The past decade has seen a sharp increase in machine learning(ML)applications in scientific research.This review introduces the basic constituents of ML,including databases,features,and algorithms,and highlights a few...The past decade has seen a sharp increase in machine learning(ML)applications in scientific research.This review introduces the basic constituents of ML,including databases,features,and algorithms,and highlights a few important achievements in chemistry that have been aided by ML techniques.The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations.Important two-dimensional(2D)and three-dimensional(3D)features representing the chemical environment of molecules and solids are briefly introduced.Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios.Three important fields of ML in chemistry are discussed:(1)retrosynthesis,in which ML predicts the likely routes of organic synthesis;(2)atomic simulations,which utilize the ML potential to accelerate potential energy surface sampling;and(3)heterogeneous catalysis,in which ML assists in various aspects of catalytic design,ranging from synthetic condition optimization to reaction mechanism exploration.Finally,a prospect on future ML applications is provided.展开更多
LASP(large-scale atomistic simulation with neural network potential)software developed by our group since 2018 is a powerful platform(www.lasphub.com)for performing atomic simulation of complex materials.The software ...LASP(large-scale atomistic simulation with neural network potential)software developed by our group since 2018 is a powerful platform(www.lasphub.com)for performing atomic simulation of complex materials.The software integrates the neural network(NN)potential technique with the global potential energy surface exploration method,and thus can be utilized widely for structure prediction and reaction mechanism exploration.Here we introduce our recent update on the LASP program version 3.0,focusing on the new functionalities including the advanced neuralnetwork training based on the multi-network framework,the newly-introduced S^(7) and S^(8) power type structure descriptor(PTSD).These new functionalities are designed to further improve the accuracy of potentials and accelerate the neural network training for multipleelement systems.Taking Cu-C-H-O neural network potential and a heterogeneous catalytic model as the example,we show that these new functionalities can accelerate the training of multi-element neural network potential by using the existing single-network potential as the input.The obtained double-network potential Cu CHO is robust in simulation and the introduction of S^(7) and S^(8) PTSDs can reduce the root-mean-square errors of energy by a factor of two.展开更多
基金financial support from the National Key Research and Development Program of China(2018YFA0208600)the National Natural Science Foundation of China(12188101,22033003,91945301,91745201,92145302,22122301,and 92061112)the Tencent Foundation for XPLORER PRIZE,and Fundamental Research Funds for the Central Universities(20720220011)。
文摘The past decade has seen a sharp increase in machine learning(ML)applications in scientific research.This review introduces the basic constituents of ML,including databases,features,and algorithms,and highlights a few important achievements in chemistry that have been aided by ML techniques.The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations.Important two-dimensional(2D)and three-dimensional(3D)features representing the chemical environment of molecules and solids are briefly introduced.Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios.Three important fields of ML in chemistry are discussed:(1)retrosynthesis,in which ML predicts the likely routes of organic synthesis;(2)atomic simulations,which utilize the ML potential to accelerate potential energy surface sampling;and(3)heterogeneous catalysis,in which ML assists in various aspects of catalytic design,ranging from synthetic condition optimization to reaction mechanism exploration.Finally,a prospect on future ML applications is provided.
基金supported by the National Key Research and Development Program of China (No.2018YFA0208600)the National Natural Science Foundation of China (No.91945301, No.22033003, No.92061112, No.22122301, and No.91745201)
文摘LASP(large-scale atomistic simulation with neural network potential)software developed by our group since 2018 is a powerful platform(www.lasphub.com)for performing atomic simulation of complex materials.The software integrates the neural network(NN)potential technique with the global potential energy surface exploration method,and thus can be utilized widely for structure prediction and reaction mechanism exploration.Here we introduce our recent update on the LASP program version 3.0,focusing on the new functionalities including the advanced neuralnetwork training based on the multi-network framework,the newly-introduced S^(7) and S^(8) power type structure descriptor(PTSD).These new functionalities are designed to further improve the accuracy of potentials and accelerate the neural network training for multipleelement systems.Taking Cu-C-H-O neural network potential and a heterogeneous catalytic model as the example,we show that these new functionalities can accelerate the training of multi-element neural network potential by using the existing single-network potential as the input.The obtained double-network potential Cu CHO is robust in simulation and the introduction of S^(7) and S^(8) PTSDs can reduce the root-mean-square errors of energy by a factor of two.
