报告摘要 |
Materials modeling with atomistic simulation has become an indispensable tool in computational materials science, enabling precise property predictions and mechanistic insights across a wide range of chemical and structural environments. While recent advancements in artificial intelligence (AI)-assisted techniques, such as machine learning interatomic potentials (MLIPs) trained on extensive databases (e.g., foundational potential), have significantly enhanced temporal and spatial simulation capabilities, exploring high-dimensional chemical spaces with quantum-level accuracy remains computationally demanding. Simultaneously, the emergence of generative modeling has demonstrated its potential to change the landscape of computational materials and chemistry through generative approaches. In this presentation, I will outline recent progress in universally augmenting foundational potentials with charge and electrical response prediction, referred to as Latent Ewald Summation (LES) for generalized learning schemes of atomic charges and long-range interactions. Beyond the advancements in foundational potential, I will discuss the diffusion-based deep generative model (CHGGen) that integrates host-guided inpainting generation and foundation potential optimization for crystal structure prediction. I will showcase how this model can be used to elucidate atomic configurations and Li transport properties within solid-electrolyte interphases. |
