First, Faster, Further: Competitive Advantage with Next-Generation Materials Development with Physics-based Simulation and Machine Learning

We have entered a paradigm-changing era in the way chemists innovate. Many fields, such as automotive engineering and particle physics, have relied on accurate simulation before experimentation for many years. In recent years, chemistry has entered a new phase of materials design powered by the synergy of physics-based tools, informatics, and augmented intelligence (AI) capabilities that work together synergistically; accelerating materials development from atomistic to coarse-grained simulation.

In this talk, critical case studies illustrating the latest physics-based simulation technology combined with advanced machine learning for the design and optimization of next-generation materials will be discussed. Underlying these success stories are a combination of materials science-specific descriptors, interpretable machine learning models, and most importantly, the critical role of physics-based simulations coupled with generative methods to automatically design new materials solutions. Finally, we will also introduce an enterprise informatics platform focused on chemical discovery, enabling multidisciplinary teams to amplify their development cycle with collaboration on a global scale.