Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
This addresses the problem of inaccurate phase transition predictions in material science for researchers using machine learning potentials, representing an incremental improvement through fine-tuning.
The paper tackles the problem of machine learning potentials having significant deviations from experimentally observed phase transition temperatures, proposing a fine-tuning strategy that directly corrects wrongly predicted transition temperatures to match experimental data. The approach accurately corrects the phase diagram of pure Titanium in a pressure range up to 5 GPa, matching experimental reference within tenths of kelvins and improving the liquid-state diffusion constant.
Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior through Molecular Dynamics simulations, which can crucially expedite material design and discovery. However, insufficiently broad and systematically biased reference data affect the predictive quality of the learned models. Often, these models exhibit significant deviations from experimentally observed phase transition temperatures, in the order of several hundred kelvins. Thus, fine-tuning is necessary to achieve adequate accuracy in many practical problems. This work proposes a fine-tuning strategy via top-down learning, directly correcting the wrongly predicted transition temperatures to match the experimental reference data. Our approach leverages the Differentiable Trajectory Reweighting algorithm to minimize the free energy differences between phases at the experimental target pressures and temperatures. We demonstrate that our approach can accurately correct the phase diagram of pure Titanium in a pressure range of up to 5 GPa, matching the experimental reference within tenths of kelvins and improving the liquid-state diffusion constant. Our approach is model-agnostic, applicable to multi-component systems with solid-solid and solid-liquid transitions, and compliant with top-down training on other experimental properties. Therefore, our approach can serve as an essential step towards highly accurate application-specific and foundational machine learning potentials.