PhyMLP: an automated strategy for machine-learning potential construction via data fusion and adaptive point-sampling

This paper presents an automated strategy for constructing machine-learning potential (MLP) datasets through a physics-strengthened point-sampling scheme called physics-strengthened machine-learning potential (PhyMLP). By integrating heterogeneous data sources - including the Rose equation of state, experimental pressure-volume (𝑃 -𝑉) measurements, traditional empirical potentials, and first-principles calculations - this method effectively circumvents computational bottlenecks and reduces the reliance on the exhaustive density functional theory (DFT) computations inherent in conventional training-set construction. The PhyMLP employs an adaptive, physics-guided sampling strategy that leverages intrinsic material responses and requires only a limited number of critical DFT calculations to efficiently characterize the potential energy surface. Using body-centered cubic tungsten as a benchmark system, the moment tensor potential trained on the PhyMLP-generated dataset exhibited exceptional predictive accuracy across a wide spectrum of material properties, ranging from fundamental physical constants to complex defect energetics and kinetic behavior. Ultimately, this study established a systematic and computationally efficient paradigm for developing accurate transferable MLPs, thereby offering a robust framework for large-scale atomistic simulations and advanced material modeling.