| Abstract Scope |
Physical behavior of materials often involves complex interactions of electrons and ions, from ultrafast processes at atomistic scale to slow dynamics at mesoscale. First-principles density-functional theory is often accurate, however it is limited to small system and short timescale. In contrast, classical molecular dynamics is a powerful tool for simulating systems at much larger length-scale and longer time-scale, its application is constrained by the availability of accurate force fields. Here I will present our recent development of machine learning force fields (MLFF) from first-principles theory for large-scale long-time simulations. We applied it to study phase transition and thermal properties of 2D materials. The generated force fields are able to capture essential physics both qualitatively and quantitatively such as energetic, structural, and dynamic properties. Our MLFF framework can be generally applied to complex multinary materials, opening up unprecedented avenues for understanding and predicting physical behaviors of complex materials. |