Cover Image

Machine-Learned Potentials for Solvation Modeling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Banchode, Roopshree, Das, Surajit, Raghunathan, Shampa, Ramakrishnan, Raghunathan
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations
by: Das, Surajit, et al.
Published: (2025)

Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules
by: Tripathy, Susmita, et al.
Published: (2024)

Unlocking Inverted Singlet-Triplet Gap in Alternant Hydrocarbons with Heteroatoms
by: Majumdar, Atreyee, et al.
Published: (2025)

Influence of Pseudo-Jahn-Teller Activity on the Singlet-Triplet Gap of Azaphenalenes
by: Majumdar, Atreyee, et al.
Published: (2024)

A Chemical Space Perspective on Diastereomeric Barriers in Alkylperoxy-to-Hydroperoxyalkyl Isomerization
by: Ramakrishnan, Raghunathan
Published: (2026)

Insights into Symmetry and Substitution Patterns Governing Singlet-Triplet Energy Gap in the Chemical Space of Azaphenalenes
by: Majumdar, Atreyee, et al.
Published: (2025)

Leveraging the Bias-Variance Tradeoff in Quantum Chemistry for Accurate Negative Singlet-Triplet Gap Predictions: A Case for Double-Hybrid DFT
by: Majumdar, Atreyee, et al.
Published: (2025)

Resilience of Hund's rule in the Chemical Space of Small Organic Molecules
by: Majumdar, Atreyee, et al.
Published: (2024)

Assessing excited-state geometry optimization strategies for adiabatic photophysical energies
by: Bera, Amrita, et al.
Published: (2026)

Probabilistic Parallels in the Classical Limit of Quantum Mechanical Models
by: Ramakrishnan, Raghunathan
Published: (2024)

Predicting Spectroscopic Properties of Solvated Nile Red with Automated Workflows for Machine Learned Interatomic Potentials
by: Eller, Jacob, et al.
Published: (2025)

A Machine Learning Model for the Chemistry of a Solvated Electron
by: Gao, Ruiqi, et al.
Published: (2025)

All-atomistic Transferable Neural Potentials for Protein Solvation
by: Dey, Rishabh, et al.
Published: (2026)

Predicting Solvation Free Energies of Molecules and Ions via First-Principles and Machine-Learning Molecular Dynamics
by: Yu, Junting, et al.
Published: (2026)

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential
by: Kumar, Nitesh, et al.
Published: (2026)

Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
by: Ho, Cheuk Hin, et al.
Published: (2025)

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
by: Xia, Junfan, et al.
Published: (2025)

Understanding the Density Maximum of Water with Machine Learned Potentials
by: Song, Yizhi, et al.
Published: (2026)

Transferability of datasets between Machine-Learning Interaction Potentials
by: Niblett, Samuel P., et al.
Published: (2024)

No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
by: Houston, Paul L., et al.
Published: (2024)

MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
by: Wehrhan, Leon, et al.
Published: (2025)

Does Hessian Data Improve the Performance of Machine Learning Potentials?
by: Rodriguez, Austin, et al.
Published: (2025)

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)

Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate
by: Qu, Chen, et al.
Published: (2026)

Machine Learning-Aided First-Principles Calculations of Redox Potentials
by: Jinnouchi, Ryosuke, et al.
Published: (2023)

Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations
by: Chaton, Kham Lek, et al.
Published: (2026)

False Metallization in Short-Ranged Machine Learned Interatomic Potentials
by: Parker, Isaac J., et al.
Published: (2026)

Investigating the Electrochemical Double Layer with Quantum-Chemical Simulations and Implicit Solvation Models
by: Mangiameli, Alessandro, et al.
Published: (2026)

"Gold-Standard" $Δ$-Machine Learned and Transferable Potential for Linear Alkanes
by: Qu, Chen, et al.
Published: (2025)

Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations
by: Hilfiker, Mathias, et al.
Published: (2025)

Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
by: Zhu, Jia-Xin, et al.
Published: (2024)

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
by: Eastman, Peter, et al.
Published: (2026)

Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials
by: Ullah, Arif, et al.
Published: (2024)

DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
by: Midgley, Laurence I., et al.
Published: (2026)

Development of an Optimized Parameter Set for Monovalent Ions in the Reference Interaction Site Model of Solvation
by: Carvalho, Felipe Silva, et al.
Published: (2025)

Machine Learning Potentials for Heterogeneous Catalysis
by: Omranpour, Amir, et al.
Published: (2024)

Pooling Solvent Mixtures for Solvation Free Energy Predictions
by: Leenhouts, Roel J., et al.
Published: (2024)

Providing Machine Learning Potentials with High Quality Uncertainty Estimates
by: Sumer, Zeynep, et al.
Published: (2025)

Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations
by: Eckhoff, Marco, et al.
Published: (2025)

Ab Initio Melting Properties of Water and Ice from Machine Learning Potentials
by: Li, Yifan, et al.
Published: (2025)