Abstract
Simultaneous Learning and Exploring Atomistic Potential Surfaces: Current Progress and Mathematical Challenges
Alexander Shapeev
Skolkovo Institute of Science and Technology
Atomistic simulations (such as molecular dynamics) is the largest consumer of supercomputing time worldwide. Atomistic simulations rely on one of the two models: very accurate and very computationally expensive quantum-mechanical models that resolve electronic structure, and empirical interatomic models that postulate a simple functional form of interatomic interaction that is fast to compute. Application of ideas of machine learning has recently been put forward as a promising way to get the best out of these two models: accuracy of quantum mechanics and computational efficiency of the interatomic potentials.
The purpose of atomistic simulations is often to explore an atomistic potential energy surface, for instance, when a molecular reaction mechanism is not known. This creates a challenge for machine-learning-based approaches: the training dataset should include representative atomistic configurations that are not known a priori in this case. This can be solved by active learning which consists in simultaneously exploring and learning the potential energy surface.
In this talk I will present my version of machine-learning interatomic potentials and an active learning algorithm. I will then illustrate applications of these methods in molecular dynamics, crystal structure prediction, alloy discovery and cheminformatics. Finally, I will discuss the mathematical challenges related to machine learning and active learning of interatomic potentials.
The purpose of atomistic simulations is often to explore an atomistic potential energy surface, for instance, when a molecular reaction mechanism is not known. This creates a challenge for machine-learning-based approaches: the training dataset should include representative atomistic configurations that are not known a priori in this case. This can be solved by active learning which consists in simultaneously exploring and learning the potential energy surface.
In this talk I will present my version of machine-learning interatomic potentials and an active learning algorithm. I will then illustrate applications of these methods in molecular dynamics, crystal structure prediction, alloy discovery and cheminformatics. Finally, I will discuss the mathematical challenges related to machine learning and active learning of interatomic potentials.