Atomistic Machine Learning

Atomistic machine learning (ML) aims to predict the properties of materials and molecules using machine learning models trained on atomistic data, accelerating materials discovery and design. Current research focuses on developing robust and efficient ML models, including neural networks and graph neural networks, often incorporating techniques like Bayesian optimization and automatic differentiation to improve accuracy and reduce computational cost. Key challenges involve handling the high dimensionality of atomistic data, ensuring model accuracy and uncertainty quantification, and developing methods that explicitly incorporate physical symmetries and constraints. These advancements promise to significantly reduce the time and resources required for materials simulations and discovery, impacting fields ranging from catalysis to drug design.