10 citations · 15 across the 2 of their papers we have counts for
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cond-mat.mtrl-sci2022★ 5 cited
Deep Reinforcement Learning for Inverse Inorganic Materials Design
Elton Pan, Christopher Karpovich, Elsa Olivetti
A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and s…
cond-mat.mtrl-sci2021★ 10 cited
Inorganic Synthesis Reaction Condition Prediction with Generative Machine Learning
Christopher Karpovich, Zach Jensen, Vineeth Venugopal +1
Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis…