Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning

化学 晶体结构预测 人工智能 结晶学 晶体结构 计算机科学
作者
Jidon Jang,Geun Ho Gu,Juhwan Noh,Juhwan Kim,Yousung Jung
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
卷期号:142 (44): 18836-18843 被引量:94
标识
DOI:10.1021/jacs.0c07384
摘要

Predicting the synthesizability of inorganic materials is one of the major challenges in accelerated material discovery. A widely employed approximate approach is to consider the thermodynamic decomposition stability due to its simplicity of computing, but it is notorious for either producing too many candidates or missing important metastable materials. These results, however, are not unexcepted since the synthesizability is a complex phenomenon, and the thermodynamic stability is just one contributor. Here, we suggest a machine-learning model to quantify the probability of synthesis based on the partially supervised learning of materials database. We adapted the positive and unlabeled machine learning (PU learning) by implementing the graph convolutional neural network as a classifier in which the model outputs crystal-likeness scores (CLscore). The model shows 87.4% true positive (CLscore > 0.5) prediction accuracy for the test set of experimentally reported cases (9356 materials) in the Materials Project. We further validated the model by predicting the synthesizability of newly reported experimental materials in the last 5 years (2015-2019) with an 86.2% true positive rate using the model trained with the database as of the end of year 2014. Our analysis shows that our model captures the structural motif for synthesizability beyond what is possible by Ehull. We find that 71 materials among the top 100 high-scoring virtual materials have indeed been previously synthesized in the literature. With the proposed data-driven metric of the crystal-likeness score, high-throughput virtual screenings and generative models can benefit significantly by effectively reducing the chemical space that needs to be explored experimentally in the future toward more rational materials design.
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

祝大家在新的一年里科研腾飞
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
AUGKING27完成签到 ,获得积分10
刚刚
Nan发布了新的文献求助10
1秒前
1秒前
1秒前
李健应助hhhhhh采纳,获得10
2秒前
魏师完成签到,获得积分10
2秒前
烟花应助不做大哥好多年采纳,获得10
3秒前
胡桃夹子发布了新的文献求助10
4秒前
一洼清泉发布了新的文献求助10
4秒前
Hello应助张爱学采纳,获得10
5秒前
爆米花应助图苏采纳,获得10
5秒前
无花果应助权威采纳,获得10
6秒前
bkagyin应助小夏咕噜采纳,获得10
6秒前
华仔应助1484采纳,获得10
7秒前
8秒前
Jasper应助听风采纳,获得10
8秒前
科研通AI2S应助雪菲菲采纳,获得10
10秒前
10秒前
打打应助自由秋荷采纳,获得10
11秒前
桐桐应助Lllll采纳,获得10
11秒前
黄钺完成签到,获得积分10
11秒前
bkagyin应助Viviiviii采纳,获得30
12秒前
阳光完成签到,获得积分10
13秒前
ju龙哥发布了新的文献求助10
13秒前
林慕然2023完成签到,获得积分10
13秒前
14秒前
Phoebe1996发布了新的文献求助10
15秒前
王一博应助涵霸天采纳,获得10
15秒前
15秒前
wangx0421发布了新的文献求助10
15秒前
嗯哼举报亦玉求助涉嫌违规
16秒前
16秒前
帅明完成签到 ,获得积分10
17秒前
17秒前
stories完成签到,获得积分10
17秒前
moonlight完成签到,获得积分10
17秒前
八乙基环辛四烯完成签到,获得积分10
18秒前
qll关闭了qll文献求助
18秒前
刚少kk完成签到,获得积分10
18秒前
19秒前
高分求助中
Востребованный временем 2500
诺贝尔奖与生命科学 2000
Les Mantodea de Guyane 1000
Aspects of Babylonian celestial divination: the lunar eclipse tablets of Enūma Anu Enlil 1000
Very-high-order BVD Schemes Using β-variable THINC Method 910
The Three Stars Each: The Astrolabes and Related Texts 500
Separation and Purification of Oligochitosan Based on Precipitation with Bis(2-ethylhexyl) Phosphate Anion, Re-Dissolution, and Re-Precipitation as the Hydrochloride Salt 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 物理化学 催化作用 细胞生物学 免疫学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3381813
求助须知:如何正确求助?哪些是违规求助? 2996611
关于积分的说明 8769784
捐赠科研通 2681921
什么是DOI,文献DOI怎么找? 1468730
科研通“疑难数据库(出版商)”最低求助积分说明 679119
邀请新用户注册赠送积分活动 671210