ArticleThe journal of physical chemistry. B2024
Software Infrastructure for Next-Generation QM/MM-ΔMLP Force Fields.
Article in The journal of physical chemistry. B, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- QM/MM Reveals Coordination Shift behind Early Lanthanide Preference in Methanol Dehydrogenase XoxF.The journal of physical chemistry. B · 2026Article
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Origins of Reactivity in SAM-Utilizing Ribozyme SAMURI-Catalyzed RNA Alkylation.Journal of the American Chemical Society · 2026Article
- Quantum-Centric Alchemical Free Energy Calculations.Journal of chemical theory and computation · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- How to Use Quantum Computers for Biomolecular Free Energies.Journal of chemical theory and computation · 2026Article
- Accelerating Quantum Mechanical/Molecular Mechanical (QM/MM) Simulations with Stochastic Iso-Kinetic Nosé-Hoover Multiple Time Step Integrator in CHARMM.The journal of physical chemistry. B · 2026Article
- Hydration-Controlled Proton Transport in Respiratory Complex I.Journal of the American Chemical Society · 2026Article
- Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization.Journal of chemical information and modeling · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Density-Functionalized QM/MM Delivers Chemical Accuracy For Solvated Systems.Journal of chemical theory and computation · 2025Article
- Toward Improving Multiple Time Step QM/MM Simulations with Δ-Machine Learning.The journal of physical chemistry. B · 2025Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.Journal of chemical theory and computation · 2025Article
- Recent Developments in Amber Biomolecular Simulations.Journal of chemical information and modeling · 2025Article
- FE-ToolKit: A Versatile Software Suite for Analysis of High-Dimensional Free Energy Surfaces and Alchemical Free Energy Networks.Journal of chemical information and modeling · 2025Article
- Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.The journal of physical chemistry. B · 2025Article
- DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials.Journal of chemical theory and computation · 2025Article
- The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions.Scientific data · 2025Article
- DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We present software infrastructure for the design and testing of new quantum mechanical/molecular mechanical and machine-learning potential (QM/MM-ΔMLP) force fields for a wide range of applications. The software integrates Amber's molecular dynamics simulation capabilities with fast, approximate quantum models in the xtb package and machine-learning potential corrections in DeePMD-kit. The xtb package implements the recently developed density-functional tight-binding QM models with multipolar electrostatics and density-dependent dispersion (GFN2-xTB), and the interface with Amber enables their use in periodic boundary QM/MM simulations with linear-scaling QM/MM particle-mesh Ewald electrostatics. The accuracy of the semiempirical models is enhanced by including machine-learning correction potentials (ΔMLPs) enabled through an interface with the DeePMD-kit software. The goal of this paper is to present and validate the implementation of this software infrastructure in molecular dynamics and free energy simulations. The utility of the new infrastructure is demonstrated in proof-of-concept example applications. The software elements presented here are open source and freely available. Their interface provides a powerful enabling technology for the design of new QM/MM-ΔMLP models for studying a wide range of problems, including biomolecular reactivity and protein-ligand binding.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.