Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
This work addresses the data scarcity issue in developing MLPs for computational chemistry, offering an incremental improvement for researchers in materials science and molecular dynamics.
The paper tackled the problem of training machine learning potentials (MLPs) with limited data by introducing transfer learning across chemically similar elements, such as from silicon to germanium, which improved force prediction and simulation stability, especially with smaller datasets.
Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable datasets to ensure robust generalization across chemical space and thermodynamic conditions. The generation of such datasets can be labor-intensive, highlighting the need for innovative methods to train MLPs in data-scarce scenarios. Here, we introduce transfer learning of potential energy surfaces between chemically similar elements. Specifically, we leverage the trained MLP for silicon to initialize and expedite the training of an MLP for germanium. Utilizing classical force field and ab initio datasets, we demonstrate that transfer learning surpasses traditional training from scratch in force prediction, leading to more stable simulations and improved temperature transferability. These advantages become even more pronounced as the training dataset size decreases. The out-of-target property analysis shows that transfer learning leads to beneficial but sometimes adversarial effects. Our findings demonstrate that transfer learning across chemical elements is a promising technique for developing accurate and numerically stable MLPs, particularly in a data-scarce regime.