Cover Image

Melting Behavior and Phase Stability of CaO from Neural Network Potentials: a Molecular Dynamics Study

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Menescardi, Francesca, de Gironcoli, Stefano
Format:	Preprint
Published:	2026
Subjects:	Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dataset for Melting Behavior and Phase Stability of CaO from Neural Network Potentials
by: Menescardi, Francesca
Published: (2026)

Melting behavior of CaO at high temperature and pressure: a molecular dynamics study
by: Menescardi, Francesca, et al.
Published: (2024)

Molecular simulations of Perovskites CsXI3 (X = Pb,Sn) Using Machine-Learning Interatomic Potentials
by: Ebrahimi, Atefe, et al.
Published: (2025)

Long-range electrostatics in atomistic machine learning: a physical perspective
by: Grasselli, Federico, et al.
Published: (2026)

First-principles study of defects and doping limits in CaO
by: Yuan, Zhenkun, et al.
Published: (2024)

Convergence of Body-Orders in Linear Atomic Cluster Expansions
by: Tan, Apolinario Miguel, et al.
Published: (2025)

Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics
by: Xie, Yu, et al.
Published: (2025)

LATTE: an atomic environment descriptor based on Cartesian tensor contractions
by: Pellegrini, Franco, et al.
Published: (2024)

Influence of CaO and SiO2 additives on the sintering behavior of Cr,Ca:YAG ceramics prepared by solid-state reaction sintering
by: Chaika, . A., et al.
Published: (2026)

Discovery of Novel Silicon Allotropes with Optimized Band Gaps to Enhance Solar Cell Efficiency through Evolutionary Algorithms and Machine Learning
by: Yaghoobi, Mostafa, et al.
Published: (2024)

Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
by: Park, Yutack, et al.
Published: (2024)

Molecular Dynamics Simulations of SrTiO$_3$ with Oxygen Vacancies using Neural Network Potentials
by: Nishiguchi, Kazutaka, et al.
Published: (2025)

Hydrothermally-Assisted Sintering of Calcium Hydroxide Sputtering Targets: A Route to Quantum-Grade CaO Thin Films
by: DeChiara, Jake A., et al.
Published: (2026)

Study of Entropy-Driven Polymorphic Stability for Aspirin Using Accurate Neural Network Interatomic Potential
by: Hattori, Shinnosuke, et al.
Published: (2024)

Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics
by: Sato, Ryuhei, et al.
Published: (2026)

Coarse-Grained Molecular Dynamics Simulations of Oxidative Aging and Stabilization in Polymer Melts with Primary Antioxidants: Effects of Antioxidant Concentration and Molecular Architecture
by: Ishida, Takato, et al.
Published: (2026)

Spin Neural Network Potential for Magnetic Phase Transitions in Uranium Dioxide
by: Kobayashi, Keita, et al.
Published: (2026)

Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations
by: Matsumura, Naoki, et al.
Published: (2024)

Predicting Ti-Al Binary Phase Diagram with an Artificial Neural Network Potential
by: Nichols, Micah, et al.
Published: (2024)

A Physics-Enforced Neural Network to Predict Polymer Melt Viscosity
by: Jain, Ayush, et al.
Published: (2024)

Molecular Dynamics Simulations of $γ$-Belite(010)-Water Interfaces with High-Dimensional Neural Network Potentials
by: Prus, Bernadeta, et al.
Published: (2025)

An accurate alternative to hybrid functionals for germanium: DFT+$α$
by: Abdurrazaq, Abdulgaffar, et al.
Published: (2025)

Toward Generalizable Surrogate Models for Molecular Dynamics via Graph Neural Networks
by: Immanuel, Judah, et al.
Published: (2025)

A High-Dimensional Neural Network Potential for Co$_3$O$_4$
by: Omranpour, Amir, et al.
Published: (2024)

Size-Dependent Tensile Behavior and Dislocation Dynamics in Cu and Ag Nanowires: A Molecular Dynamics Study
by: Hu, Xiaorui, et al.
Published: (2025)

First-Principles Study of Mg-Induced Phase Stabilization in Ga$_2$O$_3$ polymorphs
by: Prakash, Viswesh, et al.
Published: (2026)

Molecular Dynamics Simulations of Collision Cascades in Niobium: Comparing Interatomic Potentials
by: Mondal, S., et al.
Published: (2025)

Accounting for the Quantum Capacitance of Graphite in Constant Potential Molecular Dynamics Simulations
by: Goloviznina, Kateryna, et al.
Published: (2024)

Stability and Dynamics of Sn-based Halide Perovskites: Insights from MACE-MP-0 and Molecular Dynamics Simulations
by: Puccinelli, Thiago, et al.
Published: (2025)

Shadow Molecular Dynamics for a Charge-Potential Equilibration Model
by: Stanton, Robert, et al.
Published: (2025)

Fluoride doping into SiO2-MgO-CaO bioactive glass nanoparticles: bioactivity, biodegradation and biocompatibility assessments
by: Esmati, N., et al.
Published: (2025)

Insights into the Structure and Dynamics of Water at Co$_3$O$_4$(001) Using a High-Dimensional Neural Network Potential
by: Omranpour, Amir, et al.
Published: (2025)

Phase Competition and Rutile Phase Stabilization of Growing GeO2 Films by MOCVD
by: Rahaman, Imteaz, et al.
Published: (2025)

Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems
by: Tang, Ling, et al.
Published: (2025)

Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks
by: Zhu, Xiangzhou, et al.
Published: (2025)

Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics
by: Žugec, Ivan, et al.
Published: (2025)

A Molecular Dynamics Study of Laser-Excited Gold
by: Molina, J. M., et al.
Published: (2021)

Devitrification and Melting Dynamics in Vapor Deposited Water Ice
by: Leoni, Fabio, 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)

Atomic-Scale Mechanisms of SiO$_2$ Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential
by: Kim, Jaehoon, et al.
Published: (2026)