molecular-dynamics

AdaptNTK introduces an adaptive uncertainty quantification framework using neural tangent kernels to enable efficient active learning in neural network potentials, reducing computational cost while maintaining accuracy in molecular dynamics simulations.

This paper introduces stochastic control policies (FS-TPS and LaS-TPS) for molecular transition path sampling, improving robustness and performance across biomolecular systems.

Agent-MD is a framework that selectively applies LLM reasoning to long-running molecular simulation campaigns, using a deterministic rule-based agent for routine tasks and event-triggered LLM review for exceptional conditions. Demonstrated in GCMC–MD water-vapor desorption simulations, it shows that auditable, reproducible scientific workflows can avoid placing every operation inside an LLM reasoning loop.

This paper introduces CGMas, a multi-agent LLM framework that automates coarse-grained molecular dynamics for polymers, including topology construction, equilibration, mapping, potential derivation, and validation. It completed 27 polymer tasks and matched atomistic densities within 5% in most cases, drastically reducing simulation time.

Introduces implicit machine learning force fields (I-MLFFs) that replace deep neural network stacks with fixed-point equations, enabling warm-starting and up to 5x compute/memory savings across graph neural network architectures for molecular dynamics simulations.

Valence AI announced AquaGen, a model that simulates water physics 100x faster than classical molecular dynamics, with potential applications in biochemical understanding and drug research.

The paper introduces EquiFiLM, a lightweight extension that adds continuous external conditioning to equivariant foundation machine learning force fields via Feature-wise Linear Modulation, achieving significant accuracy improvements with minimal training data.

MDForge is an LLM agent that automates the design of molecular dynamics pipelines for host-guest binding free-energy calculations, achieving human-expert competitive results and discovering a novel high-affinity binder.

Researchers from MIT, University of Warwick, and NVIDIA introduce Stein Kernelized Molecular Dynamics (SKMD), an enhanced sampling method that uses interacting particle dynamics to acquire informative training configurations for active learning and fine-tuning of machine learning interatomic potentials (MLIPs). SKMD is a stochastic variant of Stein variational gradient descent adapted for molecular dynamics, preserving the Boltzmann distribution while achieving higher model accuracy in fewer training iterations compared to baselines.

EvoMD-LLM reformulates reactive molecular dynamics trajectories as symbolic temporal sequences, enabling LLMs to model species evolution over time through fine-tuning and temporal scaffolding, achieving up to 66.14% accuracy and interpretable predictions.

This paper introduces a Hessian matching framework for machine-learned coarse-grained molecular dynamics that augments force matching with stochastic Hessian-vector product matching, instilling second-order curvature information into CG potentials. The method achieves up to 85% reduction in Kullback-Leibler divergence on slow-mode metrics for fast-folding proteins.