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1篇 您的检索式：作者名="Shanthi Pandian"

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		题名	作者	年代	出处	被引量
	1	Attribute driven inverse materials design using deep learning Bayesian framework显示文摘Much of computational materials science has focused on fast and accurate forward predictions of materials properties,for example,given a molecular structure predict its electronic properties.This is achieved with first principles calculations and more recently through machine learning approaches,since the former is computation-intensive and not practical for high-throughput screening.Searching for the right material for any given application,though follows an inverse path—the desired properties are given and the task is to find the right materials.Here we present a deep learning inverse prediction framework,Structure Learning for Attributedriven Materials Design Using Novel Conditional Sampling(SLAMDUNCS),for efficient and accurate prediction of molecules exhibiting target properties.We apply this framework to the computational design of organic molecules for three applications,organic semiconductors for thin-film transistors,small organic acceptors for solar cells and electrolyte additives with high redox stability.Our method is general enough to be extended to inorganic compounds and represents an important step in deep learning based completely automated materials discovery.	Piyush M.Tagade Shashishekar P.Adiga Shanthi Pandian Min Sik Park Krishnan S.Hariharan Subramanya Mayya Kolake	2019	npj Computational Materials2019，，1：	2

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