Cover Image

Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ullah, Arif, Chen, Yuxinxin, Dral, Pavlo O.
Format:	Preprint
Published:	2024
Subjects:	Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space
by: Ge, Fuchun, et al.
Published: (2025)

All-in-one foundational models learning across quantum chemical levels
by: Chen, Yuxinxin, et al.
Published: (2024)

QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets
by: Ullah, Arif, et al.
Published: (2023)

MLQD: A package for machine learning-based quantum dissipative dynamics
by: Ullah, Arif, et al.
Published: (2023)

Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics
by: Ullah, Arif, et al.
Published: (2024)

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations
by: Hu, Jinming, et al.
Published: (2025)

AI-enhanced on-the-fly simulation of nonlinear time-resolved spectra
by: Pios, Sebastian V., et al.
Published: (2024)

Accelerated Surface Hopping via Scaling the Spin--Orbit Coupling: Opportunities for Machine Learning
by: Martinka, Jakub, et al.
Published: (2026)

Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics
by: Martinka, Jakub, et al.
Published: (2025)

MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods
by: Zhang, Lina, et al.
Published: (2024)

Toward Quantum-Aware Machine Learning: Improved Prediction of Quantum Dissipative Dynamics via Complex Valued Neural Networks
by: Atif, Muhammad, et al.
Published: (2026)

Machine learning meets $\mathfrak{su}(n)$ Lie algebra: Enhancing quantum dynamics learning with exact trace conservation
by: Ullah, Arif, et al.
Published: (2025)

A simple approach to rotationally invariant machine learning of avector quantity
by: Martinka, Jakub, et al.
Published: (2024)

Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
by: Golub, Pavlo, et al.
Published: (2024)

A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
by: Martinka, Jakub, et al.
Published: (2025)

From short-sighted to far-sighted: A comparative study of recursive machine learning approaches for open quantum systems
by: Ullah, Arif
Published: (2025)

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
by: Dral, Pavlo O., et al.
Published: (2023)

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics
by: Andreichev, Valerii, 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)

Physics-informed active learning for accelerating quantum chemical simulations
by: Hou, Yi-Fan, et al.
Published: (2024)

Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
by: Ge, Fuchun, et al.
Published: (2024)

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

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

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

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

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

Molecular Machine Learning in Chemical Process Design
by: Rittig, Jan G., et al.
Published: (2025)

A Vector-Based Algorithm for Generating Complete Balanced Reaction Sets with Arbitrary Numbers of Reagents
by: Yilmaz, Nataliia, 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)

Vector-Based Approach to the Stoichiometric Analysis of Multicomponent Chemical Reactions: The Case of Black Powder
by: Kozub, Pavlo, et al.
Published: (2025)

Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets
by: Barrett, Rhyan, et al.
Published: (2025)

Fault-tolerant Quantum Chemical Calculations with Improved Machine-Learning Models
by: Yuan, Kai, et al.
Published: (2024)

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

Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics
by: Bianchi, Michele Giovanni, et al.
Published: (2025)

Towards Improved Quantum Machine Learning for Molecular Force Fields
by: Couzinié, Yannick, et al.
Published: (2025)

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
by: Brunken, Christoph, et al.
Published: (2026)

The Quantum Method of Planes -- Local Pressure Definitions for Machine Learning Potentials
by: Smith, E. R.
Published: (2025)

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
by: Sanocki, Michal, et al.
Published: (2025)

Impact of Local Descriptors Derived from Machine Learning Potentials in Graph Neural Networks for Molecular Property Prediction
by: Uchiyama, Ryoichi, et al.
Published: (2026)

Machine-Learned Potentials for Solvation Modeling
by: Banchode, Roopshree, et al.
Published: (2025)