First Principle

"First-principles" methods in scientific computing aim to derive models directly from fundamental physical laws, avoiding reliance on empirical data or approximations. Current research focuses on accelerating these computationally expensive methods through machine learning techniques, employing architectures like neural networks and diffusion models to predict properties (e.g., material behavior, molecular structures) with significantly increased speed and efficiency. This approach is transforming fields like materials science, quantum chemistry, and even social dynamics modeling by enabling simulations at larger scales and longer timescales than previously possible, leading to faster discovery and improved design of new materials and technologies.