Molecular Dynamics
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Related Articles from SNS
Speculative Sampling For Faster Molecular Dynamics
arXiv:2606.02455v1 Announce Type: cross Abstract: Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error.
Speculative Sampling For Faster Molecular Dynamics
arXiv:2606.02455v1 Announce Type: new Abstract: Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error.
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
arXiv:2606.04100v1 Announce Type: cross Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data. We introduce Stein kernelized molecular dynamics (SKMD), an enhanced sampling method that uses interacting particle dynamics to acquire informative training configurations for the active learning and fine-tuning of MLIPs. SKMD corresponds to a stochastic variant of Stein...
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
arXiv:2606.04100v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data. We introduce Stein kernelized molecular dynamics (SKMD), an enhanced sampling method that uses interacting particle dynamics to acquire informative training configurations for the active learning and fine-tuning of MLIPs. SKMD corresponds to a stochastic variant of Stein...
Reliable Viscosity Calculation from High-Pressure Equilibrium Molecular Dynamics: Case Study of 2,2,4-Trimethylhexane
Announce Type: replace Abstract: Viscosity is a fundamental property of liquid lubricants, yet it is challenging to determine accurately, especially at high pressures. Although equilibrium molecular dynamics (EMD) simulations are a promising alternative to resource-intensive experiments, practical challenges remain in assessing the sufficiency of simulation time and in controlling uncertainties in the Green-Kubo formalism due to the finite amount of trajectory data. In this work, we extend...
Strategies for Molecular Dynamics using Hybrid Systems: LAMMPS Use Case
arXiv:2606.02319v1 Announce Type: new Abstract: The complexity of biomolecular simulations has substantially increased the demand for High-Performance Computing (HPC) infrastructures, particularly in molecular dynamics and coarse-grained modeling. This work presents a systematic performance and scalability analysis of the LAMMPS simulator for coarse-grained biomolecular simulations, using the antimicrobial peptide Tritrpticin (PDB ID: 1D6X) as the experimental workload. Pure MPI and hybrid...
Microscopic origin of polytype-dependent melting in SiC revealed by machine-learning molecular dynamics
arXiv:2606.00403v1 Announce Type: cross Abstract: Predicting how crystal structure influences high-temperature stability remains a key challenge in materials modelling and design. Silicon carbide (SiC), one of the most thermally and chemically stable materials known, provides an ideal system for studying this problem because its many polytypes preserve similar local tetrahedral bonding while differing in long-range stacking geometry. Here, we combine phase-coexistence machine-learning...
Molecular dynamics insights into biomineralisation mediated by acidic intrinsically disordered proteins: a case study of molluscan Aspein from the pearl oyster Pinctada fucata
Biomineralisation is a ubiquitous phenomenon that still fascinates the scientific community. Mineralised structures are most notably encountered in marine lifeforms the shell or exoskeleton of which are formed by precipitating specific calcium carbonate (CaCO3) polymorphs, known as amorphous (ACC), vaterite, aragonite, and calcite. To control crystalline polymorphism in their shell layers, bivalves have evolved strategies involving ion-binding secretomes composed of shell matrix proteins (SMPs).
Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators
Announce Type: replace Abstract: Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-dimensional molecular distributions such as Boltzmann distributions and transition densities. However, conventional MD is fundamentally limited by the high computational cost required to generate independent samples. Generative molecular dynamics (GenMD) has recently emerged as an...
Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators
Announce Type: replace-cross Abstract: Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-dimensional molecular distributions such as Boltzmann distributions and transition densities. However, conventional MD is fundamentally limited by the high computational cost required to generate independent samples. Generative molecular dynamics (GenMD) has recently emerged as...