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  1. Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials.

    Publication
    Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computational cost …

  2. Assessing generative modeling approaches for free energy estimates in condensed matter

    Publication
    The accurate estimation of free energy differences between two states is a long-standing challenge in molecular simulations. Traditional approaches generally rely on sampling multiple intermediate states to …

  3. Estimating Solvation Free Energies with Boltzmann Generators

    Publication
    Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficient overlap between phase-space …

  4. MXtalTools: A Toolkit for Machine Learning on Molecular Crystals

    Publication
    We present MXtalTools, a flexible Python package for the data-driven modelling of molecular crystals, facilitating machine learning studies of the molecular solid state. MXtalTools comprises several classes …

  5. Scalable Boltzmann Generators for equilibrium sampling of large-scale materials

    Publication
    The use of generative models to sample equilibrium distributions of many-body systems, as first demonstrated by Boltzmann Generators, has attracted substantial interest due to their ability to …

  6. Multi-type point cloud autoencoder: a complete equivariant embedding for molecule conformation and pose

    Publication
    Representations are a foundational component of any modeling protocol, including on molecules and molecular solids. For tasks that depend on knowledge of both molecular conformation and 3D orientation …