:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Battaglia, Stefano
Format:	Preprint
Published:	2022
Subjects:	Chemical Physics Computational Physics
Online Access:	https://arxiv.org/abs/2202.13916
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique
by: Nakano, Kousuke, et al.
Published: (2025)

Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction
by: Li, Weitang, et al.
Published: (2025)

FragPT2: Multi-Fragment Wavefunction Embedding with Perturbative Interactions
by: Koridon, Emiel, et al.
Published: (2024)

Singlet Geminal Wavefunctions
by: Johnson, Paul A., et al.
Published: (2024)

Compactifying Electronic Wavefunctions I: Error-Mitigated Transcorrelated DMRG
by: Araújo, Bruna G. M., et al.
Published: (2025)

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

Understanding Discrepancies of Wavefunction Theories for Large Molecules
by: Schäfer, Tobias, et al.
Published: (2024)

Atom-Density Representations for Machine Learning
by: Willatt, Michael J., et al.
Published: (2018)

Stochastic Representation of Time-Evolving Neural Network-based Wavefunctions
by: Huang, Bizi, et al.
Published: (2025)

QMCTorch: Molecular Wavefunctions with Neural Components for Energy and Force Calculations
by: Renaud, Nicolas
Published: (2025)

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

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

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

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

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)

Neural Wavefunction Calculations of μSR Spectra with Quantum Muons and Protons
by: Carr, Jamie, et al.
Published: (2026)

MetaWave: A Platform for Unified Implementation of Nonrelativistic and Relativistic Wavefunctions
by: Zhang, Ning, et al.
Published: (2025)

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

Compactifying the Electronic Wavefunction II: Quantum Estimators for Spin-Coupled Generalized Valence Bond Wavefunctions Applied to H4
by: Gabrielly, Bruna
Published: (2026)

QCMaquis 4.0: Multi-Purpose Electronic, Vibrational, and Vibronic Structure and Dynamics Calculations with the Density Matrix Renormalization Group
by: Szenes, Kalman, et al.
Published: (2025)

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids
by: Feng, Wei, 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)

Application of Correlated-Wavefunction and Density-Functional Theories to Endofullerenes: A Cautionary Tale
by: Panchagnula, K., et al.
Published: (2025)

Time Evolution of ML-MCTDH Wavefunctions II: Application of the Projector Splitting Integrator
by: Lindoy, Lachlan P., et al.
Published: (2021)

Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
by: Thiede, Luca, et al.
Published: (2026)

Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables
by: Trizio, Enrico, et al.
Published: (2024)

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

Unbiasing Fermionic Auxiliary-Field Quantum Monte Carlo with Matrix Product State Trial Wavefunctions
by: Jiang, Tong, et al.
Published: (2024)

Time Evolution of ML-MCTDH Wavefunctions I: Gauge Conditions, Basis Functions, and Singularities
by: Lindoy, Lachlan P., et al.
Published: (2021)

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

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

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

Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion-Pairing
by: Bian, Xuezhi, et al.
Published: (2026)

Spin-Adapted Neural Network Wavefunctions in Real Space
by: Li, Ruichen, 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)

Addressing the Band Gap Problem with a Machine-Learned Exchange Functional
by: Bystrom, Kyle, et al.
Published: (2024)

Bounds on a Wavefunction Overlap with Hamiltonian Eigen-states: Performance Guarantees for the Quantum Phase Estimation Algorithm
by: Lin, Junan, et al.
Published: (2025)

Size Extensive Auxiliary-Field Quantum Monte Carlo with Perturbative Coupled Cluster Trial Wavefunction
by: Zhang, Yichi, et al.
Published: (2026)