Atomistic
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Minimization of disorder as a key design principle for natural sizes of light harvesting 2 complexes
arXiv:2606.10103v1 Announce Type: new Abstract: The light harvesting 2 (LH2) complex of purple bacteria has excellent energy conversion efficiency. Clarifying the design principle behind such efficiency at the atomistic level is crucial for understanding its structure-function relationship, and can be utilized for the design of artificial light harvesting systems. To this end, we conducted comprehensive computational investigation of the dynamical and statistical nature of electronic excited...
Non-Additive Ion Effects on the Coil-Globule Equilibrium of a Generic Polymer in Aqueous Salt Solutions
Announce Type: replace-cross Abstract: Mixtures of weakly and strongly hydrated anions induce non-additive changes in the LCST of thermoresponsive polymers such as PNIPAM and PEO. Large-scale atomistic simulations of PNIPAM--NaI--Na$_{2}$SO$_{4}$ mixtures have shown that these effects arise from the interplay between favorable PNIPAM--iodide interactions and depletion of strongly hydrated sulfate ions. Here, we investigate whether chemically specific polymer--anion interactions are necessary...
Autonomous computational catalysis through an agentic research system
arXiv:2601.13508v4 Announce Type: replace-cross Abstract: Autonomous agents are beginning to transform scientific research from tool-assisted workflows toward self-sustaining discovery processes. Computational catalysis provides a representative challenge, as catalyst discovery requires high-level questions to be translated into coordinated model construction, atomistic simulation, mechanistic analysis, and iterative design across multiple scales. Here we introduce CatMaster, a...
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...
Beyond Gaussian Statistics in Polymer Melts: Statistical Masking of Persistent Local Constraints
arXiv:2605.25989v2 Announce Type: replace-cross Abstract: Short polymer chains exhibit clear deviations from Gaussian end-to-end distance statistics, yet the molecular mechanism by which Gaussian behavior is recovered in long chains remains unestablished. Atomistic molecular dynamics simulations of polyethylene melts reveal that conformational heterogeneity persists at the Kuhn scale across all chain lengths, consisting of a mosaic of slow-relaxing, extended aligned chain segments (ACS) and...
E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory
arXiv:2601.16622v2 Announce Type: replace Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectures face critical scalability bottlenecks due to the explicit construction of geometric features or dense tensor products on \textit{every} edge. To overcome this, we introduce \textbf{E2Former-V2}, a scalable architecture that integrates algebraic sparsity with hardware-aware execution.
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...
Coupled simulation of plasma-surface interactions during early stages of vacuum arcing
arXiv:2606.05893v1 Announce Type: new Abstract: We describe fully coupled simulations that bridge atomistic cathode dynamics and plasma formation during the earliest stages of vacuum arcing. The model combines molecular dynamics, finite element electrothermal calculations, electron emission and particle-in-cell plasma simulations via dynamic transfer of particles between the surface and plasma domains. Simulations of Cu nanoprotrusions reveal two routes to thermal runaway: direct Joule...
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
arXiv:2606.03232v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new chemical systems requires expensive retraining of foundation models. Inspired by model merging in vision and language processing, we introduce GFFMERGE, the first principled framework for closed-form model merging in GNNs. We exploit the linear structure of...
Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials
arXiv:2606.05127v1 Announce Type: new Abstract: Foundation models in atomistic machine learning encode interaction physics across diverse atomic environments, but whether that structure can be transferred when building specialist potentials at quantum-chemical accuracy remains open. Here we show that knowledge distillation from a pretrained universal machine-learning interatomic potential (MLIP), followed by coupled-cluster fine-tuning with single and double excitations and perturbative...