Cover Image

Molecular Similarity in Machine Learning of Energies in Chemical Reaction Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gugler, Stefan, Reiher, Markus
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Uncertainty-aware First-principles Exploration of Chemical Reaction Networks
by: Bensberg, Moritz, et al.
Published: (2023)

Modal Backflow Neural Quantum States for Anharmonic Vibrational Calculations
by: Ding, Lexin, et al.
Published: (2025)

Automated Microsolvation for Minimum Energy Path Construction in Solution
by: Türtscher, Paul L., et al.
Published: (2025)

Renormalized Internally-Contracted Multireference Coupled Cluster with Perturbative Triples
by: Feldmann, Robin, et al.
Published: (2024)

Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks
by: Müller, Charlotte H., et al.
Published: (2024)

N-Mode Quantized Anharmonic Vibronic Hamiltonians for Matrix Product State Dynamics
by: Barandun, Valentin, et al.
Published: (2025)

After 100 Years of Quantum Mechanics: Toward a Constructive Observation-Centered Perspective
by: Stroschein, Timothy, et al.
Published: (2026)

Trotter Error and Orbital Transformations in Quantum Phase Estimation
by: Kronenberger, Marvin, et al.
Published: (2026)

Utility-scale quantum computational chemistry
by: Castaldo, Davide, et al.
Published: (2026)

Solving intractable chemical problems by tensor decomposition
by: Glaser, Nina, et al.
Published: (2024)

Molecular relaxation by reverse diffusion with time step prediction
by: Kahouli, Khaled, et al.
Published: (2024)

Ground and excited-state energies with analytic errors and short time evolution on a quantum computer
by: Stroschein, Timothy, et al.
Published: (2025)

SCINE -- Software for Chemical Interaction Networks
by: Weymuth, Thomas, et al.
Published: (2024)

Navigating chemical reaction space with a steering wheel
by: Steiner, Miguel, et al.
Published: (2023)

Vibrational Entanglement through the Lens of Quantum Information Measures
by: Glaser, Nina, et al.
Published: (2024)

A New Paradigm for Computational Chemistry
by: Husistein, Raphael T., et al.
Published: (2026)

Neural Quantum States Based on Selected Configurations
by: Solanki, Marco Julian, et al.
Published: (2026)

Efficient Implementation of the Spin-Free Renormalized Internally-Contracted Multireference Coupled Cluster Theory
by: Szenes, Kalman, et al.
Published: (2025)

Classification of electronic structures and state preparation for quantum computation of reaction chemistry
by: Mörchen, Maximilian, et al.
Published: (2024)

Design, Assessment, and Application of Machine Learning Potential Energy Surfaces
by: Andreichev, Valerii, 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)

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

Non-iterative Triples for Transcorrelated Coupled Cluster Theory
by: Mörchen, Maximilian, et al.
Published: (2024)

Striking the Right Balance of Encoding Electron Correlation in the Hamiltonian and the Wavefunction Ansatz
by: Szenes, Kalman, et al.
Published: (2024)

More quantum chemistry with fewer qubits
by: Günther, Jakob, et al.
Published: (2023)

Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning
by: Colglazier, William, et al.
Published: (2025)

Complete Active Space Iterative Coupled Cluster Theory
by: Feldmann, Robin, et al.
Published: (2024)

Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
by: Lal, Ashique, et al.
Published: (2026)

Efficient Machine Learning Approach for Yield Prediction in Chemical Reactions
by: Ghosh, Supratim, et al.
Published: (2025)

Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations – 2
by: Eckhoff, Marco, et al.
Published: (2025)

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
by: Wang, JingChun, et al.
Published: (2025)

A Machine-Learned Symbolic Committor for a Chemical Reaction: Retinal Isomerization
by: Töpfer, Kai, et al.
Published: (2026)

Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
by: Duschatko, Blake R., et al.
Published: (2024)

Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates
by: Csizi, Katja-Sophia, et al.
Published: (2023)

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

Quantum Walks for Chemical Reaction Networks
by: Hariharan, Seenivasan, 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)

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