A Review of Material Research and Development Methods Based on Computer Technology

doi:10.11871/jfdc.issn.2096-742X.2021.02.014

Abstract

Abstract:

[Objective] With the advent of the era of big data and the proposal of the Materials Genome Initiative, the research and development mode of materials has experienced a fundamental transformation from "trial and error" to the fourth paradigm of material science. This paper aims to summarize the computer technologies involved in this new materials research mode. [Methods] Based on the literatures on material computation and data-driven techniques in recent years, the paper summarizes and discusses the application of computer technologies in material research and development. [Results] The innovation mode marked by “data + artificial intelligence” and computational material science can improve the efficiency of material research significantly, and can explore the relationship between various material parameters and performance from multi-dimensional perspectives. [Conclusions] The application of computer technologies is of great importance to material research in accelerating progress, improving material understandings, and key technology breakthroughs.

Key words: material informatics, machine learning, data driven

GUO Jialong,WANG Zongguo,WANG Yangang,ZHAO Xushan,SU Yanjing,LIU Zhiwei. A Review of Material Research and Development Methods Based on Computer Technology[J]. Frontiers of Data and Computing, 2021, 3(2): 120-132.

Figures/Tables 3

References 75

[1]	AGRAWAL A, CHOUDHARY A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science[J]. APL Materials, 2016,4(5):053208. doi: 10.1063/1.4946894
[2]	SCHLEDER G R, PADILHA A C M, ACOSTA C M, et al. From DFT to Machine Learning: recent approaches to Materials Science - a review[J]. Journal of Physics: Materials, 2019,2(3).
[3]	ONG S P, RICHARDS W D, JAIN A, et al. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis[J]. Computational Materials Science, 2013,68:314-319. doi: 10.1016/j.commatsci.2012.10.028
[4]	MATHEW K, MONTOYA J H, FAGHANINIA A, et al. Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows[J]. Computational Materials Science, 2017,139:140-152. doi: 10.1016/j.commatsci.2017.07.030
[5]	ASE-Developers. Atomic Simulation Environment[EB/OL]. [2020-11-1]. https://wiki.fysik.dtu.dk/ase/about.html.
[6]	PIZZI G, CEPELLOTTI A, SABATINI R, et al. AiiDA: automated interactive infrastructure and database for computational science[J]. Computational Materials Science, 2016,111:218-230. doi: 10.1016/j.commatsci.2015.09.013
[7]	ONG S P, CHOLIA S, JAIN A, et al. The Materials Applica-tion Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles[J]. Computational Materials Science, 2015,97:209-215. doi: 10.1016/j.commatsci.2014.10.037
[8]	JAIN A, ONG S P, CHEN W, et al. FireWorks: a dynamic workflow system designed for high-throughput applications[J]. Concurrency and Computation: Practice and Experi-ence, 2015,27(17):5037-5059.
[9]	CALDERON C E, PLATA J J, TOHER C, et al. The AFLOW standard for high-throughput materials science calcula-tions[J]. Computational Materials Science, 2015,108:233-238. doi: 10.1016/j.commatsci.2015.07.019
[10]	CURTAROLO S, SETYAWAN W, HART G L W, et al. AFLOW: An automatic framework for high-throughput materials discovery[J]. Computational Materials Science, 2012,58:218-226. doi: 10.1016/j.commatsci.2012.02.005
[11]	YANG X, WANG Z, ZHAO X, et al. MatCloud: A high-throughput computational infrastructure for integrated management of materials simulation, data and resources[J]. Computational Materials Science, 2018,146:319-333. doi: 10.1016/j.commatsci.2018.01.039
[12]	杨炯, 席丽丽, 骆军, et al. 上海大学第一性原理高通量计算平台与材料应用案例[C]. 北京: 北京科技大学, 2018.
[13]	Mayeul d'Avezac. Pylada [EB/OL]. [2020-11-1]. http://pylada.github.io/pylada/about.html.
[14]	MATHEW K, SINGH A K, GABRIEL J J, et al. MPInter-faces: A Materials Project based Python tool for high-throughput computational screening of interfacial systems[J]. Computational Materials Science, 2016,122:183-190. doi: 10.1016/j.commatsci.2016.05.020
[15]	AGRAWAL A, CHOUDHARY A. Deep materials informatics: Applications of deep learning in materials science[J]. MRS Communications, 2019,9(3):779-792. doi: 10.1557/mrc.2019.73
[16]	NUNEZ M. Exploring materials band structure space with unsupervised machine learning[J]. Computational Materials Science, 2019,158(15 February 2019):117-123. doi: 10.1016/j.commatsci.2018.11.002
[17]	ABBOD M F, LINKENS D A, ZHU Q, et al. Physically based and neuro-fuzzy hybrid modelling of thermome-chanical processing of aluminium alloys[J]. Materials Science and Engineering: A, 2002,333(1):397-408. doi: 10.1016/S0921-5093(01)01873-1
[18]	FANG S F, WANG M P, SONG M. An approach for the aging process optimization of Al-Zn-Mg-Cu series alloys[J]. Materials & Design, 2009,30(7):2460-2467. doi: 10.1016/j.matdes.2008.10.008
[19]	HAN Y F, ZENG W D, SHU Y, et al. Prediction of the mechanical properties of forged Ti-10V-2Fe-3Al titanium alloy using FNN[J]. Computational Materials Science, 2011,50(3):1009-1015. doi: 10.1016/j.commatsci.2010.10.040
[20]	GOSSETT E, TOHER C, OSES C, et al. AFLOW-ML: A RESTful API for machine-learning predictions of materials properties[J]. Computational Materials Science, 2018,152:134-145. doi: 10.1016/j.commatsci.2018.03.075
[21]	WARD L, DUNN A, FAGHANINIA A, et al. Matminer: An open source toolkit for materials data mining[J]. Computational Materials Science, 2018,152:60-69. doi: 10.1016/j.commatsci.2018.05.018
[22]	UENO T, RHONE T D, HOU Z, et al. COMBO: An efficient Bayesian optimization library for materials science[J]. Materials Discovery, 2016,4:18-21. doi: 10.1016/j.md.2016.04.001
[23]	KOLB B, LENTZ L C, KOLPAK A M. Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods[J]. Sci Rep, 2017,7(1):1192. doi: 10.1038/s41598-017-01251-z
[24]	ARTRITH N, URBAN A. An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2[J]. Computational Materials Science, 2016,114:135-150. doi: 10.1016/j.commatsci.2015.11.047
[25]	ARTRITH N, URBAN A, CEDER G. Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species[J]. Physical Review B, 2017,96(1):014112. doi: 10.1103/PhysRevB.96.014112
[26]	COOPER A M, KAESTNER J, URBAN A, et al. Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide[J]. Npj Computational Materials, 2020,6(1).
[27]	WANG H, ZHANG L, HAN J, et al. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics[J]. Computer Physics Communications, 2018,228:178-184. doi: 10.1016/j.cpc.2018.03.016
[28]	KHORSHIDI A, PETERSON A A. Amp: A modular approach to machine learning in atomistic simulations[J]. Computer Physics Communications, 2016,207:310-324. doi: 10.1016/j.cpc.2016.05.010
[29]	YAO K, HERR J E, TOTH D W, et al. The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics[J]. Chem Sci, 2018,9(8):2261-2269. doi: 10.1039/C7SC04934J
[30]	DENG Y, ZENG H, JIANG Y, et al. Ridge regression for predicting elastic moduli and hardness of calcium aluminosilicate glasses[J]. Materials Research Express, 2018,5(3):035205. doi: 10.1088/2053-1591/aab723
[31]	Liu L, Yan Y, Li J, et al. the Proceedings - 5th International Conference on Frontier of Computer Science and Technology: Predicting the Formation of Microporous Aluminophosphate AlPO4-5 Using Ridge Regression[C]. Changchun: IEEE, 2010: 483-488.
[32]	Wu Y R, Li H P, Gan X S. SVM Regression Modeling Based on Properties of Engineering Materials with PLS Feature Extraction[J]. Advanced Materials Research, 2014,848:122-125. doi: 10.4028/www.scientific.net/AMR.848
[33]	VAROL T, CANAKCI A, OZSAHIN S. Artificial neural network modeling to effect of reinforcement properties on the physical and mechanical properties of Al2024-B4C composites produced by powder metallurgy[J]. Composites Part B-Engineering, 2013,54(8):224-233. doi: 10.1016/j.compositesb.2013.05.015
[34]	PILANIA G, MANNODI-KANAKKITHODI A, UBER-UAGAI B P, et al. Machine learning bandgaps of double perovskites[J]. Scientific Reports, 2016,6(1).
[35]	ROEKEGHEM A, CARRETE J, OSES C, et al. High-throughput computation of thermal conductivity of high-temperature solid phases: The case of oxide and fluoride perovskites[J]. Physical Review X, 2016,6(4):041061. doi: 10.1103/PhysRevX.6.041061
[36]	GU G H, NOH J, KIM I, et al. Machine learning for renewable energy materials[J]. Journal of Materials Chemistry A, 2019,7(29):17096-17117. doi: 10.1039/C9TA02356A
[37]	LIU Y, NIU C, WANG Z, et al. Machine learning in materials genome initiative: A review[J]. Journal of Materials Science and Technology, 2020,57:113-122. doi: 10.1016/j.jmst.2020.01.067
[38]	SIGMUND G, GHARASOO M, HUFFER T, et al. Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials[J]. Environ Sci Technol, 2020,54(7):4583-4591. doi: 10.1021/acs.est.9b06287
[39]	OUYANG R, CURTAROLO S, AHMETCIK E, et al. SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates[J]. 2018,2(8):083802.
[40]	BARTEL C J, SUTTON C, GOLDSMITH B R, et al. New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides[J]. Science Advances, 2019, 5(2):eaav0693. doi: 10.1126/sciadv.aav0693
[41]	OUYANG R. Exploiting Ionic Radii for Rational Design of Halide Perovskites[J]. Chemistry of Materials, 2019,32:595-604. doi: 10.1021/acs.chemmater.9b04472
[42]	Loftis C, Yuan K, Zhao Y, et al. Lattice Thermal Conductivity Prediction using Symbolic Regression and Machine Learning[J]. JPCA, 2021,125(1):435-450. doi: 10.1021/acs.jpca.0c08103
[43]	TAKAHASHI K, TANAKA Y. Material synjournal and design from first principle calculations and machine learning[J]. Computational Materials Science, 2016,112:364-367. doi: 10.1016/j.commatsci.2015.11.013
[44]	MEREDIG B, AGRAWAL A, KIRKLIN S, et al. Combinatorial screening for new materials in uncon-strained composition space with machine learning[J]. Physical Review B, 2014,89(9):094104. doi: 10.1103/PhysRevB.89.094104
[45]	RYAN K. Crystal Structure Prediction via Deep Learning[D]. Florida: ProQuest LLC, 2018.
[46]	TAGADE P M, ADIGA S P, PANDIAN S, et al. Attribute driven inverse materials design using deep learning Bayesian framework[J]. npj Computational Materials, 2019,5(1).
[47]	UMEHARA M, STEIN H S, GUEVARRA D, et al. Analyzing machine learning models to accelerate generation of fundamental materials insights[J]. npj Computational Materials, 2020,4(4):135-143.
[48]	KusnE A. GILAD, YU HESHAN, WU CHANGMING, et al. On-the-fly closed-loop materials discovery via Bayesian active learning[J]. Nature Communications, 2020,11(1).
[49]	ZHONG M, TRAN K, MIN Y, et al. Accelerated disco-very of CO2 electrocatalysts using active machine learning[J]. Nature, 2020,581(7807):178-183. doi: 10.1038/s41586-020-2242-8
[50]	YANG Z, AL-BAHRANI R, REID A C E, et al. proceed-ings of the 2019 International Joint Conference on Neural Networks: Deep learning based domain knowledge integration for small datasets: Illustrative applications in materials informatics[C]. Budapest: Institute of Electrical and Electronics Engineers Inc, 2019, 1-8.
[51]	IERACITANO C, PANTO F, MAMMONE N, et al. Toward an Automatic Classification of SEM Images of Nanomaterials via a Deep Learning Approach[M]. // Springer Science and Business Media Deutschland GmbH. 2020: 61-72.
[52]	LI X, LIU Z, CUI S, et al. Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning[J]. Computer Methods in Applied Mechanics and Engineering, 2019,347(APR. 15):735-753. doi: 10.1016/j.cma.2019.01.005
[53]	游洋, 杜婉, 李惟驹, 陈竞哲. 基于机器学习方法的二维材料带隙预测[J]. 上海大学学报(自然科学版), 2020,26(05):824-833.
[54]	李霞, 苏航, 陈晓玲, et al. 材料数据库的现状与发展趋势[J]. 中国冶金, 2007,17(6):4-8.
[55]	PRICE D. Guide to materials databases[J]. Materials World, 1993,1(7):418-422.
[56]	汪洪, 项晓东, 张澜庭. 数据+人工智能是材料基因工程的核心[J]. 科技导报, 2018,36(14):15-21.
[57]	BLAISZIK B, CHARD K, PRUYNE J, et al. The Materials Data Facility: Data Services to Advance Materials Science Research[J]. Jom, 2016,68(8):2045-2052. doi: 10.1007/s11837-016-2001-3
[58]	BLAISZIK B, WARD L, SCHWARTING M, et al. A data ecosystem to support machine learning in materials science[J]. Mrs Communications, 2019,9(4):1125-1133. doi: 10.1557/mrc.2019.118
[59]	GUNTER D, CHOLIA S, JAIN A, et al. Community Accessible Datastore of High-Throughput Calculations: Experiences from the Materials Project[M]. // 2012 SC Companion: High Performance Computing, Networking Storage and Analysis. Salt Lake: IEEE, 2012: 1244-1251.
[60]	JAIN A, ONG S P, HAUTIER G, et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation[J]. Apl Materials, 2013,1(1):011002. doi: 10.1063/1.4812323
[61]	SAAL J E, KIRKLIN S, AYKOL M, et al. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)[J]. Jom, 2013,65(11):1501-1509. doi: 10.1007/s11837-013-0755-4
[62]	CURTAROLO S, SETYAWAN W, WANG S, et al. AFLO-WLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations[J]. Computational Materials Science, 2012,58(none):227-235. doi: 10.1016/j.commatsci.2012.02.002
[63]	高志玉, 刘国权. 在线材料数据库进展与NIMS/MatWeb案例研究[J]. 材料工程, 2013, (11):89-96.
[64]	VILLARS P, CENZUAL K, GLADYSHEVSKII R, et al. Pauling File: Toward a Holistic View[M]. // Materials Informatics, 2019: 55-106.
[65]	YAMAZAKI M, XU Y, MURATA M, et al. proceedings of the BALTICA VII - Life Management and Maintenance for Power Plants: NIMS structural materials databases and cross search engine - MatNavi[C]. Helsinki: Technical Research Center of Finland, 2007.
[66]	HELLENBRANDT M. The Inorganic Crystal Structure Database (ICSD)—Present and Future[J]. Crystallo-graphy Reviews, 2004,10(1):17-22.
[67]	KIM M, SINGH S P, LEE J-W, et al. Identification of a narrow band red light-emitting phosphor using computa-tional screening of ICSD: Its synjournal and optical chara-cterization[J]. Journal of Alloys and Compounds, 2019,774:338-346. doi: 10.1016/j.jallcom.2018.09.370
[68]	WHITE P S, RODGERS J R, LE P Y. Crystmet: A database of the structures and powder patterns of metals and intermetallics[J]. Acta Crystallographica Section B: Structural Science, 2002,58(3 PART 1):343-348. doi: 10.1107/S0108768102002902
[69]	GRAŽULIS S, DAŠKEVI A, MERKYS A, et al. Crystallo-graphy Open Database (COD): An open-access collection of crystal structures and platform for world-wide collaboration[J]. Nucleic Acids Research, 2012,40(D1):D420-D7. doi: 10.1093/nar/gkr900
[70]	HACHMANN J, OLIVARES-AMAYA R, JINICH A, et al. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry - the Harvard Clean Energy Project[J]. Energy & Environmental Science, 2014,7(2):698-704.
[71]	PUCHALA B, TARCEA G, MARQUIS E A, et al. The Materials Commons: A Collaboration Platform and Information Repository for the Global Materials Community[J]. JOM, 2016,68(8):2035-2044. doi: 10.1007/s11837-016-1998-7
[72]	刘芳宁, 王越, 孙瑞侠. 材料数据库的现状与发展趋势[J]. 科技创新导报, 2018,15(34):149-151.
[73]	COUDERT F-X. Materials Databases: The Need for Open, Interoperable Databases with Standardized Data and Rich Metadata[J]. Advanced Theory and Simulations, 2019,2(11):1900131. doi: 10.1002/adts.v2.11
[74]	GLICK J. Ontologies and Databases - Knowledge Engineering for Materials Informatics[J]. Informatics for Materials Science and Engineering, 2013: 147-187.
[75]	DRAXL C, SCHEFFLER M. NOMAD: The FAIR concept for big data-driven materials science[J]. Mrs Bulletin, 2018,43(9):676-682. doi: 10.1557/mrs.2018.208