Our group is interested in the development of new machine learning approaches for Computational chemistry. We focus on :

  • algorithmic developments, ranging from machine-learning assisted parametrization of force fields and density functional theory,  to the design of neural network potentials and foundation models.
  • real-life high-performance implementations on GPU-accelerated computing systems,

Selected publications in Machine Learning for Computational Chemistry

    1. A Supervised Fitting Approach to Force Field Parametrization with Application to the SIBFA Polarizable Force Field.
      M. Devereux, N. Gresh, J.-P. Piquemal, M. Meuwly, J. Comput. Chem., 2014, 35, 1577-1591 (COVER) [PDF][HAL]
      DOI: 10.1002/jcc.23661
    2. High-Resolution Mining of SARS-CoV-2 Main Protease Conformational Space: Supercomputer-Driven Unsupervised Adaptive Sampling.
      T. Jaffrelot Inizan, F. Célerse, O. Adjoua, D. El Ahdab, L.-H. Jolly, C. Liu, P. Ren, M. Montes, N. Lagarde, L. Lagardère, P. Monmarché, J.-P.Piquemal, Chem. Sci., 2021, 12, 4889 – 4907 (Open Access) [ChemRxiv][HAL]
      Simulation data can be found on the MolSSi/Bioexcel website : [Link]
      DOI: 10.1039/D1SC00145K
    3. Accurate Deep Learning-aided Density-free Strategy for Many-Body Dispersion-corrected Density Functional Theory.
      P. P. Poier, T. Jaffrelot Inizan, O. Adjoua, L. Lagardère, J.-P. Piquemal, J. Phys. Chem. Lett.,  2022, 13, 19, 4381–4388 [HAL] [ArXiv]
      DOI: 10.1021/acs.jpclett.2c00936
    4. Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory.
      P. P. Poier, L. Lagardère, J.-P. Piquemal, J. Phys. Chem. Lett., 2023, 14, 6, 1609–1617 (Open Access) [HAL][ArXiv]
      DOI: 10.1021/acs.jpclett.2c03722
    5. Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: from Force Fields to Machine Learning Potentials.
      T. Plé, N. Mauger, O. Adjoua,T. Jaffrelot-Inizan, L. Lagardère, S. Huppert, J.-P. Piquemal, J. Chem. Theory. Comput., 2023, 19, 5, 1432–1445 (COVER) [HAL][ArXiv]
      DOI: 10.1021/acs.jctc.2c01233
    6. Scalable Hybrid Deep Neural Networks/Polarizable Potentials Biomolecular Simulations including long-range effects.
      T. Jaffrelot Inizan, T. Plé, O. Adjoua, P. Ren, H. Gökcan, O. Isayev, L. Lagardère, J.-P. Piquemal, Chem. Sci., 2023, 14, 5438-5452 (Open Access) [HAL][ArXiv]
      DOI: 10.1039/D2SC04815A
    7. Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties and long-range effects.
      T. Plé, L. Lagardère, J.-P. Piquemal, Chem. Sci., 2023, 14, 12554-12569 (Open Access) [HAL][ArXiv]
      DOI: 10.1039/D3SC02581K
    8. Incorporating Neural Networks into the AMOEBA Polarizable Force Field.
      X. Wang, T. Jaffrelot Inizan, C. Liu, J.-P. Piquemal, P. Ren, J. Phys. Chem. B, 2024, 128 (10), 2381–2388 [HAL][ChemRxiv]
      DOI: 10.1021/acs.jpcb.3c08166
    9. Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach
      G. Chen,       T. Jaffrelot Inizan, T. Plé, L. Lagardère, J.-P. Piquemal, Y. Maday, J. Chem. Theory. Comput., 2024, 20 (13), 5558-5569 [HAL][ChemRxiv]
      DOI: 10.1021/acs.jctc.3c01421
    10. FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials.
      T. Plé, O. Adjoua, L. Lagardère, J.-P. Piquemal, J. Chem. Phys., 2024, 161, 4,  042502 [HAL][ArXiv]
      DOI: 10.1063/5.0217688
    11. Targeting RNA with Small Molecules using State-of-the-Art Methods Provides Highly Predictive Affinities of Riboswitch Inhibitors.
      N. Ansari, C. Liu, F. Hédin, J. Hénin, J. W. Ponder, P. Ren, J.-P. Piquemal, L. Lagardère, K. El Hage, Communications Biology, 2025, 8, 1405 (Open Access) [HAL][ChemRxiv]
      DOI: 10.1038/s42003-025-08809-y
    12. A Foundation Model for Accurate Atomistic Simulations in Drug Design.
      T. Plé, O. Adjoua, A. Benali, E. Posenitskiy, C. Villot, L. Lagardère, J.-P.Piquemal, 2025, submitted [HAL][ChemRxiv]
      DOI:10.26434/chemrxiv-2025-f1hgn
    13. Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals .
      A. Benali, T. Plé, O. Adjoua,  V. Agarawal, T. Applencourt, M. Blazhynska, R. Clay III, K. Gasperich, K. Hossain, J. Kim, C. Knight, J. T. Krogel, Y. Maday, M. Maria, M. Montes, Y. Luo, E. Posenitskiy, C. Villot, V. Vishwanath, L. Lagardère,  J.-P. Piquemal, 2025, submitted [HAL][ArXiv]
      DOI: 10.48550/arXiv.2504.07948
    14. Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models using Multiple Time-Step and Distillation
      C. Cattin, T. Plé, O. Adjoua, N. Gouraud, L. Lagardère, J.-P. Piquemal, 2025, submitted  [HAL][ArXiv]
      DOI: 10.48550/arXiv.2510.06562

 

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Jean-Philip Piquemal