Accelerating materials discovery using integrated deep machine learning approaches†
a
Ling
Tang,
b
Chao
Zhang,
d
Gayatri
Viswanathan,
af
Kirill
Kovnir
af
and
Cai-Zhuang
Wang
*ae
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.
- This article is part of the themed collection: Machine learning and artificial neural networks: Celebrating the 2024 Nobel Prize in Physics
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