Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning

doi:10.1021/acsami.9b14530

2019-11-06

101

PRE

Gabriel R. Schleder

Carlos Mera Acosta

A. Fazzio *

DOI：10.1021/acsami.9b14530

原文链接

原文求助

摘要

En 中文

The increasing interest and research on two-dimensional (2D) materials has not yet translated into a reality of diverse materials applications. To go beyond graphene and transition metal dichalcogenides for several applications, suitable candidates with desirable properties must be proposed. Here we use machine learning techniques to identify thermodynamically stable 2D materials, which is the first essential requirement for any application. According to the formation energy and energy above the convex hull, we classify materials as having low, medium, or high stability. The proposed approach enables the stability evaluation of novel 2D compounds for further detailed investigation of promising candidates, using only composition properties and structural symmetry, without the need for information about atomic positions. We demonstrate the usefulness of the model generating more than a thousand novel compounds, corroborating with DFT calculations the classification for five of these materials. To illustrate the applicability of the stable materials, we then perform a screening of electronic materials suitable for photoelectrocatalytic water splitting, identifying the potential candidate Sn2SeTe generated by our model, and also PbTe, both not yet reported for this application.

Keyword:

two-dimensional materials

machine learning

high throughput screening

big data

density functional theory (DFT)

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

ACS Applied Materials and Interfaces

IF:

8.2

论文数:

6.1W

被引数:

38.7W

机构

暂无机构信息

引用论文

Synthesis and structure of free-standing germanium quantum dots and their application in live cell imaging

RSC Advances

IF0

2015-01-01

OAAI

Ali Karatutlu; Mingying Song; Ann P. Wheeler; Osman Ersoy; William R. Little; Yuanpeng Zhang; Pascal Puech; Filippo S. Boi; Zofia Luklinska; Andrei V. Sapelkin

Atomic-position independent descriptor for machine learning of material properties 用于材料属性机器学习的原子位置独立描述符

PHYSICAL REVIEW B

IF3.7

2018-12-20

OAAI

Jain, Ankit; Bligaard, Thomas

Heat transfer enhancement in fin-tube heat exchangers by winglet type vortex generators 翼片式涡流发生器在翅片管换热器中的传热增强

International Journal of Heat and Mass Transfer

IF0

1994-01-01

PREAI

G. Biswas; N.K. Mitra; M. Fiebig