Copertina

Study of Entropy-Driven Polymorphic Stability for Aspirin Using Accurate Neural Network Interatomic Potential

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hattori, Shinnosuke, Zhu, Qiang
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Automated High-throughput Organic Crystal Structure Prediction via Population-based Sampling
di: Zhu, Qiang, et al.
Pubblicazione: (2024)

Bending Deformation Driven by Molecular Rotation
di: Santos-Florez, Pedro A., et al.
Pubblicazione: (2022)

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
di: Hattori, Shinnosuke, et al.
Pubblicazione: (2025)

Quantification of Crystal Packing Similarity from Spherical Harmonic Transform
di: Zhu, Qiang, et al.
Pubblicazione: (2022)

Accurate and Efficient Interatomic Potentials for Dislocations in InP
di: Rocke, Thomas, et al.
Pubblicazione: (2026)

Importance of Electronic Entropy for Machine Learning Interatomic Potentials
di: Petersen, Martin Hoffmann, et al.
Pubblicazione: (2026)

Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential
di: Sivak, Jacob T., et al.
Pubblicazione: (2024)

Flexocurrent-induced magnetization: Strain gradient-induced magnetization in time-reversal symmetric systems
di: Koyama, Shinnosuke, et al.
Pubblicazione: (2026)

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

How Graph Neural Network Interatomic Potentials Extrapolate: Role of the Message-Passing Algorithm
di: Kang, Sungwoo
Pubblicazione: (2024)

Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
di: Birks, Fraser, et al.
Pubblicazione: (2025)

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties
di: Hawthorne, Felipe, et al.
Pubblicazione: (2025)

An Active Learning Interatomic Potential For Defect-Engineered CoCrFeMnNi High-Entropy Alloy
di: Sahoo, Manish, et al.
Pubblicazione: (2025)

Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
di: Nong, Wei, et al.
Pubblicazione: (2025)

A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation
di: Gu, Yaohui, et al.
Pubblicazione: (2026)

Large-Scale, Long-Time Atomistic Simulations of Proton Transport in Polymer Electrolyte Membranes Using a Neural Network Interatomic Potential
di: Yoshimoto, Yuta, et al.
Pubblicazione: (2025)

Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System
di: Kunselman, Courtney, et al.
Pubblicazione: (2025)

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
di: Perez, Danny, et al.
Pubblicazione: (2025)

Classical and Machine Learning Interatomic Potentials for BCC Vanadium
di: Wang, Rui, et al.
Pubblicazione: (2022)

A "Magnetic" Machine Learning Interatomic Potential for Nickel
di: Gong, Xiaoguo, et al.
Pubblicazione: (2023)

Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study
di: Hamilton, Brenden W., et al.
Pubblicazione: (2026)

Machine-Learning Interatomic Potential for Twisted Hexagonal Boron Nitride: Accurate Structural Relaxation and Emergent Polarization
di: Luna, Wilson Nieto, et al.
Pubblicazione: (2025)

Graph Neural Network for Unified Electronic and Interatomic Potentials: Strain-tunable Electronic Structures in 2D Materials
di: Choi, Moon-ki, et al.
Pubblicazione: (2025)

Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials
di: Bonakala, Satyanarayana, et al.
Pubblicazione: (2025)

Prediction of Mechanical Properties and Thermodynamic Stability of Ti-N system using MTP Interatomic Potential
di: Rana, Pradeep Kumar, et al.
Pubblicazione: (2025)

Uncertainty Quantification in Atomistic Simulations of Silicon using Interatomic Potentials
di: Best, I. R., et al.
Pubblicazione: (2024)

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
di: Tahmasbi, Hossein, et al.
Pubblicazione: (2025)

Local Order Average-Atom Interatomic Potentials
di: Zeller, Chloe A., et al.
Pubblicazione: (2025)

Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
di: Mehdizadeh, Ardavan, et al.
Pubblicazione: (2025)

Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential
di: Gong, Xuhe, et al.
Pubblicazione: (2025)

Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
di: Roberts, Josiah, et al.
Pubblicazione: (2024)

Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials
di: Restuccia, Paolo, et al.
Pubblicazione: (2025)

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

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
di: Kim, Jaesun, et al.
Pubblicazione: (2025)

Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining
di: Zheng, Qianyu, et al.
Pubblicazione: (2026)

Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
di: Varughese, Bilvin, et al.
Pubblicazione: (2025)

Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
di: Gao, Pengfei, et al.
Pubblicazione: (2025)

Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials
di: Ho, Cheuk Hin, et al.
Pubblicazione: (2026)

Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions
di: Bheemaguli, Achinthya Krishna, et al.
Pubblicazione: (2025)

Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
di: Shuang, Fei, et al.
Pubblicazione: (2026)