Machine-learning-assistedmaterialsdiscovery usingfailedexperiments PaulRaccuglia 1 ,KatherineC.Elbert 1 ,PhilipD.F.Adler 1 ,CaseyFalk 1 ,MaliaB.Wenny 1 ,AurelioMollo 1 ,MatthiasZeller 2 , SorelleA.Friedler 1 ,JoshuaSchrier 1 &AlexanderJ.Norquist ...
et al. Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors. Nat Commun 14, 4607 (2023). https://doi.org/10.1038/s41467-023-40282-1 Download citation Received23 June 2022 Accepted18 July 2023 Published01 August 2023 DOIhttps...
Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors Abstract Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures....
Machine-learning-assisted materials discovery using failed experiments 基于失败实验的机器学习辅助材料发现.pdf,LETTER doi:10.1038/nature17439 Machine-learning-assisted materials discovery using failed experiments 1 1 1 1 1 1 2 Paul Raccuglia , Katherine C
As the big data generated by the development of modern experiments and computing technology becomes more and more accessible, the material design method based on machine learning (ML) has opened a new paradigm for materials science research. With its ability to automatically solve complex tasks, mac...
We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid Li superionic conductors is of critical importance to the ...
The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real-world applications, particularly in polymer science. We d
Machine-learning-assisted materials discovery using failed experiments Nature, 533 (2016), pp. 73-76 CrossrefView in ScopusGoogle Scholar [29] L.-Q. Chen, L.-D. Chen, S.V. Kalinin, G. Klimeck, S.K. Kumar, J. Neugebauer, et al. Design and discovery of materials guided by theory an...
Wu, S. et al. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm.npj Comput. Mater.5, 66 (2019). MATHGoogle Scholar Deshmukh, A. A. et al. Flexible polyolefin dielectric by strategic design of organic modules for harsh condition el...
用于计算机辅助药物发现的机器学习方法 Machine Learning Approaches for Computer Aided Drug Discovery 热度: Machine-learning-assisted materials discovery using failed experiments-nature-2016-5-5 热度: 664 MRSBULLETIN•VOLUME43•SEPTEMBER2018•.mrs/bulletin ...