#compchem Second preprint linked to the FeNNix-Bio1 #machinelearning foundation model. FeNNix-Bio1's inference is pretty fast already with a few GPUs but, "what if", we were able to push it at the #Exascale? Let's have a glimpse into the future (1/3): "Pushing the Accuracy Limit of Foundation Neural Network Models... with Quantum Monte Carlo Forces and Path Integrals" Check it out 💫: We propose an end-to-end integrated strategy to produce highly accurate quantum chemistry synthetic datasets (energies and forces) aimed at deriving Foundation Machine Learning models for molecular simulation. Starting from Density Functional Theory (DFT), a "Jacob's Ladder" approach leverages computationally-optimized layers of massively #GPU-accelerated software with increasing accuracy. Thanks to Exascale, this is the first time that the computationally intensive calculation of Quantum Monte Carlo forces (QMC), and the combination of multi-determinant QMC energies and forces with selected-Configuration Interaction wavefunctions, are computed at such scale at the complete basis-set limit. To bridge the gap between accurate QC and condensed-phase Molecular Dynamics, we leverage transfer learning to improve the DFT-based FeNNix-Bio1 foundation model. 🚀The resulting approach is coupled to path integrals adaptive sampling quantum dynamics to perform nanosecond reactive simulations at unprecedented accuracy on a full Satellite Tobacco Mosaic Virus (STMV) 1M, all-atom, complete solvated model (see the video produced using VTX, Maxime MARIA Matthieu Montes ). These results demonstrate the promise of Exascale to deepen our understanding of the inner machinery of complex biosystems. Immense thanks to all co-authors at Qubit Pharmaceuticals, Laboratoire de Chimie Théorique (Sorbonne Université /CNRS 🌍 ), The University of Chicago, Sandia National Labs, Oak Ridge Lab and Argonne National Lab for this collaborative efforts. Some are on X: Anouar Benali Thomas Plé ADJOUA Olivier Evgeny Posenitskiy Margaret Blazhynska Thomas Applencourt Jeongnim Kim #HPC This work was made possible thanks to #INCITE projects enabling the use of Argonne's Aurora exascale system and of the Polaris machine, to Genci (Jean Zay @ Idris) and EuroHPC Joint Undertaking (Leonardo CINECA). #supercomputingshow more

Jean-Philip Piquemal

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