Survey of Machine Learning Interatomic Potentials: Models, Training, and Evaluation

Machine learning interatomic potentials (MLIPs) bridge the gap between microscopic quantum mechanics and macroscopic property simulation by fitting high-dimensional potential energy surfaces. While maintaining near-first-principles accuracy, MLIPs extend molecular dynamics simulations to significantly larger spatial and temporal scales. Driven by the synergy of “model, training, and evaluation”, where diverse architectures need tailored training strategies, efficient optimizations can accelerate model development, and comprehensive task evaluation ensures the MLIPs’ reliability. This paper provides a systematic review of the latest advancements in MLIPs architecture, training optimizations, and evaluation benchmarks. For model architecture, MLIPs can be categorized into invariant, equivariant, and unconstrained based on their symmetry-keeping approach. We then analyze the evolution from “specialized models” to “pretrained models” and introduce representative models. On the training strategies, we discuss key techniques such as framework-less implementations and kernel fusion for specialized MLIPs, alongside load balancing and two-stage training strategies for pretrained MLIPs. Finally, we list the evaluation dimensions of modern benchmarks such as Matbench Discovery and OMC-Bench.