计算机科学
航程(航空)
操作员(生物学)
多样性(控制论)
排列(音乐)
独立性(概率论)
鉴定(生物学)
电子结构
特征(语言学)
功能(生物学)
特征选择
工作(物理)
机器学习
人工智能
统计物理学
材料科学
数学
机械工程
物理
化学
工程类
凝聚态物理
生物化学
进化生物学
转录因子
统计
植物
基因
哲学
抑制因子
语言学
生物
复合材料
声学
作者
Eswar Ramanathan,Chandra Chowdhury
标识
DOI:10.1002/cphc.202300308
摘要
Abstract The growing number of studies and interest in two‐dimensional (2D) materials has not yet resulted in a wide range of material applications. This is a result of difficulties in getting the properties, which are often determined through numerical experiments or through first‐principles predictions, both of which require lots of time and resources. Here we provide a general machine learning (ML) model that works incredibly well as a predictor for a variety of electronic and structural properties such as band gap, fermi level, work function, total energy and area of unit cell for a wide range of 2D materials derived from the Computational 2D Materials Database (C2DB). Our predicted model for classification of samples works extraordinarily well and gives an accuracy of around 99 %. We are able to successfully decrease the number of studied features by employing a strict permutation‐based feature selection method along with the sure independence screening and sparsifying operator (SISSO), which further supports the design recommendations for the identification of novel 2D materials with the desired properties.
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