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1篇 您的检索式：作者名="Brook Wander"

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		题名	作者	年代	出处	被引量
	1	AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials显示文摘Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications.A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate and a catalyst surface of interest.Traditionally,the identification of low-energy adsorbate-surface configurations relies on heuristic methods and researcher intuition.As the desire to perform high-throughput screening increases,it becomes challenging to use heuristics and intuition alone.In this paper,we demonstrate machine learning potentials can be leveraged to identify low-energy adsorbate-surface configurations more accurately and efficiently.Our algorithm provides a spectrum of trade-offs between accuracy and efficiency,with one balanced option finding the lowest energy configuration 87.36%of the time,while achieving a~2000×speedup in computation.To standardize benchmarking,we introduce the Open Catalyst Dense dataset containing nearly 1000 diverse surfaces and~100,000 unique configurations.	Janice Lan Aini Palizhati Muhammed Shuaibi Brandon M.Wood Brook Wander Abhishek Das Matt Uyttendaele C.Lawrence Zitnick Zachary W.Ulissi	2023	npj Computational Materials2023，，1：	0

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