{"total":53,"rows":[{"id":"2402.06121","title":"Iterated Denoising Energy Matching for Sampling from Boltzmann Densities","created":"2024-02-09","cited_by_count":116,"area":null},{"id":"2409.17808","title":"Generative Modeling of Molecular Dynamics Trajectories","created":"2024-09-26","cited_by_count":51,"area":null},{"id":"2406.14426","title":"Transferable Boltzmann Generators","created":"2024-06-20","cited_by_count":46,"area":null},{"id":"2502.18462","title":"Scalable Equilibrium Sampling with Sequential Boltzmann Generators","created":"2025-02-25","cited_by_count":38,"area":null},{"id":"2308.10364","title":"SE(3) Equivariant Augmented Coupling Flows","created":"2023-08-20","cited_by_count":37,"area":null},{"id":"2306.03117","title":"Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling","created":"2023-06-05","cited_by_count":29,"area":null},{"id":"2306.05445","title":"Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning","created":"2023-06-08","cited_by_count":26,"area":null},{"id":"2410.18403","title":"Structure Language Models for Protein Conformation Generation","created":"2024-10-24","cited_by_count":17,"area":null},{"id":"2410.09667","title":"EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants","created":"2024-10-12","cited_by_count":16,"area":null},{"id":"2510.03824","title":"Proximal Diffusion Neural Sampler","created":"2025-10-04","cited_by_count":16,"area":null},{"id":"2503.23794","title":"Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks","created":"2025-03-31","cited_by_count":15,"area":null},{"id":"2305.18046","title":"Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics","created":"2023-05-29","cited_by_count":14,"area":null},{"id":"2505.19350","title":"FlashMD: long-stride, universal prediction of molecular dynamics","created":"2025-05-25","cited_by_count":12,"area":null},{"id":"2508.18175","title":"Amortized Sampling with Transferable Normalizing Flows","created":"2025-08-25","cited_by_count":12,"area":null},{"id":"2510.07589","title":"Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics","created":"2025-10-08","cited_by_count":11,"area":null},{"id":"2401.04246","title":"Scalable Normalizing Flows Enable Boltzmann Generators for Macromolecules","created":"2024-01-08","cited_by_count":10,"area":null},{"id":"2405.19961","title":"Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers","created":"2024-05-30","cited_by_count":10,"area":null},{"id":"2404.10935","title":"Molecular relaxation by reverse diffusion with time step prediction","created":"2024-04-16","cited_by_count":9,"area":null},{"id":"2410.01464","title":"Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials","created":"2024-10-02","cited_by_count":9,"area":null},{"id":"2505.17478","title":"ConfRover: Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression","created":"2025-05-23","cited_by_count":8,"area":null},{"id":"2506.03157","title":"UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules","created":"2025-05-20","cited_by_count":7,"area":null},{"id":"2507.00846","title":"BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation","created":"2025-07-01","cited_by_count":7,"area":null},{"id":"2601.22123","title":"Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics","created":"2026-01-29","cited_by_count":7,"area":null},{"id":"2309.07200","title":"Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck","created":"2023-09-13","cited_by_count":6,"area":null},{"id":"2408.15126","title":"Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptides","created":"2024-08-27","cited_by_count":6,"area":null},{"id":"2509.24779","title":"MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models","created":"2025-09-29","cited_by_count":6,"area":null},{"id":"2510.21542","title":"HollowFlow: Efficient Sample Likelihood Evaluation using Hollow Message Passing","created":"2025-10-24","cited_by_count":5,"area":null},{"id":"2409.01931","title":"On the design space between molecular mechanics and machine learning force fields","created":"2024-09-03","cited_by_count":4,"area":null},{"id":"2410.14621","title":"JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles","created":"2024-10-18","cited_by_count":4,"area":null},{"id":"2602.11216","title":"Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators","created":"2026-02-11","cited_by_count":4,"area":null},{"id":"2410.21683","title":"Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer","created":"2024-10-29","cited_by_count":3,"area":null},{"id":"2411.01600","title":"Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics","created":"2024-11-03","cited_by_count":2,"area":null},{"id":"2504.02839","title":"PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks","created":"2025-03-19","cited_by_count":2,"area":null},{"id":"2602.07588","title":"Unified Biomolecular Trajectory Generation via Pretrained Variational Bridge","created":"2026-02-07","cited_by_count":2,"area":null},{"id":"2603.17633","title":"Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics","created":"2026-03-18","cited_by_count":2,"area":null},{"id":"2607.19519","title":"Boltzmann-Expected Molecular Design with Decoupled Annealing Flows","created":"2026-07-21","cited_by_count":2,"area":null},{"id":"2509.17224","title":"AI-based Methods for Simulating, Sampling, and Predicting Protein Ensembles","created":"2025-09-21","cited_by_count":1,"area":null},{"id":"2510.05482","title":"ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics","created":"2025-10-07","cited_by_count":1,"area":null},{"id":"2604.25244","title":"Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics","created":"2026-04-28","cited_by_count":1,"area":null},{"id":"2606.02455","title":"Speculative Sampling For Faster Molecular Dynamics","created":"2026-06-01","cited_by_count":1,"area":null},{"id":"2606.27361","title":"Autoregressive Boltzmann Generators","created":"2026-06-25","cited_by_count":1,"area":null},{"id":"2306.14705","title":"Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies","created":"2023-06-26","cited_by_count":0,"area":null},{"id":"2506.19628","title":"Operator Forces For Coarse-Grained Molecular Dynamics","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2602.10637","title":"Coarse-Grained Boltzmann Generators","created":"2026-02-11","cited_by_count":0,"area":null},{"id":"2604.03911","title":"Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics","created":"2026-04-05","cited_by_count":0,"area":null},{"id":"2605.09311","title":"Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor","created":"2026-05-10","cited_by_count":0,"area":null},{"id":"2609.29413","title":"Neural Transport Nested Sampling","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.32309","title":"PhiFold: Towards Dynamic Protein Design with Physics-Structured Covariance Modeling","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.00546","title":"Generative Modeling of Stochastic Dynamics for Long-Time Evolution","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.01381","title":"SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.05998","title":"Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.12518","title":"Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.16736","title":"Amortised adaptive importance sampling","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}