arXiv (All)
open access
Predicting Solvation Free Energies of Molecules and Ions via First-Principles and Machine-Learning Molecular Dynamics
The solvation free energy (SFE) of molecules and ions is a fundamental property governing their solvation behavior and solubility. Molecular simulations offer a route to compute SFEs using alchemical free energy methods, such as thermodynamic integration or free energy perturbation. However, these m
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Chemical intuition on bond-dissociation energies as an emergent ability of universal machine-learning interatomic potentials.#449872
- Machine learning kinetics from molecular dynamics data#1011460
- Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning.#25642
- Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics#1115545
- ESPResSo++: A Fast and Extensible Molecular Simulation Package for Coarse-Grained Models#1049953
Record · ID 1187374
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive