Cover Image

FlashMD: long-stride, universal prediction of molecular dynamics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bigi, Filippo, Chong, Sanggyu, Kristiadi, Agustinus, Ceriotti, Michele
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning the action for long-time-step simulations of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)

The dark side of the forces: assessing non-conservative force models for atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2024)

Representing spherical tensors with scalar-based machine-learning models
by: Domina, Michelangelo, et al.
Published: (2025)

A prediction rigidity formalism for low-cost uncertainties in trained neural networks
by: Bigi, Filippo, et al.
Published: (2024)

Prediction rigidities for data-driven chemistry
by: Chong, Sanggyu, et al.
Published: (2024)

Pushing the limits of unconstrained machine-learned interatomic potentials
by: Bigi, Filippo, et al.
Published: (2026)

Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)

A universal machine learning model for the electronic density of states
by: How, Wei Bin, et al.
Published: (2025)

How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)

PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
by: Mazitov, Arslan, et al.
Published: (2025)

Probing the effects of broken symmetries in machine learning
by: Langer, Marcel F., et al.
Published: (2024)

Introduction to the Analysis of Probabilistic Decision-Making Algorithms
by: Kristiadi, Agustinus
Published: (2025)

Completeness of Atomic Structure Representations
by: Nigam, Jigyasa, et al.
Published: (2023)

High-quality, high-information datasets for universal atomistic machine learning
by: Malosso, Cesare, et al.
Published: (2026)

Adaptive energy reference for machine-learning models of the electronic density of states
by: How, Wei Bin, et al.
Published: (2024)

Smooth, exact rotational symmetrization for deep learning on point clouds
by: Pozdnyakov, Sergey N., et al.
Published: (2023)

Tabular foundation models for in-context prediction of molecular properties
by: Hicham, Karim K. Ben, et al.
Published: (2026)

Robustness of Local Predictions in Atomistic Machine Learning Models
by: Chong, Sanggyu, et al.
Published: (2023)

Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2025)

Multitask methods for predicting molecular properties from heterogeneous data
by: Fisher, Katharine, et al.
Published: (2024)

Motional representation; the ability to predict odor characters using molecular vibrations
by: Harada, Yuki, et al.
Published: (2025)

Enhancing molecular dynamics with equivariant machine-learned densities
by: Bogojeski, Mihail, et al.
Published: (2026)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)

Uncertainty quantification by direct propagation of shallow ensembles
by: Kellner, Matthias, et al.
Published: (2024)

On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective
by: Wenger, Jonathan, et al.
Published: (2023)

Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
by: Maheshwari, Shagun, et al.
Published: (2025)

The impact of conformer quality on learned representations of molecular conformer ensembles
by: Adams, Keir, et al.
Published: (2025)

Challenging reaction prediction models to generalize to novel chemistry
by: Bradshaw, John, et al.
Published: (2025)

Scalable Autoregressive 3D Molecule Generation
by: Cheng, Austin H., et al.
Published: (2025)

ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics
by: Viswanath, Siddharth, et al.
Published: (2024)

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
by: Schaaf, Lars L., et al.
Published: (2024)

Integer linear programming for unsupervised training set selection in molecular machine learning
by: Haeberle, Matthieu, et al.
Published: (2024)

DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
by: Pavšek, Jan, et al.
Published: (2025)

Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
by: Mao, Yun-Wen, et al.
Published: (2023)

Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
by: Williams, Nicholas J., et al.
Published: (2024)

Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
by: Zaverkin, Viktor, et al.
Published: (2025)

Limits of PRM-Guided Tree Search for Mathematical Reasoning with LLMs
by: Cinquin, Tristan, et al.
Published: (2025)

Graph Neural Networks embedded into Margules model for vapor-liquid equilibria prediction
by: Medina, Edgar Ivan Sanchez, et al.
Published: (2025)

Leap: molecular synthesisability scoring with intermediates
by: Calvi, Antonia, et al.
Published: (2024)