ArticleJournal of chemical theory and computation2023
Electrostatic Embedding of Machine Learning Potentials.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
21 citing papers in PubMed, 39 citations in OpenAlex.
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Evaluating Mechanical-Embedding ML/MM for Predicting Mutation Effects in Chorismate Mutase Catalysis.Journal of chemical theory and computation · 2026Article
- Accurate and Time-Efficient Condensed-Phase Free Energy Simulations with Reaction Specific Δ-Machine Learning Potentials in CHARMM.Journal of chemical theory and computation · 2026Article
- Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.Journal of the American Chemical Society · 2026Article
- Simulating enzyme catalysis with electrostatically embedded machine learning potentials.Chemical science · 2026Article
- Computing Solvation Free Energies of Small Molecules with Experimental Accuracy.Journal of the American Chemical Society · 2026Article
- Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.Journal of chemical theory and computation · 2026Article
- Density-Functionalized QM/MM Delivers Chemical Accuracy For Solvated Systems.Journal of chemical theory and computation · 2025Article
- Unlocking the chemistry facilitated by enzymes that process nucleic acids using quantum mechanical and combined quantum mechanics-molecular mechanics techniques.Pure and applied chemistry. Chimie pure et appliquee · 2025Review
- Accurate Free Energy Calculation via Multiscale Simulations Driven by Hybrid Machine Learning and Molecular Mechanics Potentials.Journal of chemical theory and computation · 2025Article
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
- Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.The journal of physical chemistry. B · 2025Article
- Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.Journal of chemical theory and computation · 2025Article
- Improved Description of Environment and Vibronic Effects with Electrostatically Embedded ML Potentials.The journal of physical chemistry letters · 2025Article
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- Machine learning the electric field response of condensed phase systems using perturbed neural network potentials.Nature communications · 2024Article
- Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.The journal of physical chemistry. B · 2024Article
- Article
- Training machine learning potentials for reactive systems: A Colab tutorial on basic models.Journal of computational chemistry · 2024Article
- Electrostatics as a Guiding Principle in Understanding and Designing Enzymes.Journal of chemical theory and computation · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work presents a variant of an electrostatic embedding scheme that allows the embedding of arbitrary machine learned potentials trained on molecular systems
Identifiers
What OpenQuestion holds
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.