Abstract: Artificial Intelligence (AI) is rapidly reshaping almost every aspect of our life, just as how lithium-ion battery did about four decades ago. As a special application of AI, AI4Science represents a fundamental shift in how research is conducted, accelerating discoveries across nearly every scientific domain, from materials design to system data analysis, from image processing to manufacturing control, and eventually the fully autonomous laboratories. Among those numerous materials discovery domains, battery perhaps represents the most challenging case scenario, not only because battery is a complicated system itself subjected to the influences of myriads of parameters from many dimensions, but also because all components in a battery are working at electrochemical extremities far beyond their thermodynamic limits.
Molecular Universe (MU) is the world’s first AI-platform that aims to address the acceleration of materials discoveries. Constructed upon a series of structural (1012) and property (108) databases of astronomical scale, all-inclusive literature databases on the scale of 107, agentic systems operating with various large-language and machine-learning models, and adopting hybridized first-principles as well as data-driven approaches, MU enables us to explore the universe of all small organic molecules that were impossible to reach just a few years ago.
The significance of MU is by no means confined to battery domain only. From broader perspective, the databases underneath MU are agnostic of applications scenarios, as long as “small” organic chemicals are involved, because the basic logic of how chemicals work is constructed on the assembly of molecules, whose quantum properties at single molecule level as well as assemblies in condensed phase together constitute the bulk and interfacial states.
In this talk I will briefly introduce how MU was constructed on various AI techniques, and explore possible ways it can be used in general chemistry and materials science.
About the speaker: Kang Xu is the Chief Technology Officer at SES AI Corp. and an Ohio Eminent Scholar and Winbigler Chair Professor in the Department of Mechanical and Aerospace Engineering at The Ohio State University. He is also a Fellow of the Materials Research Society (MRS), the Electrochemical Society (ECS), and an emeritus Fellow of the Army Research Laboratory (ARL).