Efficiency Advances in Moment Tensor Potentials for Machine-Learning Atomistic Modelling

The study of nuclear materials is strongly complemented by computational materials science (CMS), which provides an alternative means of investigating radiation-induced phenomena, defect evolution, and thermophysical properties—all of which can be hazardous, technically challenging, or impractical to study using traditional experimental methods. Within CMS, a central focus is the accurate atomic-level description of potential energy surfaces and atomic forces, which enables a wide range of simulations. These descriptions, known as interatomic potentials, have recently seen significant advancement through machine learning models. Co-supervised by the Departments of Mechanical and Materials Engineering, and Electrical and Computer Engineering, this Master’s thesis applies the principles of CMS, high-performance computing, and machine learning to advance the computational efficiency of Machine Learning Interatomic Potentials (MLIPs). While emphasis is placed on a specific MLIP, the Moment Tensor Potential (MTP), the methodologies developed herein can be extended for more general use. This thesis presents three contributions, each associated with a manuscript that has been published in, submitted to, or is under internal review before submission to a peer-reviewed journal. The first contribution introduces an efficient small-cell active learning protocol, achieving an order-of-magnitude reduction in training costs while maintaining high fidelity. The second contribution enhances the scalability of the MTP through an improved CPU implementation and a new GPU implementation, enabling higher simulation rates and the modeling of million-atom systems. The third contribution proposes a novel post-training pruning algorithm that is both data-driven and cost-aware, optimizing the MTP’s algorithmic structure and achieving up to sevenfold speedups. Collectively, these contributions establish a more efficient and scalable MTP framework, expanding the scale of accurate atomistic simulations for nuclear materials and for computational materials research more broadly.