METALICA is a new method that combines Metadynamics with Replica Exchange on pretrained diffusion models to improve sampling of rare conformational states in proteins. It builds a bias potential along a chosen Collective Variable, pushes new samples away from previously visited ones, and reweights them to recover the unbiased distribution. By running one replica per diffusion level and allowing inter‑replica communication, METALICA can generate long Markov chains that uncover rare events, as demonstrated on a bimodal test system and on protein unfolding where it reveals a second free‑energy minimum that other methods miss.
By Alireza Omidi, Jiajun He, J\"org Gsponer, Saifuddin Syed
arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.
By Marylou Gabri\'e
arXiv:2609.22663v1 Announce Type: cross
Abstract: Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these...
By Venkata Sai Sreyas Adury (Chemical Physics Program and Institute for Physical Science and Technology, University of Maryland), Pratyush Tiwary (Biophysics Program and Institute for Physical Science and Technology, University of Maryland, Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, University of Maryland Institute for Health Computing, Bethesda, USA)
arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).
By Emmanuel Bengio, Sanjeev Raja, Yui Tik Pang, Kerstin Klaeser, Cristian Gabellini, Nikhil Shenoy, Francesco Di Giovanni, Prudencio Tossou
arXiv:2605. 00337v2 Announce Type: replace Abstract: Sampling the distribution of collective variables (CVs) and estimating the associated free energy surface are crucial problems in statistical physics, as they underpin a better understanding of chemical reactions and conformational transitions.
By Zichen Liu, Tiejun Li
arXiv:2605. 31498v2 Announce Type: replace Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules.
By Daniel Pe\~naherrera, Rishal Aggarwal, David Ryan Koes
arXiv:2510.07589v2 Announce Type: replace-cross
Abstract: Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conve...
By Juan Viguera Diez, Mathias Schreiner, Simon Olsson
arXiv:2608. 13800v1 Announce Type: new Abstract: Transition path sampling (TPS) aims to efficiently generate rare molecular transition trajectories between metastable states and is essential for understanding biomolecular mechanisms.
By Jingqian Liu, Yu-Hsiang Wang, Yanru Qu, Ge Liu
arXiv:2610.02081v1 Announce Type: new
Abstract: There has been a proliferation of sampling algorithms based on Wasserstein gradient flows (WGF) and forward-only diffusion processes (FODP), often acco...
By Daniel McBride, Pratik Khandagale, Cristina Garcia-Cardona, Yen Ting Lin
arXiv:2606. 30687v1 Announce Type: cross Abstract: Diffusion models are increasingly utilized for modeling molecular structures and conformational ensembles, yet the thermodynamic meaning of their learned representations and scores remains elusive.
By Wenjie Xi
arXiv:2602. 24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data.
By Advaith Maddipatla, Anar Rzayev, Marco Pegoraro, Martin Pacesa, Paul Schanda, Ailie Marx, Sanketh Vedula, Alex M. Bronstein
Neural Transport Nested Sampling (NTNS) is a new sampling algorithm that merges nested sampling with neural flow-based methods to sample from Boltzmann distributions of molecular systems. It employs a flow-matching velocity as the drift in a Metropolis–Hastings corrected Langevin kernel within a nested sampling loop, requiring only target energy evaluations and enabling scalable estimation of the full partition function for high-dimensional particle systems. Benchmarks on Lennard–Jones clusters up to 55 particles show NTNS reduces interatomic distance and energy Wasserstein errors by more than an order of magnitude compared to leading neural baselines, while also providing a calibrated, temperature-resolved partition function estimate that captures phase structure from a single run.
By David Yallup, Will Handley