Cover Image

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hu, Jeffrey, Dong, Rongzhi, Feng, Ying, Hu, Ming, Hu, Jianjun
Format:	Preprint
Published:	2026
Subjects:	Materials Science Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
by: Omee, Sadman Sadeed, et al.
Published: (2024)

AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
by: Song, Yuqi, et al.
Published: (2024)

In context learning Foundation models for Materials Property Prediction with Small datasets
by: Li, Qinyang, et al.
Published: (2025)

Facet: highly efficient E(3)-equivariant networks for interatomic potentials
by: Miklaucic, Nicholas, et al.
Published: (2025)

Multimodal Foundation Models for Material Property Prediction and Discovery
by: Moro, Viggo, et al.
Published: (2023)

MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
by: Kong, Lingyu, et al.
Published: (2025)

Out-of-distribution materials property prediction using adversarial learning based fine-tuning
by: Li, Qinyang, et al.
Published: (2024)

A Message Passing Neural Network Surrogate Model for Bond-Associated Peridynamic Material Correspondence Formulation
by: Hu, Xuan, et al.
Published: (2024)

AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials
by: Lin, Yan, et al.
Published: (2026)

Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control
by: Omee, Sadman Sadeed, et al.
Published: (2025)

A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery
by: Hashimoto, Yusuke, et al.
Published: (2025)

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
by: Schoener, Brandon, et al.
Published: (2026)

Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
by: Li, Zhuoyuan, et al.
Published: (2025)

Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials
by: Prein, Thorben, et al.
Published: (2025)

Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
by: Schwarting, Marcus, et al.
Published: (2025)

A Foundation Model for Material Fracture Prediction
by: Marcato, Agnese, et al.
Published: (2025)

Improving realistic material property prediction using domain adaptation based machine learning
by: Hu, Jeffrey, et al.
Published: (2023)

Tensor Completion for Surrogate Modeling of Material Property Prediction
by: Pakala, Shaan, et al.
Published: (2025)

Training-Free Active Learning Framework in Materials Science with Large Language Models
by: Wang, Hongchen, et al.
Published: (2025)

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
by: Cheng, Mouyang, et al.
Published: (2025)

Active Learning Enables Extrapolation in Molecular Generative Models
by: Antoniuk, Evan R., et al.
Published: (2025)

A Critical Examination of Active Learning Workflows in Materials Science
by: Nair, Akhil S., et al.
Published: (2026)

Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
by: Okabe, Ryotaro, et al.
Published: (2024)

Foundation Models for Discovery and Exploration in Chemical Space
by: Wadell, Alexius, et al.
Published: (2025)

TCSP 2.0: Template Based Crystal Structure Prediction with Improved Oxidation State Prediction and Chemistry Heuristics
by: Wei, Lai, et al.
Published: (2025)

Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
by: Mok, Dong Hyeon, et al.
Published: (2026)

From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agent
by: Zhuang, Genmao, et al.
Published: (2026)

PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure
by: Buzzy, Michael, et al.
Published: (2025)

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

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

Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts
by: Ding, Rui, et al.
Published: (2024)

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

Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design
by: Comlek, Yigitcan, et al.
Published: (2023)

MADE: Benchmark Environments for Closed-Loop Materials Discovery
by: Malik, Shreshth A, et al.
Published: (2026)

Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
by: Ratcliff II, William
Published: (2026)

Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
by: Slautin, Boris N., et al.
Published: (2024)

Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs
by: Li, Beichen, et al.
Published: (2023)

Causal Discovery from Data Assisted by Large Language Models
by: Barakati, Kamyar, et al.
Published: (2025)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
by: Kang, Kisung, et al.
Published: (2024)

Deep Learning-Based Surrogate Creep Modelling in Inconel 625: A High-Temperature Alloy Study
by: Das, Shubham, et al.
Published: (2025)