In-Silico Molecular Simulation Is Slow and Kludgy
In-silico molecular simulation has not received the necessary push, despite the promise of machine learning-based surrogate models. Moreover, advancements in quantum chemistry—both AI accelerated and quantum/ASIC-enabled—remain underexploited.
Foundational Capabilities (8)
Use neural network potentials and force fields to enhance molecular dynamics simulations, making them more efficient and accurate.
Extend work on ML force fields to charge transfer problems in external potentials, enabling in-silico discoveries in batteries, electrolysis, carbon capture, biochemistry and the origins of life
Utilize hybrid quantum algorithms to perform quantum chemistry simulations, capitalizing on recent progress in quantum computing.
Create accurate, thoroughly benchmarked calculations for elements beyond the second row of the periodic table, for clusters of 3 or more atoms. Could complement 29.5.
Leverage AI techniques to accelerate quantum chemistry calculations, improving the speed and accuracy of electronic structure predictions.
Produce a high-quality experimental dataset to validate molecular simulation techniques and transition to frictionless reproducibility.
Support open alternatives to Reaxys and the Cambridge Structural Dataset to the point where they are equal or superior in quality