Rongmei Liu, Qiuping Zhao, Wenyong Dong, Dan Guo, Zhanlei Shen, Yi Li, Wanliang Zhang, Dongfang Zhu, Jingbao Zhang, Junwen Bai, Ruizhe Ren, Mingyue Zhen, Jiajia Zhang, Jinxin Cui, Xinran Li, Yudong Miao
Assessing public health service capability of primary healthcare personnel: a large-scale survey in Henan Province, China
BMC health services research
manipulation crystallographic defects 2htransition metal dichalcogenides 2htmdcs whether pre postsynthesis garnered significant interest recently holds promise tuning thermal chemical electronic properties materials however desirable improvements often come cost deteriorated elastic inelastic properties may lead serious concerns considering mechanical reliability issues therefore persistent efforts needed explore effects energetically favorable vacancies mechanical properties 2d tmdcs effective tuning material properties versatile applications context machine learning models trained data based molecular models provide fast efficient material models also unearth crucial structureproperty relations however efforts early stage development study machine learning deep learning techniques used analyze mechanical properties 2d transition metal dichalcogenides tmdcs pristine defect forms goal predict failure stress strain failure strength based chirality strain various crystallographic defects considered extensive molecular dynamics simulations performed xgboost densely connected neural network densenet algorithms used make accurate stateoftheart predictions comparative evaluation shapley value analysis models presented improve interpretability