ArticleThe journal of physical chemistry letters2025
Alchemical Free-Energy Calculations at Quantum-Chemical Precision.
Article in The journal of physical chemistry letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Buffer Region Embedding for Hybrid Machine-Learned/Molecular-Mechanical Simulations in Complex Environments.Journal of chemical information and modeling · 2026Article
- Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- How to Use Quantum Computers for Biomolecular Free Energies.Journal of chemical theory and computation · 2026Article
- Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.Journal of chemical theory and computation · 2026Article
- Data-Efficient Equivariant NNPs Enable DFT-Accurate Simulations and Implicit Solvation Free Energies.The journal of physical chemistry. B · 2025Article
- Transferable Neural Network Potentials and Condensed Phase Properties.Journal of chemical information and modeling · 2025Article
- Efficient Multistate Free-Energy Calculations with QM/MM Accuracy Using Replica-Exchange Enveloping Distribution Sampling.The journal of physical chemistry. B · 2025Article
- QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past decade, machine-learned potentials (MLP) have demonstrated the capability to predict various QM properties learned from a set of reference QM calculations. Accordingly, hybrid QM/MM simulations can be accelerated by replacement of expensive QM calculations with efficient MLP energy predictions. At the same time, alchemical free-energy perturbations (FEP) remain unachievable at the QM level of theory. In this work, we extend the capabilities of the Buffer Region Neural Network (BuRNN) QM/MM scheme toward FEP. BuRNN introduces a buffer region that experiences full electronic polarization by the QM region to minimize artifacts at the QM/MM interface. An MLP is used to predict the energies for the QM region and its interactions with the buffer region. Furthermore, BuRNN allows us to implement FEP directly into the MLP Hamiltonian. Here, we describe the alchemical change from methanol to methane in water at the MLP/MM level as a proof of concept.
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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.