Cover Image

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhu, Siya, Sariturk, Doguhan, Arroyave, Raymundo
Format:	Preprint
Published:	2025
Subjects:	Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
by: Zhu, Siya, et al.
Published: (2024)

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

Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling
by: Zhu, Siya, et al.
Published: (2026)

Influence of Coherent Elastic Strain on Phase Separation in BCC Nb-V Alloys
by: Zhu, Siya, et al.
Published: (2026)

DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery
by: Singh, Divyanshu, et al.
Published: (2026)

Predicting interstitial elements in Refractory Complex Concentrated Alloys
by: Huang, Aomin, et al.
Published: (2025)

A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
by: Yan, Keqiang, et al.
Published: (2025)

Evaluating Moment Tensor Potential in Ag-Cu Alloy: Accuracy, Transferability, and Phase Diagram Fidelity
by: Nitol, Mashroor S., et al.
Published: (2025)

Deciphering Chemical Ordering in High Entropy Materials: A Machine Learning-Accelerated High-throughput Cluster Expansion Approach
by: Vazquez, Guillermo, et al.
Published: (2024)

Visualizing High Entropy Alloy Spaces: Methods and Best Practices
by: Vela, Brent, et al.
Published: (2024)

Decoding Non-Linearity and Complexity: Deep Tabular Learning Approaches for Materials Science
by: Attari, Vahid, et al.
Published: (2024)

Good Enough is Better: Feasibility vs. Pareto-Optimality in Alloy Design
by: Maguire, Cayden, et al.
Published: (2025)

Mapping of Microstructure Transitions during Rapid Alloy Solidification Using Bayesian-Guided Phase-Field Simulations
by: Mancias, José, et al.
Published: (2025)

Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
by: Hardcastle, Christofer, et al.
Published: (2025)

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

Supply Risk-Aware Alloy Discovery and Design
by: Mulukutla, Mrinalini, et al.
Published: (2024)

Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark
by: Xiao, Enda, et al.
Published: (2025)

Performance-driven Computational Design of Multi-terminal Compositionally Graded Alloy Structures using Graphs
by: Allen, Marshall D., et al.
Published: (2024)

Microstructure-Aware Bayesian Materials Design
by: Khatamsaz, Danial, et al.
Published: (2025)

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

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
by: Park, Eunjeong, et al.
Published: (2026)

Hierarchical Gaussian Process-Based Bayesian Optimization for Materials Discovery in High Entropy Alloy Spaces
by: Alvi, Sk Md Ahnaf Akif, et al.
Published: (2024)

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

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows
by: Rejiba, Khalil, et al.
Published: (2025)

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

Analytical Gradient-Based Optimization of CALPHAD Model Parameters
by: Kunselman, Courtney, et al.
Published: (2025)

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

General-Purpose Machine-Learned Potential for CrCoNi Alloys Enabling Large-Scale Atomistic Simulations with First-Principles Accuracy
by: Wu, Yong-Chao, et al.
Published: (2026)

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

Water Phase Diagram from a General-Purpose Atomic Cluster Expansion Potential
by: Ibrahim, Eslam, et al.
Published: (2026)

Deep Learning Accelerated Phase Prediction of Refractory Multi-Principal Element Alloys
by: Shargh, A. K., et al.
Published: (2024)

Quantum-Annealing Enhanced Machine Learning for Interpretable Phase Classification of High-Entropy Alloys
by: Hoyos, Diego Ibarra, et al.
Published: (2025)

A Machine Learning Framework for Predicting Glass-Forming Ability in Ternary Alloy Systems
by: Mahmoudi, Fatemeh
Published: (2025)

Accelerating the Design of Resorbable Magnesium Alloys: A Machine Learning Approach to Property Prediction
by: Nandal, Vickey, et al.
Published: (2026)

Machine Learning Approach to Predict the Curie Temperature of Fe- and Pt-Based Alloys
by: Ponomarova, Svitlana, et al.
Published: (2025)

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

Accelerated Multi-Objective Alloy Discovery through Efficient Bayesian Methods: Application to the FCC Alloy Space
by: Hastings, Trevor, et al.
Published: (2024)

Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach
by: Xie, Zhuocheng, et al.
Published: (2024)

Two-Shot Optimization of Compositionally Complex Refractory Alloys
by: Paramore, James D., et al.
Published: (2024)

FerroAI: A Deep Learning Model for Predicting Phase Diagrams of Ferroelectric Materials
by: Zhang, Chenbo, et al.
Published: (2025)