Jean-Philip Piquemal @piquemal@mastodon-pa.sorbonne-universite.fr

#compchem Good read: Hybrid Auxiliary Field Quantum Monte Carlo for Molecular Systems. https://doi.org/10.1021/acs.jctc.3c00038

#compchem Good read: Geometric Deep Learning for Molecular Crystal Structure Prediction https://doi.org/10.1021/acs.jctc.3c00031

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#compchem Good read: Automated Active Space Selection with Dipole Moments https://doi.org/10.1021/acs.jctc.2c01128

#compchem Good read: Solving the Schrödinger Equation in the Configuration Space with Generative Machine Learning https://doi.org/10.1021/acs.jctc.2c01216

#compchem #quantumcomputing Good read although too pessimistic in my opinion. Not sure the conclusions will last long but we will see rapidly. "Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry" https://www.nature.com/articles/s41467-023-37587-6

#compchem Just published @JPhysChem A: Extension of the Trotterized Unitary Coupled Cluster to Triple Excitations. Quantum Chemistry & #quantumcomputing: introducing UCCSDT!!! Great work by @HaidarM54481494. Another nice collab. with MJ Rančić & Y. Maday. https://doi.org/10.1021/acs.jpca.3c01753

#compchem #MachineLearning #HPC Out in Chemical Science. "Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects", introducing the Deep-HP #NeuralNetworks multi-#GPU-accelerated platform (part of Tinker-HP). Check the paper out (#openaccess) !!! https://doi.org/10.1039/D2SC04815A

#compchem Good read: CHARMM-GUI Membrane Builder: Past, Current, and Future Developments and Applications https://doi.org/10.1021/acs.jctc.2c01246

#compchem Good read: Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces https://doi.org/10.1021/acs.jctc.2c01038

#compchem Simulating more than 10 μs, we compute charged/uncharged ligands solvation free energies in 4 solvents, & absolute binding free energies of host–guest complexes from SAMPL challenges. ANI-2X/AMOEBA average errors are within chemical accuracy at force-field cost.
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RT @jppiquem
#compchem #MachineLearning #HPC Out @ChemicalScience. "Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including lo…
https://twitter.com/jppiquem/status/1643163320503918592

#compchem
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RT @JPhysChem
Improved accuracy for the Deep Learning-aided many-body dispersion (DNN-MBD) correction to DFT. DNN-MBDQ goes beyond dipole including quadrupole polarizability (Q) terms via a generalized Random Phase Approximation (RPA) formalism. @PierPoier @jppiquem
https://go.acs.org/4na
https://twitter.com/JPhysChem/status/1642149829596905478