Machine Learning in Materials Chemistry: An Invitation

人工智能 机器学习 计算机科学 现状 化学 市场经济 经济
作者
Daniel M. Packwood,Linh Thi Hoai Nguyen,Pierluigi Cesana,Guoxi Zhang,Aleksandar Staykov,Yasuhide Fukumoto,Đình Hòa Nguyễn
出处
期刊:Machine learning with applications [Elsevier]
卷期号:8: 100265-100265 被引量:30
标识
DOI:10.1016/j.mlwa.2022.100265
摘要

Materials chemistry is being profoundly influenced by the uptake of machine learning methodologies. Machine learning techniques, in combination with established techniques from computational physics, promise to accelerate the discovery of new materials by elucidating complex structure–property relationships from massive material databases. Despite exciting possibilities, further methodological developments call for a greater synergism between materials chemists, physicists, and engineers on one side, with computer science and math majors on the other. In this review, we provide a non-exhaustive account of machine learning in materials chemistry for computer scientists and applied mathematicians, with an emphasis on molecule datasets and materials chemistry problems. The first part of this review provides a tutorial on how to prepare such datasets for subsequent model building, with an emphasis on the construction of feature vectors. We also provide a self-contained introduction to density functional theory, a method from computational physics which is widely used to generate datasets and compute response variables. The second part reviews two machine learning methodologies which represent the status quo in materials chemistry at present – kernelized machine learning and Bayesian machine learning – and discusses their application to real datasets. In the third part of the review, we introduce some emerging machine learning techniques which have not been widely adopted by materials scientists and therefore present potential avenues for computer science and applied math majors. In the final concluding section, we discuss some recent machine learning-based approaches to real materials discovery problems and speculate on some promising future directions.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
宋子墨完成签到,获得积分20
刚刚
1秒前
小璐璐呀发布了新的文献求助10
1秒前
2秒前
小新应助song采纳,获得10
3秒前
积极的爆米花完成签到,获得积分10
3秒前
meng完成签到 ,获得积分10
3秒前
3秒前
3秒前
4秒前
4秒前
5秒前
5秒前
逆旅发布了新的文献求助10
6秒前
8R60d8应助小超人吼吼采纳,获得10
6秒前
JamesPei应助宣兰采纳,获得10
6秒前
7秒前
7秒前
oookkay发布了新的文献求助10
8秒前
陈凝景完成签到,获得积分20
8秒前
咪咪虾条发布了新的文献求助10
8秒前
sff完成签到,获得积分10
9秒前
一二完成签到,获得积分10
9秒前
NexusExplorer应助keroro采纳,获得10
10秒前
清风徐来发布了新的文献求助10
10秒前
11秒前
11秒前
TAboo发布了新的文献求助10
11秒前
DJ发布了新的文献求助10
11秒前
orixero应助sorawing采纳,获得10
11秒前
顾矜应助DE2022采纳,获得10
12秒前
科研迪迦奥特曼完成签到,获得积分10
12秒前
12秒前
le000000发布了新的文献求助30
13秒前
秋雅发布了新的文献求助10
13秒前
我是老大应助逆旅采纳,获得10
13秒前
14秒前
14秒前
15秒前
15秒前
高分求助中
歯科矯正学 第7版(或第5版) 1004
Semiconductor Process Reliability in Practice 1000
Smart but Scattered: The Revolutionary Executive Skills Approach to Helping Kids Reach Their Potential (第二版) 1000
GROUP-THEORY AND POLARIZATION ALGEBRA 500
Mesopotamian divination texts : conversing with the gods : sources from the first millennium BCE 500
Days of Transition. The Parsi Death Rituals(2011) 500
The Heath Anthology of American Literature: Early Nineteenth Century 1800 - 1865 Vol. B 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3233445
求助须知:如何正确求助?哪些是违规求助? 2879969
关于积分的说明 8213423
捐赠科研通 2547415
什么是DOI,文献DOI怎么找? 1376927
科研通“疑难数据库(出版商)”最低求助积分说明 647713
邀请新用户注册赠送积分活动 623150