Cover Image

Thermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Juejing, Anderson, Haydn, Waxman, Noah I., Kovalev, Vsevolod, Fisher, Byron, Li, Elizabeth, Guo, Xiaofeng
Format:	Preprint
Published:	2025
Subjects:	Materials Science Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning
by: Juejing Liu, et al.
Published: (2026)

Data-Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data
by: Liu, Juejing, et al.
Published: (2025)

Machine Learning for Improved Density Functional Theory Thermodynamics
by: Simak, Sergei I., et al.
Published: (2025)

Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids
by: Li, Yanfei, et al.
Published: (2024)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
by: Benítez, Pol, et al.
Published: (2025)

On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
by: Avilov, Fedor S., et al.
Published: (2024)

Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial
by: Star, et al.
Published: (2024)

Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
by: Yu, Benjamin, et al.
Published: (2025)

Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
by: Lahouari, Adam, et al.
Published: (2025)

Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
by: Price, Christopher C., et al.
Published: (2024)

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures
by: Xin, Yu, et al.
Published: (2025)

Industrial-scale Prediction of Cement Clinker Phases using Machine Learning
by: Fayaz, Sheikh Junaid, et al.
Published: (2024)

CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines
by: Liang, Chen, et al.
Published: (2025)

Complete and Efficient Graph Transformers for Crystal Material Property Prediction
by: Yan, Keqiang, et al.
Published: (2024)

Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys
by: Hasnain, Mohd
Published: (2026)

Predictive Inorganic Synthesis based on Machine Learning using Small Data sets: a case study of size-controlled Cu Nanoparticles
by: Motmans, Brent, et al.
Published: (2025)

Topology-Informed Machine Learning for Efficient Prediction of Solid Oxide Fuel Cell Electrode Polarization
by: Szemer, Maksym, et al.
Published: (2024)

Representation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and Datasets
by: Andrello, Samuel, et al.
Published: (2025)

Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors
by: Kenney, Michael J., et al.
Published: (2024)

Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale
by: Lu, Jiali, et al.
Published: (2026)

Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data
by: Mathew, Ansu, et al.
Published: (2026)

Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
by: Luo, Jing, et al.
Published: (2025)

Toward Learning Latent-Variable Representations of Microstructures by Optimizing in Spatial Statistics Space
by: Hashemi, Sayed Sajad, et al.
Published: (2024)

Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
by: Yan, Keqiang, et al.
Published: (2025)

A Data-Driven Machine Learning Approach for Predicting Axial Load Capacity in Steel Storage Rack Columns
by: Mammadli, Bakhtiyar, et al.
Published: (2025)

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

Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks
by: Han, Seunghee, et al.
Published: (2024)

A Comparative Study of Machine Learning Models Predicting Energetics of Interacting Defects
by: Yu, Hao
Published: (2024)

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
by: Antoniuk, Evan R., et al.
Published: (2025)

Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space
by: Chen, Hongshun, et al.
Published: (2026)

Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning
by: Ward, Logan, et al.
Published: (2023)

Non-stoichiometric and Subnano-heterogeneous Ln-incorporated UO2: its defect chemistry and thermal oxidation
by: Liu, Juejing, et al.
Published: (2024)

CrystalICL: Enabling In-Context Learning for Crystal Generation
by: Wang, Ruobing, et al.
Published: (2025)

Leakage-Aware Bandgap Prediction on the JARVIS-DFT Dataset: A Phase-Wise Feature Analysis
by: Sharma, Gaurav Kumar
Published: (2025)

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials
by: Shuang, Fei, et al.
Published: (2025)

Thermodynamically-Informed Iterative Neural Operators for Heterogeneous Elastic Localization
by: Kelly, Conlain, et al.
Published: (2024)

MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing
by: Akbari, Parand, et al.
Published: (2022)

Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training
by: Oerder, Rick, et al.
Published: (2026)

Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers
by: Sours, Tyler, et al.
Published: (2024)

Thermodynamic assessment of machine learning models for solid-state synthesis prediction
by: Schlesinger, Jane, et al.
Published: (2026)