Multiscale AI

Given low-level theory and computationally-expensive simulation code, how can we model complex systems on a useful time scale? Fundamental laws of Nature, Standard Model of Physics, and the most applied part of it, quantum mechanics, are well established. Theoretically, the dynamics of anything starting from a hydrogen atom and all the way to Earth's climate follow those equations. The problem is complexity Dirac 1929. An exact computation of a modest system containing 100 atoms is still beyond the capability of modern computers. Some of the greatest scientific achievements resulted from breakthroughs in scale transitions: renormalization, density functional theory, Higgs boson, multiscale models for complex chemical systems, climate modeling, protein folding. Those achievements are highly regarded because they are impactful – but also unique and can't be readily applied to different systems. Encouraged by the recent successes, this workshop aims to enable the development of universal AI methods that would be able to find efficient and accurate approximations, and use them for some of the most pressing and high-impact scientific problems that have computational complexity as the limiting factor to an in silico solution, such as: High-temperature superconductivity Fusion power Weather prediction Catalysts Brain and consciousness If we solve scale transition, we solve science.