6

[86] Guo Y,Feng W.Structural stability and magnetic properties of SmCo_7-xGa_x..Appl Phys Lett2005;86:192513

[87] Luo J,Guo Y.Effects of the doping element on crystal structure and magnetic properties of Sm(Co,M)₇ compounds (M=Si, Cu, Ti, Zr, and Hf)..Intermetallics2005;13:710-6

[88] Al-omari I,Sellmyer D.Magnetic and structural properties of SmCo_6.75-xFe_xZr_0.25 compounds..J Alloys Compd2000;298:295-8

[89] Huang MQ,McHenry M,Ma BM.Structure and magnetic properties of SmCo_7-xZr_x alloys (x = 0-0.8)..J Appl Phys1998;83:6718-20

[90] Zhou J,Liu JP.Structure and magnetic properties of SmCo_7-xTi_x with TbCu₇-type structure..J Appl Phys2000;87:5299-301

[91] Luo J,Guo YQ.Crystal structure and magnetic properties of SmCo_7-xHf_x compounds..Appl Phys Lett2004;85:5299-301

[92] Guo Y,Li W,Liang J.Magnetism and phase stability of R(Co,M)₇ pseudobinary intermetallics with TbCu₇-type structure..J Appl Phys2007;101:023919

[93] Li Y,Chen Y.Atomistic simulation for disordered TbCu₇-type compounds SmCo₇ and Sm(Co,T)₇ (T=Ti, Ga, Si, Cu, Hf, Zr)..Solid State Sciences2010;12:33-8

[94] Sobolev A,Popov A.Embedded atom potential for Sm-Co compounds obtained by force-matching..J Magn Magn Mater2019;490:165468

[95] Song XY,Zhang ZX.Nanoscale stabilization of metastable phase: thermodynamic model and experimental studies..Acta Phys Sin2012;61:200510(in Chinese)

[96] Seyring M,Zhang Z.Concurrent ordering and phase transformation in SmCo₇ nanograins..Nanoscale2015;7:12126-32

[97] Hua G,Tang F.Modeling and experimental studies of Hf-doped nanocrystalline SmCo₇ alloys..Cryst Eng Comm2016;18:8080-8

[98] Mao F,Liu D,Tang F.Structural stability and magnetic properties of SmCo₅ compounds doped with transition metal elements..J Alloys Compd2019;810:151888

[99] Hua G,Liu D,Wang H.Effects of Hf on phase structure and magnetic performance of nanocrystalline SmCo₇-type alloy..J Mater Sci2016;51:3390-7

[100] Das B,Skomski R.Anisotropy and orbital moment in Sm-Co permanent magnets..Phys Rev B2019;100:024419

[101] Söderlind P,Locht ILM.Prediction of the new efficient permanent magnet SmCoNiFe₃..Phys Rev B2017;96:100404

[102] Gavrikov IS,Zheleznyi MV,Khotulev ES.Effect of Ni doping on stabilization of Sm(Co_1-xFe_x)₅ compound: thermodynamic calculation and experiment..J Phys Condens Matter2020;32:425803

[103] Chen Nx.Carlsson-Gelatt-Ehrenreich technique and the Möbius inversion theorem..Phys Rev B Condens Matter1992;45:8177-80

[104] Chen Nx.Modified Möbius inverse formula and its applications in physics..Phys Rev Lett1990;64:1193-5

[105] Shen J,Chen N.Atomistic investigation on site preference and lattice vibrations of Sm(Co,M)₁₂ (M = Cr, Ti, V, Nb, Fe)..J Alloys Compd2005;388:208-14

[106] Shen J,Chen N.Atomistic simulation on phase stability and site preference of R₂ (Co, Mn)₁₇ (R = Nd, Sm, Gd)..Modelling Simul Mater Sci Eng2005;13:239-47

[107] Wang AY,Kauwe SK.Machine learning for materials scientists: an introductory guide toward best practices..Chem Mater2020;32:4954-65

[108] Ong SP.Accelerating materials science with high-throughput computations and machine learning..Comp Mater Sci2019;161:143-50

[109] Liu D,Tang F.Selecting doping elements by data mining for advanced magnets..Chem Mater2019;31:10117-25

[110] Liu X,Guo K,Mao F.Development of database and information management system for data-driven materials design..Sci Sin-Tech2020;50:786-800

[111] Vassiliadis P.A survey of extract-transform-load technology..Int J Data Warehous Min2009;5:1-27

[112] Ong SP,Jain A.The materials application programming interface (API): a simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles..Comp Mater Sci2015;97:209-15

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

[114] Dai D,Wei X.Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys..Comp Mater Sci2020;175:109618

[115] Jain A,Hautier G.Commentary: The Materials Project: a Materials genome approach to accelerating materials innovation..APL Materials2013;1:011002

[116] Kim E,Saunders A,Ceder G.Materials synthesis insights from scientific literature via text extraction and machine learning..Chem Mater2017;29:9436-44

[117] Wu Y,Hautier G,Ceder G.First principles high throughput screening of oxynitrides for water-splitting photocatalysts..Energy Environ Sci2013;6:157-68

[118] Schmidt J,Borlido P,Botti S.Predicting the thermodynamic stability of solids combining density functional theory and machine learning..Chem Mater2017;29:5090-103

[119] Tao Q,Li M.Machine learning for perovskite materials design and discovery..npj Comput Mater2021;7:1-18

[120] Durodola J.Machine learning for design, phase transformation and mechanical properties of alloys..Prog Mater Sci2021;

[121] Lu Z,Liu X.Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses..npj Comput Mater2020;6:1-9

[122] Ihalage A.Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints..npj Comput Mater2021;7:1-12