Moving closer to experimental level materials property prediction using AI

Dipendra Jha, Vishu Gupta, Wei keng Liao, Alok Choudhary, Ankit Agrawal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


While 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 eV/atom) for the first time.

Original languageEnglish (US)
Article number11953
JournalScientific reports
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • General


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