The core philosophy of Material Genome Initiative is the transition of the way of new material design from traditional"try-anderror"approach to the in-silico material design approach which employs intensive computing and material informatics. It aims to speed up discovery, development, production and deployment of new material two times faster than it is now. It means a culture shift of the material discovery, development and deployment:simulation and prediction first, followed by the experiment. This paper depicts how computational approach and informatics can discover new materials. A high throughput computational material platform and software framework, namely, MatCloud, is discussed.
[1] 田恬. 材料基因组工程——推动材料创新的引擎[J]. 科技导报, 2015, 33(10):3. Tian Tian. Material Genome Engineering-An engine to accelerate new material innovation[J]. Science & Technol——材料研发新模式[J]. 科技导报, 2015, 33(10):13-19. Wang Hong, Xiang Yong, Xiagn Xiaodong, et al. Materials genome en-ables research and development revolution[J]. Science & Technology Review, 2015, 33(10):13-19.
[3] Yang X, Wallom D, Waddington S, et al. Cloud computing in e-Sci-ence:Research challenges and opportunities[J]. The Journal of Super-computing, 2014, 70(1):408-464.
[4] 杨小渝. 加快材料基因组工程信息化基础设施的建设[J]. 科技导报, 2016, 34(8):13-14. Yang Xiaoyu. Accelerating the development of material genome initia-tive cyberinfrastructure[J]. Science & Technology Review 2016, 34(8):13-14.
[5] Hohenberg P, Kohn W. Inhomogeneous electron gas[J]. Physical Re-view, 1964, 136(3B):B864.
[6] Kohn W, Sham L J. Self-consistent equations including exchange and correlation effects[J]. Physical Review, 1965, 140(4):1133-1138.
[7] Pople J A. Nobel lecture:Quantum chemical models[J]. Reviews of Mod-ern Physics, 1999, 71(5):1267.
[8] Allen M P. Introduction to molecular dynamics simulation[J]. Computa-tional soft matter:from synthetic polymers to proteins, 2004, 23:1-28.
[9] Warshel A, Levitt M. Theoretical studies of enzymic reactions:Dielec-tric, electrostatic and steric stabilization of the carbonium ion in the re-action of lysozyme[J]. Journal of Molecular Biology, 1976, 103(2):227-249.
[10] Kaufman L, Bernstein H. Computer calculation of phase diagrams[M]. New York:Academic Press, 1970.
[11] Kang B, Ceder G. Battery materials for ultrafast charging and discharg-ing[J]. Nature, 2009, 458(7235):190-193.
[12] Hautier G, Jain A, Ong S P, et al. Phosphates as lithium-ion battery cathodes:An evaluation based on high-throughput ab initio calculation[J]. Chemistry of Materials. 2011, 23(15):3495-3508.
[13] Kang K, Meng Y S, Bréger J, et al. Electrodes with high power and high capacity for rechargeable lithium batteries[J]. Science, 2006, 311(5763):977-980.
[14] Curtarolo S, Morgan D, Ceder G. Accuracy of ab initio methods in pre-dicting the crystal structures of metals:A review of 80 binary alloys[J]. Calphad, 2005, 29(3):163-211.
[15] Raccuglia C, Elbert K, Adler P, et al. Machine-learning-assisted ma-terials discovery using failed experiments[J]. Nature, 2016, 533:73-76.
[16] Kristian S, Karsten W. Making the most of materials computations[J]. Science, 2016, 354(6309):180-181.
[17] Makishima A, Uo M, Inoue H. Improved expert system for materials design of glasses[C]//Proceedings of the Second International Confer-ence and Exhibition on Computer Applications to Materials and Molec-ular Science and Engineering, Yokohama, Japan:Computer Aided In-novation of New Materials II, 1992:22-25.
[18] Yasui I, Utsuno F. Material design of glasses based on database-IN-TERGLAD[C]//Proceedings of the Second International Conference and Exhibition on Computer Applications to Materials and Molecular Science and Engineering, Yokohama, Japan:Computer Aided Innova-tion of New Materials, 1993:1539-1544.
[19] Futagami T, Makishima A, Yasui I, et al. Expert system for materials Design of PTC Thermistors[C]//Proceedings of the Second Internation-al Conference and Exhibition on Computer Applications to Materials and Molecular Science and Engineering, Yokohama, Japan:Computer Aided Innovation of New Materials II, 1993:1565-1568.
[20] Ashby M F. Materials selection in mechanical design[M]. 2nd, Oxford:Butterworth-Heinemann, 1999.
[21] Balachandran P V, Broderick S R, Rajan K. Identifying the ‘inorganic gene’ for high-temperature piezoelectric perovskites through statistical learning[J]. Proceedings of Royal Society A:Mathematical, Physics, and Engineering Science, 2011, 467(2132):2271-2290.
[22] Fischer C C, Tibbetts K J, Morgan D, et al. Predicting crystal struc-ture by merging data mining with quantum mechanics[J]. Nature Mate-rials, 2006, 5(8):641-646.
[23] Saad Y, Gao D, Ngo T, et al. Data mining for materials:Computational experiments with AB compounds[J]. Physical Review B, 2012, 85(10):104104.
[24] Saal J E, Kirklin S, Aykol M, et al. Materials design and discovery with high-throughput density functional theory:The open quantum ma-terials database (OQMD)[J]. Journal of the Minerals, Metals & Materi-als Society, 2013, 65(11):1501-1509.
[25] Kirklin S, Meredig B, Wolverton C. High-throughput computational screening of new Li-Ion battery anode materials[J]. Advanced Energy Materials, 2013, 3(2):252-262.
[26] Saal J E, Wolverton C. Thermodynamic stability of Mg-based ternary long-period stacking ordered structures[J]. Acta Materialia, 2014, 68:325-338.
[27] Hu L H, Wang X J, Wong L H, et al. Combined first-principles calcu-lation and neural-network correction approach for heat of formation[J]. Journal of Chemical Physics, 2003, 119(22):11501-11507.
[28] Bligaard T, Johannesson G H, Ruban A V, et al. Pareto-optimal alloys[J]. Applied Physics Letters, 2003, 83(22):4527-4529.
[29] 王卓, 杨小渝, 郑宇飞, 等. 材料基因组框架下的材料集成设计及信息平台初探[J]. 科学通报, 2013, 58(35):3733-3742. Wang Zhuo, Yang Xiaoyu, Zheng Yufei, et al. Integrated materials de-sign and informatics platform within the materials genome framework[J]. Chinese Science Bulletin, 2013, 58(35):3733-3742.
