当前位置: X-MOL 学术Catal. Rev. Sci. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Recent advances in knowledge discovery for heterogeneous catalysis using machine learning
Catalysis Reviews, Science and Engineering ( IF 9.3 ) Pub Date : 2020-06-02 , DOI: 10.1080/01614940.2020.1770402
M. Erdem Günay 1 , Ramazan Yıldırım 2
Affiliation  

ABSTRACT

The use of machine learning (ML) in catalysis has been significantly increased in recent years due to the astonishing developments in data processing technologies and the accumulation of a large amount of data in published literature and databases. The data generated in house or extracted from external sources have been analyzed using various ML techniques to see patterns, develop models for prediction and deduce heuristic rules for the future. This communication aims to review the works involving knowledge discovery in catalysis using ML techniques; the basic principles, common tools and implementation of ML in catalysis are also summarized.

Abbreviations: ANN: Artificial neural network; ASLA: Atomistic structure learning algorithm; CatApp: A web application heterogeneous catalysis; CSD: Cambridge Structural Database; co-pre: Co-precipitation; Cx: Fraction of curvature; DFT: Density functional theory; DT: Decision tree; ∆ECO: CO adsorption energy; Fx: Fraction of facets; MBTR: Many-body tensor representation; ML: Machine learning; MOF: Metal-organic framework; Nx: Number of atoms; PC: Polymerized complex; Rx: Radius; R2: Coefficient of determination; RMSE: Root mean square error; RSM: Response surface methodology; SG: Sol-gel; SISSO: Sure independence screening and sparsifying operator; SIMELS: Simplified molecular-input line-entry system; SOAP: Smooth overlap of atomic positions; SSR: Solid-state reaction; T: Temperature; t: Time; τ: Atomic deposition rate; WIPO: World Intellectual Property Organization; WOS: Web of Science; XANES: X-ray absorption near-edge structure



中文翻译:

使用机器学习进行多相催化的知识发现的最新进展

摘要

近年来,由于数据处理技术的惊人发展以及已出版文献和数据库中大量数据的积累,机器学习(ML)在催化中的使用已大大增加。已使用各种ML技术分析了内部生成的数据或从外部来源提取的数据,以查看模式,开发预测模型并推论未来的启发式规则。本交流旨在回顾涉及使用ML技术进行催化知识发现的工作;总结了ML在催化中的基本原理,常用工具和实现方法。

缩写: ANN:人工神经网络;ASLA:原子结构学习算法;CatApp:Web应用程序的非均相催化;CSD:剑桥结构数据库;共同沉淀:共同沉淀;C x:曲率的分数;DFT:密度泛函理论;DT:决策树;ΔE CO:CO吸附能量; F x:分面的分数;MBTR:多体张量表示;ML:机器学习;MOF:金属有机框架;N x:原子数;PC:聚合配合物;R x:半径;R 2:测定系数;RMSE:均方根误差;RSM:响应面方法;SG:溶胶凝胶;SISSO:确保独立性筛选和疏散操作员;SIMELS:简化的分子输入行输入系统;SOAP:原子位置的平滑重叠;SSR:固态反应;T:温度;t:时间;τ:原子沉积速率;WIPO:世界知识产权组织;WOS:Web of Science;XANES:X射线吸收近边缘结构

更新日期:2020-06-02
down
wechat
bug