Saved in:
| Main Author: | Ratcliff II, William |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23821 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
by: Kang, Kisung, et al.
Published: (2024)
Quantum Kernel Machine Learning for Autonomous Materials Science
by: Adams, Felix, et al.
Published: (2026)
by: Adams, Felix, et al.
Published: (2026)
A Critical Examination of Active Learning Workflows in Materials Science
by: Nair, Akhil S., et al.
Published: (2026)
by: Nair, Akhil S., et al.
Published: (2026)
Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
by: Hu, Jeffrey, et al.
Published: (2026)
by: Hu, Jeffrey, et al.
Published: (2026)
Training-Free Active Learning Framework in Materials Science with Large Language Models
by: Wang, Hongchen, et al.
Published: (2025)
by: Wang, Hongchen, et al.
Published: (2025)
Extended Low-Rank Approximation Accelerates Learning of Elastic Response in Heterogeneous Materials
by: Karmakar, Prabhat, et al.
Published: (2025)
by: Karmakar, Prabhat, et al.
Published: (2025)
Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations
by: Proppe, Andrew H., et al.
Published: (2024)
by: Proppe, Andrew H., et al.
Published: (2024)
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
by: Slautin, Boris N., et al.
Published: (2024)
by: Slautin, Boris N., et al.
Published: (2024)
Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores
by: Amirian, Benhour, et al.
Published: (2025)
by: Amirian, Benhour, et al.
Published: (2025)
Autonomous Reliability Qualification of Ga$_2$O$_3$-based Hydrogen and Temperature Sensors via Safe Active Learning
by: Febba, Davi, et al.
Published: (2026)
by: Febba, Davi, et al.
Published: (2026)
Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization
by: Raghavan, Aditya, et al.
Published: (2024)
by: Raghavan, Aditya, et al.
Published: (2024)
Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
by: Schwarting, Marcus, et al.
Published: (2025)
by: Schwarting, Marcus, et al.
Published: (2025)
Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
by: Ullberg, R. Seaton, et al.
Published: (2026)
by: Ullberg, R. Seaton, et al.
Published: (2026)
Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy
by: Binh, Quach Thi Thai, et al.
Published: (2025)
by: Binh, Quach Thi Thai, et al.
Published: (2025)
SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learning
by: Ueno, Koki, et al.
Published: (2024)
by: Ueno, Koki, et al.
Published: (2024)
Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
by: Nam, Juno, et al.
Published: (2024)
by: Nam, Juno, et al.
Published: (2024)
Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review
by: Oche, Agada Joseph, et al.
Published: (2025)
by: Oche, Agada Joseph, et al.
Published: (2025)
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
by: Zimmermann, Yoel, et al.
Published: (2025)
by: Zimmermann, Yoel, et al.
Published: (2025)
Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
by: Luisier, Mathieu, et al.
Published: (2026)
by: Luisier, Mathieu, et al.
Published: (2026)
Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
by: Okabe, Ryotaro, et al.
Published: (2024)
by: Okabe, Ryotaro, et al.
Published: (2024)
Large Language Model-Guided Prediction Toward Quantum Materials Synthesis
by: Okabe, Ryotaro, et al.
Published: (2024)
by: Okabe, Ryotaro, et al.
Published: (2024)
Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth
by: Sabattini, Leonardo, et al.
Published: (2024)
by: Sabattini, Leonardo, et al.
Published: (2024)
MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis
by: Weng, Yonghao, et al.
Published: (2025)
by: Weng, Yonghao, et al.
Published: (2025)
Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
by: Du, Yuanqi, et al.
Published: (2025)
by: Du, Yuanqi, et al.
Published: (2025)
Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
by: Price, Christopher C., et al.
Published: (2024)
by: Price, Christopher C., et al.
Published: (2024)
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery
by: Boyar, Onur, et al.
Published: (2025)
by: Boyar, Onur, et al.
Published: (2025)
Open Materials Generation with Inference-Time Reinforcement Learning
by: Hoellmer, Philipp, et al.
Published: (2026)
by: Hoellmer, Philipp, et al.
Published: (2026)
Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits
by: Willow, Soohaeng Yoo, et al.
Published: (2025)
by: Willow, Soohaeng Yoo, et al.
Published: (2025)
Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials
by: Lee, Chih-Yu, et al.
Published: (2026)
by: Lee, Chih-Yu, et al.
Published: (2026)
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
by: Li, Zhuoyuan, et al.
Published: (2025)
by: Li, Zhuoyuan, et al.
Published: (2025)
Autonomous microARPES
by: Agustsson, Steinn Ymir, et al.
Published: (2024)
by: Agustsson, Steinn Ymir, et al.
Published: (2024)
Learning ORDER-Aware Multimodal Representations for Composite Materials Design
by: Li, Xinyao, et al.
Published: (2026)
by: Li, Xinyao, et al.
Published: (2026)
A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery
by: Hashimoto, Yusuke, et al.
Published: (2025)
by: Hashimoto, Yusuke, et al.
Published: (2025)
LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation
by: Khastagir, Subhojyoti, et al.
Published: (2025)
by: Khastagir, Subhojyoti, et al.
Published: (2025)
A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures
by: Yan, Keqiang, et al.
Published: (2025)
by: Yan, Keqiang, et al.
Published: (2025)
Learning Physics-Consistent Material Behavior from Dynamic Displacements
by: Han, Zhichao, et al.
Published: (2024)
by: Han, Zhichao, et al.
Published: (2024)
Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials
by: Appleton, Robert J., et al.
Published: (2024)
by: Appleton, Robert J., et al.
Published: (2024)
When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
by: Dale, Ashley S., et al.
Published: (2025)
by: Dale, Ashley S., et al.
Published: (2025)
Learning Magnetic Order Classification from Large-Scale Materials Databases
by: Fahmy, Ahmed E.
Published: (2025)
by: Fahmy, Ahmed E.
Published: (2025)
Composite Material Design for Optimized Fracture Toughness Using Machine Learning
by: Jahromi, Mohammad Naqizadeh, et al.
Published: (2024)
by: Jahromi, Mohammad Naqizadeh, et al.
Published: (2024)
Similar Items
-
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) -
Quantum Kernel Machine Learning for Autonomous Materials Science
by: Adams, Felix, et al.
Published: (2026) -
A Critical Examination of Active Learning Workflows in Materials Science
by: Nair, Akhil S., et al.
Published: (2026) -
Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
by: Hu, Jeffrey, et al.
Published: (2026) -
Training-Free Active Learning Framework in Materials Science with Large Language Models
by: Wang, Hongchen, et al.
Published: (2025)