Cover Image

Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tang, Ling, Xia, Weiyi, Viswanathan, Gayatri, Soto, Ernesto, Kovnir, Kirill, Wang, Cai-Zhuang
Format:	Preprint
Published:	2025
Subjects:	Materials Science Disordered Systems and Neural Networks Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Linear-Scaling Potential-Free Data-Driven Molecular Dynamics for Arbitrary-Sized Water Clusters $(\text{H}_2\text{O})_n$
by: Yan, Hongyu, et al.
Published: (2024)

Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
by: Ibrahim, Akram, et al.
Published: (2023)

Specific Heat Anomalies and Local Symmetry Breaking in (Anti-)Fluorite Materials: A Machine Learning Molecular Dynamics Study
by: Kobayashi, Keita, et al.
Published: (2024)

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks
by: Zubatyuk, Tetiana, et al.
Published: (2019)

Scalable Neural Quantum State based Kernel Polynomial Method for Optical Properties from the First Principle
by: Liu, Wei, et al.
Published: (2025)

Machine Learning Interatomic Potentials with Keras API
by: Rili, James Paolo
Published: (2024)

Data-Driven Molecular Dynamics and TEM Analysis of Crystal Growth and Hydrogen Sensing in Pt-Functionalized Graphene Chemiresistive Sensors
by: Ibrahim, Akram, et al.
Published: (2025)

Thermodynamics-Consistent Graph Neural Networks
by: Rittig, Jan G., et al.
Published: (2024)

Super diffusive length dependent thermal conductivity in one-dimensional materials with structural defects: longitudinal to transverse phonon scattering leads to $κ\propto L^{1/3}$ law
by: Burin, Alexander L.
Published: (2025)

Thermal conductivity of aligned polymers with kinks
by: Parshin, Igor V., et al.
Published: (2026)

Collective excitations in Hydrogen across the pressure-induced transition from molecular to atomic fluid
by: Ilenkov, I. -M., et al.
Published: (2025)

Efficient, Equivariant Predictions of Distributed Charge Models
by: Boittier, Eric D., et al.
Published: (2026)

ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters
by: Demchuk, Taras, et al.
Published: (2025)

Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
by: Hino, Kentaro, et al.
Published: (2024)

First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
by: Meir, Sagi, et al.
Published: (2025)

Neural network distillation of orbital dependent density functional theory
by: Medvidović, Matija, et al.
Published: (2024)

Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties
by: Chen, Ruoxia, et al.
Published: (2025)

A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy
by: Wada, Naoki, et al.
Published: (2026)

Charge Hopping Dynamics along a Disordered Chain in Quantum Environments: Comparative Study of Different Rate Kernels
by: Jang, Seogjoo J., et al.
Published: (2026)

Projector, Neural, and Tensor-Network Representations of $\mathbb{Z}_N$ Cluster and Dipolar-cluster SPT States
by: Lee, Seungho, et al.
Published: (2026)

Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies
by: Bensberg, Moritz, et al.
Published: (2025)

Validation Workflow for Machine Learning Interatomic Potentials for Complex Ceramics
by: Ghaffari, Kimia, et al.
Published: (2024)

Neural network backflow for ab-initio quantum chemistry
by: Liu, An-Jun, et al.
Published: (2024)

Exponentially Tilted Thermodynamic Maps (expTM): Predicting Phase Transitions Across Temperature, Pressure, and Chemical Potential
by: Lee, Suemin, et al.
Published: (2025)

Electric Polarization from Many-Body Neural Network Ansatz
by: Li, Xiang, et al.
Published: (2023)

Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning
by: You, Qi, et al.
Published: (2025)

Kramers rate for Brownian particles in excitable media with deformable double-well substrates
by: Dikande, Alain M.
Published: (2025)

Transfer tensor analysis of localization in the Anderson and Aubry-André-Harper models
by: Anderson, Michelle C., et al.
Published: (2025)

Slowly Quenched, High Pressure Glassy B$_2$O$_3$ at DFT Accuracy
by: Meher, Debendra, et al.
Published: (2024)

Max-Cut graph-driven quantum circuit design for planar spin glasses
by: Ghasempouri, Seyed Ehsan, et al.
Published: (2025)

Orbital-interaction-aware deep learning model for efficient surface chemistry simulations
by: Zhang, Zhihao, et al.
Published: (2025)

Amending CALPHAD databases using a neural network for predicting mixing enthalpy of liquids
by: Vincely, Clement, et al.
Published: (2025)

Annealing for prediction of grand canonical crystal structures: Efficient implementation of n-body atomic interactions
by: Couzinie, Yannick, et al.
Published: (2023)

On the Boroxol Ring Fraction in Melt-Quenched B$_2$O$_3$ Glass
by: Meher, Debendra, et al.
Published: (2025)

Construction of Machine-Learning Interatomic Potential Under Heat Flux Regularization and Its Application to Power Spectrum Analysis for Silver Chalcogenides
by: Shimamura, Kohei, et al.
Published: (2022)

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
by: Focassio, Bruno, et al.
Published: (2024)

Unraveling the Crystallization Kinetics of the Ge$_2$Sb$_2$Te$_5$ Phase Change Compound with a Machine-Learned Interatomic Potential
by: Kheir, Omar Abou El, et al.
Published: (2023)

Separable Force Matching of PBE0 Hybrid-Functional Reference Forces for Path-Integral Simulations of Liquid Water
by: Kessler, Jan, et al.
Published: (2026)

Replica Field Theory for a Generalized Franz--Parisi Potential of Inhomogeneous Glassy Systems: New Closure and the Associated Self-Consistent Equation
by: Frusawa, Hiroshi
Published: (2024)

Disentangling morphology and conductance in amorphous graphene
by: Gastellu, Nicolas, et al.
Published: (2024)