Accelerating materials discovery using integrated deep machine learning approaches

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. Our work introduces an innovative deep machine learning framework to significantly accelerate novel materials discovery, as demonstrated by its application to the La-Si-P system where new ternary and quaternary compounds were successfully identified.