FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials DOI
Thomas Plé,

Olivier Adjoua,

Louis Lagardère

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(4)

Published: July 25, 2024

Neural network interatomic potentials (NNPs) have recently proven to be powerful tools accurately model complex molecular systems while bypassing the high numerical cost of ab initio dynamics simulations. In recent years, numerous advances in architectures as well development hybrid models combining machine-learning (ML) with more traditional, physically motivated, force-field interactions considerably increased design space ML potentials. this paper, we present FeNNol, a new library for building, training, and running force-field-enhanced neural It provides flexible modular system building models, allowing us easily combine state-of-the-art embeddings ML-parameterized physical interaction terms without need explicit programming. Furthermore, FeNNol leverages automatic differentiation just-in-time compilation features Jax Python enable fast evaluation NNPs, shrinking performance gap between standard force-fields. This is demonstrated popular ANI-2x reaching simulation speeds nearly on par AMOEBA polarizable commodity GPUs (graphics processing units). We hope that will facilitate application NNP wide range problems.

Language: Английский

TinkerModeller: An Efficient Tool for Building Biological Systems in Tinker Simulations DOI Creative Commons
Xujian Wang,

H. Liu,

Yu Li

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Polarizable force fields advance our understanding of electrostatic interactions in molecular systems; however, their widespread application is limited by the complexity required modeling. We here present TinkerModeller (TKM), a versatile software package designed to streamline construction biological systems Tinker simulation software. The core functionality TKM lies its capacity generate input files for complex and facilitate conversion from classical polarizable fields. With user-friendly, standalone script, provides an intuitive interface that supports users modeling through postanalysis, creating comprehensive platform dynamics simulations within Tinker. Furthermore, includes electric field (EF) postanalysis module, introducing novel approach employs charge methods point approximations efficient internal EF estimation. This module offers computationally low-demand solution high-throughput Our work paves way broader, more accessible use introduces new method estimation, advancing explore effects materials science applications.

Language: Английский

Citations

1
From Ab Initio to Instrumentation: A Field Guide to Characterizing Multivalent Liquid Electrolytes DOI
Glenn Pastel, Travis P. Pollard,

Oleg Borodin

et al.

Chemical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

In this field guide, we outline empirical and theory-based approaches to characterize the fundamental properties of liquid multivalent-ion battery electrolytes, including (i) structure chemistry, (ii) transport, (iii) electrochemical properties. When detailed molecular-scale understanding multivalent electrolyte behavior is insufficient use examples from well-studied lithium-ion electrolytes. recognition that coupling techniques highly effective, but often nontrivial, also highlight recent characterization efforts uncover a more comprehensive nuanced underlying structures, processes, reactions drive performance system-level behavior. We hope insights these discussions will guide design future studies, accelerate development next-generation batteries through modeling with experiments, help avoid pitfalls ensure reproducibility results.

Language: Английский

Citations

1
Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach DOI
Gong Chen, Théo Jaffrelot Inizan, Thomas Plé

et al.

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(13), P. 5558 - 5569

Published: June 14, 2024

Force fields (FFs) are an established tool for simulating large and complex molecular systems. However, parametrizing FFs is a challenging time-consuming task that relies on empirical heuristics, experimental data, computational data. Recent efforts aim to automate the assignment of FF parameters using pre-existing databases on-the-fly

Language: Английский

Citations

7
FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials DOI
Thomas Plé,

Olivier Adjoua,

Louis Lagardère

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(4)

Published: July 25, 2024

Neural network interatomic potentials (NNPs) have recently proven to be powerful tools accurately model complex molecular systems while bypassing the high numerical cost of ab initio dynamics simulations. In recent years, numerous advances in architectures as well development hybrid models combining machine-learning (ML) with more traditional, physically motivated, force-field interactions considerably increased design space ML potentials. this paper, we present FeNNol, a new library for building, training, and running force-field-enhanced neural It provides flexible modular system building models, allowing us easily combine state-of-the-art embeddings ML-parameterized physical interaction terms without need explicit programming. Furthermore, FeNNol leverages automatic differentiation just-in-time compilation features Jax Python enable fast evaluation NNPs, shrinking performance gap between standard force-fields. This is demonstrated popular ANI-2x reaching simulation speeds nearly on par AMOEBA polarizable commodity GPUs (graphics processing units). We hope that will facilitate application NNP wide range problems.

Language: Английский

Citations

5