Cover Image

Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chahal, Rajni, Gibson, Luke D, Roy, Santanu, Bryantsev, Vyacheslav S
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

Uncertainty and Exploration of Deep Learning-based Atomistic Models for Screening Molten Salt Properties and Compositions
by: Lam, Stephen T., et al.
Published: (2024)

Best Practices for Fitting Machine Learning Interatomic Potentials for Molten Salts: A Case Study Using NaCl-MgCl2
by: Attarian, Siamak, et al.
Published: (2024)

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials
by: Chahal-Crockett, Rajni, et al.
Published: (2026)

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

Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
by: Ho, Cheuk Hin, et al.
Published: (2025)

Study of Vapor–Liquid–Liquid Equilibrium Changes of Quaternary Mixtures: Theory and Practice
by: Agnes Szanyi, et al.
Published: (2025)

MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
by: Wehrhan, Leon, et al.
Published: (2025)

False Metallization in Short-Ranged Machine Learned Interatomic Potentials
by: Parker, Isaac J., et al.
Published: (2026)

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)

Molecular interaction volume model of mixing enthalpy for molten salt system: An integrated calorimetry-model case study of LaCl$_3$-(LiCl-KCl)
by: Goncharov, Vitaliy G., et al.
Published: (2024)

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

Machine-Learning Interatomic Potentials for Long-Range Systems
by: Ji, Yajie, et al.
Published: (2025)

DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
by: Midgley, Laurence I., et al.
Published: (2026)

Structure of Molten FeCl2 and FeCl3
by: Bhuiyan, Fakhrul Hasan, et al.
Published: (2026)

Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials
by: Zhang, Yaolong, et al.
Published: (2025)

Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
by: Schäfer, Moritz René, et al.
Published: (2025)

Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins
by: Zeng, Lejia, et al.
Published: (2026)

Reliable and Efficient Automated Transition-State Searches with Machine-Learned Interatomic Potentials
by: Marks, Jonah, et al.
Published: (2026)

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
by: Liu, Yuzhi, et al.
Published: (2026)

Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems
by: Hooven, Natalie E., et al.
Published: (2025)

Enhanced Representation-Based Sampling for the Efficient Generation of Datasets for Machine-Learned Interatomic Potentials
by: Schäfer, Moritz René, et al.
Published: (2026)

MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
by: Osaro, Etinosa, et al.
Published: (2026)

Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
by: Baldwin, William J., et al.
Published: (2026)

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, 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)

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
by: Brunken, Christoph, et al.
Published: (2025)

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

Mass Transfer Modeling of CsCl During Crystallization of Molten LiCl‐KCl‐CsCl Salt Mixture
by: Sujish Divakaran, et al.
Published: (2024)

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
by: Fu, Cong, et al.
Published: (2025)

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, et al.
Published: (2026)

Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
by: Maxson, Tristan, et al.
Published: (2024)

Deep Learning Interatomic Potential Connects Molecular Structural Ordering to Macroscale Properties of Polyacrylonitrile (PAN) Polymer
by: Chahal, Rajni, et al.
Published: (2024)

Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
by: Edwards, Connor W., et al.
Published: (2026)

Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials
by: Rodriguez, Austin, 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)

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate
by: Soyemi, Ademola, et al.
Published: (2025)

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials
by: Dutta, Soumajit, et al.
Published: (2025)

Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration
by: Xu, Jiayan, et al.
Published: (2025)

Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
by: Dominguez, Jon Eunan Quinlivan, et al.
Published: (2025)

Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
by: Pelletier, Vivienne, et al.
Published: (2026)