摘要
新材料的发现是推动现代科学发展与技术革新的源动力之一,是当前促进经济发展与解决环境问题的迫切需求.传统的材料研发基于试错法,效率低且成本高.大量实验与计算模拟产生的数据为新材料的研发提供了新契机.基于这些数据,机器学习最近在材料性能预测、新材料的发现与设计等领域取得了很大进展.譬如基于材料项目(materials project)数据库对钙钛矿材料的统计分类、结合高通量计算对双钙钛矿卤化物材料稳定性的预测,以及金属间化合物电催化剂的设计与筛选等.除了基于隐式模型的预测,机器学习也可以用来发现具有物理可解释性的显式描述符,从而加速新材料的发现.
With rapid development of economy and society, the excessive demand for resources has caused the imbalance of ecological environment. It is therefore urgent to develop new functional materials, especially energy conversion materials,to solve the scientific and engineering problems in the field of resources and environment. However, the research and development of materials was traditionally based on inefficient trial-and-error experiments. Although state-of-the-art approach such as density functional theory(DFT) is able to simulate materials properties, the calculations of high temperature, high pressure and strong magnetic field environment, as well as the selection of strong and weak correlation system between electrons and interaction potential between atoms are still unsatisfactory.Huge amount of data produced by experiments and simulations provides databases for machine learning. Combining the theory of probability and statistics algorithm, machine learning has recently made much progress in the new material discovery and design, the prediction of material performance and application and other purposes ranged from the macroscopic to the microscopic scale, such as the statistics classification of perovskite materials, the stability prediction of perovskite materials based on high-throughput computing and intermetallic compound electrocatalysts design and selection, etc. Meanwhile, developing a physically interpretable descriptor that captures the trend of materials properties is a critical goal of data-driven science. Machine learning has been applied in the field of materials science and engineering,exhibiting a different perspective from traditional approaches. In this paper, recent progress of materials design in photovoltaics, electrocatalytic and performance evaluation of energy storage batteries are reviewed. In those efforts,machine learning aims to discover the relationships among compositional and structural features and functionality in complex systems of materials.Machine learning is a data-driven
作者
孙中体
李珍珠
程观剑
徐其琛
侯柱锋
尹万健
Zhongti Sun;Zhenzhu Li;Guanjian Cheng;Qichen Xu;Zhufeng Hou;Wanjian Yin(Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province,Soochow Institute for Energy and Materials Innovations(SIEMIS),College of Energy,Soochow University,Suzhou 215006,China;State Key Laboratory of Structural Chemistry,Fujian Institute of Research on the Structure of Matter,Chinese Academy of Sciences,Fuzhou 350002,China)
出处
《科学通报》
EI
CAS
CSCD
北大核心
2019年第32期3270-3275,共6页
Chinese Science Bulletin
基金
国家重点研发计划(2016YFB0700700)
国家自然科学基金(11674237,11974257,51602211)资助
关键词
机器学习
材料设计
能源转换
描述符
machine learning
material design
energy conversion
descriptor
