Abstract
Finding novel materials in an accelerated, yet cost effective manner that is not dependent on trial and error is one of the central goals of the U.S. Materials Genome Initiative. Learning from data is an attracting tool to accelerate the discovery of new materials. However, in addition to data, a distinguishing aspect of materials science is that there exists a substantial body of knowledge in the form of phenomenological models and physical theories. Here we focused on combining the informatics techniques and materials knowledge to further accelerated search for new materials with targeted properties. Two case studies include 1) using results from Landau–Devonshire theory to guide experiments in the design of new lead-free piezoelectrics with better temperature reliability; 2) using physical understanding to isolate possible global minimums of the search space to achieve a ferroelectric material with higher energy storage density using as few experiments as possible. Our framework may offer the opportunity to significantly reduce the number of costly and time-consuming experiments.
Keywords: Materials informatics; Machine learning; Bayesian learning; Adaptive design
References:
1Dezhen Xue, Prasanna V. Balachandran, Ruihao Yuan, Tao Hu, Xiaoning Qian, Edward R. Dougherty, Turab Lookman., PNAS, 2016; 113: 13301-13306.
2Dezhen Xue, Prasanna V. Balachandran, John Hogden, James Theiler, Deqing Xue, Turab Lookman., Nature Communications. 2016; 7:11241.
3Dezhen Xue, Prasanna V. Balachandran, Haijun Wu, Ruihao Yuan, Yumei Zhou, Xiangdong Ding, Jun Sun, Turab Lookman, Applied Physics Letters, 2017; 111: 032907.
4Dezhen Xue, Deqing Xue, Ruihao Yuan, Yumei Zhou, Prasanna V. Balachandran, Xiangdong Ding, Jun Sun, Turab Lookman，Acta Materialia, 2017; 125: 532-541.

个人简历：
薛德祯，西安交通大学材料科学与工程学院、金属材料强度国家重点实验室副教授。博士毕业于西安交通大学，获陕西省优秀博士论文；获得美国洛斯阿拉莫斯国家实验室院长博士后（director funded fellow）资助，进行三年博士后研究。主要研究方向是材料信息学，主要利用机器学习技术，研究缺陷对结构相变（铁电相变、马氏体相变等）的影响规律，实现铁性智能材料的高性能化，致力于材料学与信息学两个学科交叉领域的研究。迄今在Nat. Comm.，PNAS，Phys. Rev. Lett.，Phys. Rev. B.，Acta Mater.等期刊上发表论文56篇。相关的研究工作受到了国内外同行的关注，被Nature China，MRS Bulletin 等杂志专题评论。