High-Performance, Quantum and AI-Driven Chemical Discovery

Le résumé fourni par la source

Addressing today s most pressing technological and scientific challenges from developing new therapeutics and biotechnologies to creating greener chemical processes requires the ability to accurately model matter at large atomistic scales and with high speed. However, current simulation techniques can be prohibitively slow, imprecise, or computationally too costly, resulting in heavy reliance on physical experiments that are time-consuming, expensive, and subject to inherent limitations. In response, we present a pioneering digital chemistry framework that integrates many-GPU high-performance computing, computational quantum chemistry, and machine learning, delivering unprecedented accuracy and scalability in chemical discovery particularly for drug design. By leveraging novel algorithms and software optimizations, we overcome the steep computational barriers that have historically limited quantum chemistry at large molecular scales. Recognized with the 2024 Gordon Bell Prize, our approach enables the first quantum-level simulations of complex bioscale molecular systems with accuracy nearing that of physical experiments. Through exascale supercomputing, agentic AI workflows, and machine learning models trained on quantum-accurate data, we streamline end-to-end simulation and design processes. These intelligent workflows reduce reliance on laboratory experiments, providing a fully automatable, cost-effective, adaptive, and accurate solution for investigating complex molecular phenomena. The result is a powerful platform that accelerates chemical discovery, enhances molecular design, and redefines in silico research opportunities across chemistry, biology, and related fields.

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