Copertina

A Critical Examination of Active Learning Workflows in Materials Science

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nair, Akhil S., Foppa, Lucas
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Materials Science Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis
di: Nair, Akhil S., et al.
Pubblicazione: (2024)

Materials Database from All-electron Hybrid Functional DFT Calculations
di: Nair, Akhil S., et al.
Pubblicazione: (2025)

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

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

Unveiling the Core of Materials Properties via SISSO and Sensitivity Analysis
di: Foppa, Lucas, et al.
Pubblicazione: (2026)

Coherent Collections of Rules Describing Exceptional Materials Identified with a Multi-Objective Optimization of Subgroups
di: Foppa, Lucas, et al.
Pubblicazione: (2024)

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
di: Li, Jiaxiang, et al.
Pubblicazione: (2025)

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
di: Hu, Jeffrey, et al.
Pubblicazione: (2026)

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

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

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

Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
di: Suzuki, Yuta, et al.
Pubblicazione: (2025)

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

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

Functional Unit: A New Perspective on Materials Science Research Paradigms
di: Ye, Caichao, et al.
Pubblicazione: (2025)

Quantum Kernel Machine Learning for Autonomous Materials Science
di: Adams, Felix, et al.
Pubblicazione: (2026)

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
di: Polat, Can, et al.
Pubblicazione: (2025)

Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science
di: Chen, An, et al.
Pubblicazione: (2024)

Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework
di: Polat, Can, et al.
Pubblicazione: (2025)

XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science
di: Subramanian, Jithendaraa, et al.
Pubblicazione: (2025)

From Text to Insight: Large Language Models for Materials Science Data Extraction
di: Schilling-Wilhelmi, Mara, et al.
Pubblicazione: (2024)

MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data
di: Li, Wentao, et al.
Pubblicazione: (2025)

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
di: Ullberg, R. Seaton, et al.
Pubblicazione: (2026)

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

34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
di: Zimmermann, Yoel, et al.
Pubblicazione: (2025)

Open Materials Generation with Inference-Time Reinforcement Learning
di: Hoellmer, Philipp, et al.
Pubblicazione: (2026)

MoMa: A Modular Deep Learning Framework for Material Property Prediction
di: Wang, Botian, et al.
Pubblicazione: (2025)

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

Learning ORDER-Aware Multimodal Representations for Composite Materials Design
di: Li, Xinyao, et al.
Pubblicazione: (2026)

Learning Physics-Consistent Material Behavior from Dynamic Displacements
di: Han, Zhichao, et al.
Pubblicazione: (2024)

Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials
di: Appleton, Robert J., et al.
Pubblicazione: (2024)

When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
di: Dale, Ashley S., et al.
Pubblicazione: (2025)

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
di: Yan, Xiaoli, et al.
Pubblicazione: (2025)

Learning Magnetic Order Classification from Large-Scale Materials Databases
di: Fahmy, Ahmed E.
Pubblicazione: (2025)

Composite Material Design for Optimized Fracture Toughness Using Machine Learning
di: Jahromi, Mohammad Naqizadeh, et al.
Pubblicazione: (2024)

Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials
di: Dong, Guangyi, et al.
Pubblicazione: (2025)

UniMat: Unifying Materials Embeddings through Multi-modal Learning
di: Ock, Janghoon, et al.
Pubblicazione: (2024)

NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
di: Chen, Chengbing, et al.
Pubblicazione: (2025)

Extended Low-Rank Approximation Accelerates Learning of Elastic Response in Heterogeneous Materials
di: Karmakar, Prabhat, et al.
Pubblicazione: (2025)

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
di: Cangi, Attila, et al.
Pubblicazione: (2024)