Recent Advance of Machine Learning in Selecting New Materials

doi:10.6023/A22110446

Acta Chimica Sinica ›› 2023, Vol. 81 ›› Issue (2): 158-174.DOI: 10.6023/A22110446 Previous Articles Next Articles

Review

机器学习在新材料筛选方面的应用进展

戚兴怡, 胡耀峰, 王若愚, 杨雅清, 赵宇飞^*()

北京化工大学化学学院化工资源有效利用国家重点实验室北京 100029

投稿日期:2022-11-01 发布日期:2022-12-23
通讯作者: 赵宇飞

作者简介:

戚兴怡, 北京化工大学化学学院化学专业在读研究生. 2022年6月于北京化工大学化学学院应用化学专业获得学士学位.

胡耀峰, 北京化工大学化学学院化学专业在读本科生.

赵宇飞, 北京化工大学化学学院教授, 博士生导师. 入选中国科协“青年人才托举工程”计划, 2019年度国家自然科学基金优秀青年科学基金获得者. 工作围绕水滑石基二维插层材料的可控合成及精细结构表征, 面向高值精细化学品的光/电催化合成. 近年来以第一/通讯作者在Chem. Soc. Rev.、J. Am. Chem. Soc.、Angew. Chem.、Adv. Mater.、Chem、Joule、Ind. Eng. Chem. Res.等期刊上发表SCI收录论文30余篇; 累计SCI他引4500余次; 5篇入选ESI高被引论文, 已授权国家发明专利19项.

^†同等贡献

基金资助:
国家自然科学基金(21922801); 国家自然科学基金(22090032); 国家自然科学基金(22090030); 北京自然科学基金(2202036)

Recent Advance of Machine Learning in Selecting New Materials

Xingyi Qi, Yaofeng Hu, Ruoyu Wang, Yaqing Yang, Yufei Zhao()

State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029

Received:2022-11-01 Published:2022-12-23
Contact: Yufei Zhao
About author:
^†Contributed equally to this work
Supported by:
National Natural Science Foundation of China(21922801); National Natural Science Foundation of China(22090032); National Natural Science Foundation of China(22090030); Natural Science Foundation of Beijing(2202036)

The new material industry is the foundation of technological change in many related fields, and also the forerunner of the development of new energy, aerospace, electronic information and other high-tech industries. Traditional means cannot meet the development needs of modern society because of disadvantages such as high cost, low efficiency and long commercial cycle. In recent years, with the application of big data combined with artificial intelligence in a deeper degree, data-driven machine learning has made great progress in the design, screening and performance prediction of new materials, which has greatly promoted the development and application of new materials. In this review, the basic process of machine learning, the algorithms commonly used in materials science and the relevant materials database are summarized. This review focuses on the application of machine learning in different functions, as well as the performance prediction in the fields of catalyst materials, lithium-ion batteries, semiconductor materials and alloy materials, presenting the latest progress in materials development. Finally, machine learning in the application of new materials are analyzed and prospected.

Key words: machine learning, materials science, material genome, high throughput computing

Cite this article

Xingyi Qi, Yaofeng Hu, Ruoyu Wang, Yaqing Yang, Yufei Zhao. Recent Advance of Machine Learning in Selecting New Materials[J]. Acta Chimica Sinica, 2023, 81(2): 158-174.

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Fig. & Tab. 14

Figure 1. Accelerate the process of exploring new materials[3]

Figure 2. The results of ML researches which mainly include (a) underfitting, (b) correct fitting and (c) overfitting[19]

Figure 3. Example of a decision tree[22]

Figure 4. The working principle of the algorithm of ANN[19]

数据库名称	数据库简介	数据库网址	所属机构
Materials Project (MP)	MP提供了超过13万个无机化合物的结构信息以及性质, 存储了几乎所有的晶体材料和高通量材料的性质计算信息(包括能带态密度信息、电池材料的充放电曲线、相图等). 但MP中目前仅有50%的结构拥有电子结构信息.	https://materialsproject.org/	美国麻省理工学院等
The Open Quantum Materials Database (OQMD)	OQMD可以清楚地显示出材料的相图, 目前该数据库钙钛矿数据居多. OQMD可快速预测任意化合物的热力学性质并推测其稳定性. OQMD包含大量易于计算的合金材料, 但目前对离子化合物的相图刻画非常不全面, 且数据质量参差不齐.	http://oqmd.org/	美国西北大学
Aflowlib	目前AFLOWLIB拥有超过100万种的材料. 其包含成千上万的假想材料, 其中许多在实际中仅能存在极短时间. 该系统可以以已知的晶体结构为基础, 自动预测新的晶体结构.	http://aflowlib.org/	美国杜克大学
Atomly	Atomly拥有30余万种无机晶体材料的详细电子结构信息(能带、态密度等)以及近4万组热力学相关的相图. 其可迅速预测新材料热力学数据等, 进而指导新材料设计、预测合成难度等. 目前平台仍有大量材料的介电函数、声子谱等数据尚未进行分析计算.	https://atomly.net	中国科学院物理研究所
机数大材库(dcaiku)	机数大材库包含9448万分子基础化合物及相应性能数据, 同时也包括经理论计算获得的部分理论性质. 该数据库也整合了分子结构检索功能, 方便用户直观快捷的结构搜索, 与此同时还提供基于WebGL技术的分子3D模型展示, 用于实时交互式观察分子结构.	http://www.dcaiku.com/#/	中国合肥机数量子科技有限公司
非线性光学材料数据库	非线性光学材料数据库包含材料的化学式、空间群、ICSD编号, 用DFT方法计算的带隙、折射率、双折射、倍频系数等性质. 此外, 该数据库提供综合检索功能, 可通过元素组成、材料体系、材料性能等检索出满足要求的材料.	http://nlo.hbu.cn	中国河北大学

数据库名称	数据库简介	数据库网址	所属机构
Materials Project (MP)	MP提供了超过13万个无机化合物的结构信息以及性质, 存储了几乎所有的晶体材料和高通量材料的性质计算信息(包括能带态密度信息、电池材料的充放电曲线、相图等). 但MP中目前仅有50%的结构拥有电子结构信息.	https://materialsproject.org/	美国麻省理工学院等
The Open Quantum Materials Database (OQMD)	OQMD可以清楚地显示出材料的相图, 目前该数据库钙钛矿数据居多. OQMD可快速预测任意化合物的热力学性质并推测其稳定性. OQMD包含大量易于计算的合金材料, 但目前对离子化合物的相图刻画非常不全面, 且数据质量参差不齐.	http://oqmd.org/	美国西北大学
Aflowlib	目前AFLOWLIB拥有超过100万种的材料. 其包含成千上万的假想材料, 其中许多在实际中仅能存在极短时间. 该系统可以以已知的晶体结构为基础, 自动预测新的晶体结构.	http://aflowlib.org/	美国杜克大学
Atomly	Atomly拥有30余万种无机晶体材料的详细电子结构信息(能带、态密度等)以及近4万组热力学相关的相图. 其可迅速预测新材料热力学数据等, 进而指导新材料设计、预测合成难度等. 目前平台仍有大量材料的介电函数、声子谱等数据尚未进行分析计算.	https://atomly.net	中国科学院物理研究所
机数大材库(dcaiku)	机数大材库包含9448万分子基础化合物及相应性能数据, 同时也包括经理论计算获得的部分理论性质. 该数据库也整合了分子结构检索功能, 方便用户直观快捷的结构搜索, 与此同时还提供基于WebGL技术的分子3D模型展示, 用于实时交互式观察分子结构.	http://www.dcaiku.com/#/	中国合肥机数量子科技有限公司
非线性光学材料数据库	非线性光学材料数据库包含材料的化学式、空间群、ICSD编号, 用DFT方法计算的带隙、折射率、双折射、倍频系数等性质. 此外, 该数据库提供综合检索功能, 可通过元素组成、材料体系、材料性能等检索出满足要求的材料.	http://nlo.hbu.cn	中国河北大学

Table 1. Part of the material database platform introduction

Figure 5. High throughput screening results and experimental validations[33]. (a) A heatmap of the screened candidates for CLAS; (b) experimental oxygen capacity, recovery and usable capacity of the samples tested for CLAS; (c) a heatmap of the screened promising candidates for CL CO2/H2O splitting at 950 ℃; (d) experimental syngas yield and CO2 conversion of the samples tested for CL CO2 splitting

Figure 6. (a) The fitting results of test bandgaps EgPBE and predicted bandgaps EgML[38]; (b) scatter plots of tolerance factors against the bandgaps for the prediction dataset from trained ML model[38]; (c) the distribution of G0W0 band gaps corresponding to the data set for machine learning[39]; (d) primary feature importance calculated through bagging method[39]

Figure 7. Electron tomography for nanoscale matter. (A~C) Radial strain maps (A), fitted lattice parameters (B), and averaged radial strain values (C) of single-crystalline Pt particles. (D~F) Segmentation of a bacteriophage. (D) Image series and (E) density maps showing the segmentation of a bacteriophage (magenta) from the liquid background (cyan). (F) Tilt images of a phage particle. (G) 2D slice and 3D view of the polyamide membrane after reconstruction. (H) Individual crumples segmented from the 3D data set resolving various projections and interior void (purple)[46]

Figure 8. Tracking complex defect transformations on the surface. (a~d) STEM based tracking of defect transformations on graphene surface; (e~h) corresponding single defect structures extracted by applying FCN, LoG and graph representations to data in (a~d)[49]

Figure 9. Solvation free energy calculations with quantum mechanics/ molecular mechanics and machine learning models[55]

Figure 10. Schematic strategy of ML based on DFT for high-throughput stability engineering of halide perovskites[70]

Figure 11. DXXX-OPV3-XXXD system. (a) Chemical structure of the prototypical DXXX-OPV3-XXXD peptide monomer; (b) molecular simulation snapshot of a self-assembled pseudo-1D nanoaggregate formed by the spontaneous association of DVAA-OPV3-VAAD peptide monomers into a linear stack; (c) experimental transmission electron microscopy (TEM) image of self-assembled fibrils formed by DFFG-OPV3-GFFD peptides in an acidic environment; (d) illustration of the mapping from the DGAG-OPV3-GAGD all-atom structure to the coarse-grained representation at which the simulations in this work are conducted[79]

Figure 12. (a) Workflow of the machine learning-based segmentation; (b) comparison of conventional segmentation results and the machine-learning- assisted segmentation results for a few representative particles[91]

Figure 13. (a) A two-dimensional activity volcano plot for CO2 reduction[100]; (b) a two-dimensional selectivity volcano plot for CO2 reduction[100]; (c) loading magnitudes for the 14 descriptors obtained by factor analysis[102]; (d) results of the analysis of band gaps: decision tree constructed for band gaps[105]

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机器学习在新材料筛选方面的应用进展

Recent Advance of Machine Learning in Selecting New Materials

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