Issue 47, 2023

Accelerating materials discovery using integrated deep machine learning approaches

Abstract

We present an integrated deep machine learning (ML) approach that combines crystal graph convolutional neural networks (CGCNN) for predicting formation energies and artificial neural networks (ANN) for constructing interatomic potentials. Using the La–Si–P ternary system as a proof-of-concept, we achieve a remarkable speed-up of at least 100 times compared to high-throughput first-principles calculations. The ML approach successfully identifies known compounds and uncovers 16 new P-rich compounds with formation energies within 100 meV per atom above the convex hull, including a stable La2SiP3 phase. We also employ the developed ML interatomic potential in a genetic algorithm for efficient structure search, leading to the discovery of more metastable compounds. Moreover, substitution of La atoms with Ba reveals a new stable quaternary compound, BaLaSiP3. Our generic and robust approach holds great promise for accelerating materials discovery in various compounds, enabling more efficient exploration of complex chemical spaces and enhancing the prediction of material properties.

Graphical abstract: Accelerating materials discovery using integrated deep machine learning approaches

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Supplementary files

Article information

Article type
Paper
Submitted
27 Чер 2023
Accepted
27 Вер 2023
First published
29 Вер 2023
This article is Open Access
Creative Commons BY license

J. Mater. Chem. A, 2023,11, 25973-25982

Accelerating materials discovery using integrated deep machine learning approaches

W. Xia, L. Tang, H. Sun, C. Zhang, K. Ho, G. Viswanathan, K. Kovnir and C. Wang, J. Mater. Chem. A, 2023, 11, 25973 DOI: 10.1039/D3TA03771A

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