Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids
Fuente:
arXiv
Saved in:
| Main Authors: | Feng, Wei, Liu, Siyuan, Wang, Hongyi, Mu, Zhenliang, Pu, Zhichen, Han, Xu, Zheng, Tianze, Yang, Zhenze, Wang, Zhi, Gao, Weihao, Cao, Yidan, Yu, Kuang, Gong, Sheng, Yan, Wen |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field
by: Zheng, Tianze, et al.
Published: (2025)
by: Zheng, Tianze, et al.
Published: (2025)
A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes
by: Chen, Junmin, et al.
Published: (2025)
by: Chen, Junmin, et al.
Published: (2025)
Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage
by: Zheng, Tianze, et al.
Published: (2024)
by: Zheng, Tianze, et al.
Published: (2024)
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
by: Kovács, Dávid Péter, et al.
Published: (2023)
by: Kovács, Dávid Péter, et al.
Published: (2023)
Enhancing PySCF-based Quantum Chemistry Simulations with Modern Hardware, Algorithms, and Python Tools
by: Pu, Zhichen, et al.
Published: (2025)
by: Pu, Zhichen, et al.
Published: (2025)
Enhancing GPU-acceleration in the Python-based Simulations of Chemistry Framework
by: Wu, Xiaojie, et al.
Published: (2024)
by: Wu, Xiaojie, et al.
Published: (2024)
Ionic Liquid Molecular Dynamics Simulation with Machine Learning Force Fields: DPMD and MACE
by: Park, Anseong, et al.
Published: (2025)
by: Park, Anseong, et al.
Published: (2025)
Towards A Transferable Acceleration Method for Density Functional Theory
by: Liu, Zhe, et al.
Published: (2025)
by: Liu, Zhe, et al.
Published: (2025)
THEMol dataset: Torsion, Hessian, and Energy of Molecules
by: Liang, Jiashu, et al.
Published: (2026)
by: Liang, Jiashu, et al.
Published: (2026)
Refinement and Performance Benchmark for Range-Separated Water Force Field
by: Gao, Qian, et al.
Published: (2026)
by: Gao, Qian, et al.
Published: (2026)
De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models
by: Yang, Zhenze, et al.
Published: (2023)
by: Yang, Zhenze, et al.
Published: (2023)
Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties
by: Weber, John L., et al.
Published: (2025)
by: Weber, John L., et al.
Published: (2025)
Analytical Excited-State Gradients and Derivative Couplings in TDDFT with Minimal Auxiliary Basis Set Approximation and GPU Acceleration
by: Pu, Zhichen, et al.
Published: (2025)
by: Pu, Zhichen, et al.
Published: (2025)
Artificial Intelligence Driven Workflow for Accelerating Design of Novel Photosensitizers
by: Wang, Hongyi, et al.
Published: (2025)
by: Wang, Hongyi, et al.
Published: (2025)
Machine Learning Force Fields
by: Unke, Oliver T., et al.
Published: (2020)
by: Unke, Oliver T., et al.
Published: (2020)
Towards Improved Quantum Machine Learning for Molecular Force Fields
by: Couzinié, Yannick, et al.
Published: (2025)
by: Couzinié, Yannick, et al.
Published: (2025)
Molecular Dynamics Force Field Genetic Optimization for Tri-n-butyl Phosphate Liquid
by: Hatami, Faranak, et al.
Published: (2026)
by: Hatami, Faranak, et al.
Published: (2026)
Thermal Conductivity of Metastable Ionic Liquid [$C_{2}mim$][$CH_{3}SO_{3}$]
by: Lozano-Martín, Daniel, et al.
Published: (2024)
by: Lozano-Martín, Daniel, et al.
Published: (2024)
Dataset Distillation for Machine Learning Force Field in Phase Transition Regime
by: Chen, Ruiyang, et al.
Published: (2026)
by: Chen, Ruiyang, et al.
Published: (2026)
TDDFT Gradients and Nonadiabatic Couplings with Minimal Auxiliary Basis Set Approximation for Fewest-Switches Surface Hopping Dynamics
by: Fan, Cheng, et al.
Published: (2026)
by: Fan, Cheng, et al.
Published: (2026)
SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles
by: Simm, Gregor N. C., et al.
Published: (2025)
by: Simm, Gregor N. C., et al.
Published: (2025)
A predictive machine learning force field framework for liquid electrolyte development
by: Gong, Sheng, et al.
Published: (2024)
by: Gong, Sheng, et al.
Published: (2024)
Ensemble Learning of Machine Learning Force Fields
by: Yin, Bangchen, et al.
Published: (2024)
by: Yin, Bangchen, et al.
Published: (2024)
Quantum-Accurate Conformational Stabilities and Vibrational Dynamics in Molecules and Proteins with Machine-Learned Force Fields
by: Suárez-Dou, Sergio, et al.
Published: (2026)
by: Suárez-Dou, Sergio, et al.
Published: (2026)
Grappa -- A Machine Learned Molecular Mechanics Force Field
by: Seute, Leif, et al.
Published: (2024)
by: Seute, Leif, et al.
Published: (2024)
aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields
by: Henkes, Tobias, et al.
Published: (2025)
by: Henkes, Tobias, et al.
Published: (2025)
Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules
by: Kabylda, Adil, et al.
Published: (2022)
by: Kabylda, Adil, et al.
Published: (2022)
Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models
by: Qaisrani, Muhammad Nawaz, et al.
Published: (2025)
by: Qaisrani, Muhammad Nawaz, et al.
Published: (2025)
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields
by: Shao, Shihao, et al.
Published: (2024)
by: Shao, Shihao, et al.
Published: (2024)
Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing
by: Wang, Chu, et al.
Published: (2026)
by: Wang, Chu, et al.
Published: (2026)
Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning
by: Sahrmann, Patrick G., et al.
Published: (2026)
by: Sahrmann, Patrick G., et al.
Published: (2026)
Knowledge Distillation of Noisy Force Labels for Improved Coarse-Grained Force Fields
by: Olowookere, Feranmi V., et al.
Published: (2025)
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)
by: Plé, Thomas, et al.
Published: (2022)
$Δ$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
by: Nandi, Apurba, et al.
Published: (2024)
by: Nandi, Apurba, et al.
Published: (2024)
Improved Treatment of 1-4 interactions in Force Fields for Molecular Dynamics Simulations
by: Abdullah, Aalim S., et al.
Published: (2025)
by: Abdullah, Aalim S., et al.
Published: (2025)
Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
by: Xin, Zaizhou, et al.
Published: (2025)
by: Xin, Zaizhou, et al.
Published: (2025)
A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model
by: Khajeh, Arash, et al.
Published: (2023)
by: Khajeh, Arash, et al.
Published: (2023)
Probing the Temporal Response of Liquid Water to a THz Pump Pulse Using Machine Learning-Accelerated Non-Equilibrium Molecular Dynamics
by: Joll, Kit, et al.
Published: (2025)
by: Joll, Kit, et al.
Published: (2025)
Towards Quantitative Interpretation of 3D Atomic Force Microscopy at Solid-Liquid Interfaces
by: Ai, Qian, et al.
Published: (2025)
by: Ai, Qian, et al.
Published: (2025)
Enhanced Ionic Conductivity of confined Ionic-Liquid in Angstrom-scale 2D channels
by: Yang, Jing, et al.
Published: (2026)
by: Yang, Jing, et al.
Published: (2026)
Similar Items
-
Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field
by: Zheng, Tianze, et al.
Published: (2025) -
A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes
by: Chen, Junmin, et al.
Published: (2025) -
Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage
by: Zheng, Tianze, et al.
Published: (2024) -
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
by: Kovács, Dávid Péter, et al.
Published: (2023) -
Enhancing PySCF-based Quantum Chemistry Simulations with Modern Hardware, Algorithms, and Python Tools
by: Pu, Zhichen, et al.
Published: (2025)