Machine-learning-assisted materials discovery using failed experiments

计算机科学 数据科学 人工智能
作者
Paul Raccuglia,Katherine C. Elbert,Philip Adler,Casey Falk,Malia B. Wenny,Aurelio Mollo,Mat­thias Zeller,Sorelle A. Friedler,Joshua Schrier,Alexander J. Norquist
出处
期刊:Nature [Nature Portfolio]
卷期号:533 (7601): 73-76 被引量:1432
标识
DOI:10.1038/nature17439
摘要

Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on 'dark' reactions--failed or unsuccessful hydrothermal syntheses--collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.
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
PDF的下载单位、IP信息已删除 (2025-6-4)

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
科目三应助Ilyas0525采纳,获得10
7秒前
DeepLearning发布了新的文献求助10
12秒前
Jasmineyfz完成签到 ,获得积分10
12秒前
14秒前
Ilyas0525完成签到,获得积分10
23秒前
Emma完成签到,获得积分10
26秒前
小木林完成签到 ,获得积分10
35秒前
朴素羊完成签到 ,获得积分10
37秒前
辛勤的喉完成签到 ,获得积分10
40秒前
yunt完成签到 ,获得积分10
40秒前
林利芳完成签到 ,获得积分0
42秒前
神勇的翠丝完成签到,获得积分10
42秒前
xiangwang完成签到 ,获得积分10
49秒前
Joan_89完成签到,获得积分10
52秒前
juliar完成签到 ,获得积分10
1分钟前
Akim应助DeepLearning采纳,获得10
1分钟前
迈克老狼完成签到 ,获得积分10
1分钟前
Kelevator完成签到,获得积分10
1分钟前
DeepLearning完成签到,获得积分20
1分钟前
1分钟前
DeepLearning发布了新的文献求助10
1分钟前
SOL完成签到 ,获得积分10
1分钟前
丝丢皮的完成签到 ,获得积分10
1分钟前
t铁核桃1985完成签到 ,获得积分10
1分钟前
丝丢皮得完成签到 ,获得积分10
1分钟前
爆米花应助DeepLearning采纳,获得10
1分钟前
铜豌豆完成签到 ,获得积分10
1分钟前
cheng完成签到,获得积分10
1分钟前
君看一叶舟完成签到 ,获得积分10
1分钟前
在水一方应助科研通管家采纳,获得10
1分钟前
1分钟前
Chuang完成签到 ,获得积分10
2分钟前
热心市民完成签到 ,获得积分10
2分钟前
吃不胖的魔芋丝完成签到 ,获得积分10
2分钟前
2分钟前
李东东完成签到 ,获得积分10
2分钟前
fsznc1完成签到 ,获得积分0
2分钟前
年轻的笙完成签到,获得积分10
2分钟前
嘻嘻完成签到 ,获得积分10
2分钟前
huiluowork完成签到 ,获得积分10
2分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 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 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990836
求助须知:如何正确求助?哪些是违规求助? 3532241
关于积分的说明 11256631
捐赠科研通 3271100
什么是DOI,文献DOI怎么找? 1805313
邀请新用户注册赠送积分活动 882302
科研通“疑难数据库(出版商)”最低求助积分说明 809236