#compchem New preprint: Scalable Hybrid Deep #NeuralNetworks/Polarizable Potentials Biomolecular Simulations including Long-range Effects. Great work by Jaffrelot Inizan Théo Thomas Plé ADJOUA Olivier & collabs: Olexandr Isayev 🇺🇦🇺🇸 Pengyu Ren. EMC2 European Research Council (ERC), #HPC Genci arxiv.org/abs/2207.14276

#compchem New #preprint: Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: from Force Fields to Machine Learning Potentials. Great work by Thomas Plé. TINKERtools arxiv.org/abs/2212.03137

#compchem🚨#preprint🚨:Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties & long-range effects. Great work by Thomas Plé introducing the hybrid physically-driven #machinelearning FENNIX model arxiv.org/abs/2301.08734

#compchem #MachineLearning #HPC Out Chemical Science. "Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects", introducing the Deep-HP #NeuralNetworks multi-#GPU-accelerated platform @Tinkertools doi.org/10.1039/D2SC04…

#compchem Preprint:Lambda-ABF: Simplified, Accurate & Cost-effective Alchemical Free Energy Computations. New Colvars #opensource library for NAMD & Tinker-HP. Great efforts: L. Lagardère & L. Maurin & collabs: P. Monmarché & J. Hénin Institut de Biologie Physico-Chimique (IBPC) Paris Laboratoire de Chimie Théorique arxiv.org/abs/2307.08006

🚨Just published in Chemical Science🚨: "Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties and long-range effects. doi.org/10.1039/D3SC02… Amazing #compchem work by Thomas Plé introducing the hybrid

🥂As the end of year is nearly there, a quick recap on the 2023 the Piquemal Group's papers & preprints in #MachineLearning for #compchem🤖: - Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory; J. Phys.

#compchem Latest group preprint: FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials. arxiv.org/abs/2405.01491 A new GPU-accelerated #opensource library for building, training and running force-field-enhanced neural network

#compchem Check out this new paper where we reach chemical accuracy at the Full CI/CBS limit for small molecules (up to nearly 300 qubits!!) using GPU-accelerated quantum emulation.

#compchem Just published in JCTC JCIM & JCTC Journals: "Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach". dx.doi.org/10.1021/acs.jc… In this study, we propose a Graph-Based Force Fields (GB-FFs) model to directly derive parameters for the Generalized

#compchem Happy to see this one out and published The Journal of Chemical Physics as part of the special issue: "Modular and Interoperable Software for Chemical Physics": "FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials. FeNNol is a new

#compchem Just published in Chemical Science: Water-Glycan Interactions Drive the SARS-CoV-2 Spike Dynamics: Insights into Glycan-Gate Control and Camouflage Mechanism. dx.doi.org/10.1039/D4SC04… Towards developing therapeutic strategies against #COVID19, we performed μs-long

#compchem New group preprint: "Velocity Jumps for Molecular Dynamics". arxiv.org/abs/2412.15073 We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical

#compchem New preprint: Satellite Tobacco Mosaic Virus: Revealing Environmental Drivers of Capsid and Nucleocapsid Stability using High-Resolution Simulations. biorxiv.org/content/10.110… Large scale MD & metadynamics simulations of the complete STMV using the AMOEBA polarizable FF.

#compchem What could we do if we had a fast and accurate foundation #MachineLearning learning model for condensed phase molecular dynamics simulation of biological systems? 👉Check the ChemRxiv preprint: "A Foundation Model for Accurate Atomistic Simulations in Drug Design":

#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

#compchem New preprint: "𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐃𝐲𝐧𝐚𝐦𝐢𝐜𝐬 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐌𝐨𝐝𝐞𝐥𝐬 𝐮𝐬𝐢𝐧𝐠 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐓𝐢𝐦𝐞-𝐒𝐭𝐞𝐩 𝐚𝐧𝐝 𝐃𝐢𝐬𝐭𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧" in link with our

💫 As promised, we just released on GitHub the weights of the #FeNNixBio1 foundation machine learning model for drug design! 💫 Weights: github.com/FeNNol-tools/F… FeNNol GPU code: github.com/FeNNol-tools/F… The models are distributed under the open source ASL license (i. e.

#compchem Our paper in J. Phys. Chem. Lett. (The Journal of Physical Chemistry): "Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation" made it to one of the covers! pubs.acs.org/doi/full/10.10…

#quantumcomputing New group preprint: "Experimental Realization of the Markov Chain Monte Carlo Algorithm on a Quantum Computer". arxiv.org/abs/2603.08395 In this work, we experimentally use encodings of Markov chains to prepare quantum states and run a quantum Markov Chain