Author:
Jha Dipendra,Gupta Vishu,Liao Wei-keng,Choudhary Alok,Agrawal Ankit
Abstract
AbstractWhile experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$
>
0.076
eV/atom) for the first time.
Funder
U.S. Department of Commerce
NSF awards
U.S. Department of Energy
Publisher
Springer Science and Business Media LLC
Cited by
14 articles.
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