:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Colglazier, William, Lubbers, Nicholas, Tretiak, Sergei, Niklasson, Anders M. N., Kulichenko, Maksim
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Computational Physics
Online Access:	https://arxiv.org/abs/2509.22435
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PySEQM 2.0: Accelerated Semiempirical Excited State Calculations on Graphical Processing Units
by: Athavale, Vishikh, et al.
Published: (2025)

Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
by: Li, Xinyang, et al.
Published: (2023)

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks
by: Zubatyuk, Tetiana, et al.
Published: (2019)

Semiclassical Nonadiabatic Molecular Dynamics for Molecular Exciton-Polaritons
by: Li, Xinyang, et al.
Published: (2024)

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
by: Allen, Alice E. A., et al.
Published: (2025)

Shadow Molecular Dynamics for a Charge-Potential Equilibration Model
by: Stanton, Robert, et al.
Published: (2025)

Data Mining and Computational Screening of Rashba-Dresselhaus Splitting and Optoelectronic Properties in Two-Dimensional Perovskite Materials
by: Stanton, Robert, et al.
Published: (2025)

Shadow Molecular Dynamics for Flexible Multipole Models
by: Grove, Rae A. Corrigan, et al.
Published: (2025)

Spin-Flip Configuration Interaction for Strong Static Correlation in Quantum Electrodynamics
by: Weight, Braden M., et al.
Published: (2026)

Meta-Learning Linear Models for Molecular Property Prediction
by: Pimonova, Yulia, et al.
Published: (2025)

Modeling Reactions on the Solid-Liquid Interface With Next Generation Extended Lagrangian Quantum-Based Molecular Dynamics
by: Grove, Rae A. Corrigan, et al.
Published: (2025)

Optimal Invariant Bases for Atomistic Machine Learning
by: Allen, Alice E. A., et al.
Published: (2025)

Shadow excited state molecular dynamics with the \DeltaSCF method
by: Fajen, O. Jonathan, et al.
Published: (2025)

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
by: Matin, Sakib, et al.
Published: (2025)

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

Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
by: Messerly, Mitchell, et al.
Published: (2025)

GPU-Accelerated Charge-Equilibration for Shadow Molecular Dynamics in Python
by: Kaymak, Mehmet Cagri, et al.
Published: (2025)

Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning
by: Han, Mingjun, et al.
Published: (2024)

Molecular Similarity in Machine Learning of Energies in Chemical Reaction Networks
by: Gugler, Stefan, et al.
Published: (2025)

Optical Cycling and Sensitivity to the Electron's Electric Dipole Moment in Gold-Containing Molecules
by: Stuntz, K. Cooper, et al.
Published: (2024)

Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
by: Murdeshwar, Sanya, et al.
Published: (2026)

Spin-Orbit Induced Non-Adiabatic Dynamics: An Exact $Ω$-Representation
by: Brady, Ryan P., et al.
Published: (2026)

Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
by: Matin, Sakib, et al.
Published: (2025)

Predicting The One-Particle Density Matrix With Machine Learning
by: Hazra, S., et al.
Published: (2024)

Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials
by: Rodriguez, Austin, et al.
Published: (2026)

Toward Reliable Dipole Moments without Single Excitations: The Role of Orbital Rotations and Dynamical Correlation
by: Chakraborty, Rahul, et al.
Published: (2024)

Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies
by: Bensberg, Moritz, et al.
Published: (2025)

Revolutionising Antibacterial Warfare: Machine Learning and Molecular Dynamics Unveiling Potential Gram-Negative Bacteria Inhibitors
by: Joshi, Pritish, et al.
Published: (2025)

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials
by: Cui, Taoyong, et al.
Published: (2025)

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids
by: Feng, Wei, et al.
Published: (2025)

Grappa -- A Machine Learned Molecular Mechanics Force Field
by: Seute, Leif, et al.
Published: (2024)

Knowledge Distillation of Noisy Force Labels for Improved Coarse-Grained Force Fields
by: Olowookere, Feranmi V., et al.
Published: (2025)

Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: from Force Fields to Machine Learning Potentials
by: Plé, Thomas, et al.
Published: (2022)

Performing Path Integral Molecular Dynamics Using Artificial Intelligence Enhanced Molecular Simulation Framework
by: Fan, Cheng, et al.
Published: (2025)

Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
by: Goodwin, Zachary A. H., et al.
Published: (2024)

Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings
by: Ghosh, Ayana, et al.
Published: (2024)

Machine-Learning Electron Dynamics with Moment Propagation Theory: Application to Optical Absorption Spectrum Computation using Real-Time TDDFT
by: Boyer, Nicholas J., et al.
Published: (2024)

Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids
by: Wagner, Jens, et al.
Published: (2026)

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

Machine Learning Wavefunction
by: Battaglia, Stefano
Published: (2022)