Imagen de Portada

Are LLMs Ready for Real-World Materials Discovery?

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Miret, Santiago, Krishnan, N M Anoop
Formato:	Preprint
Publicado:	2024
Materias:	Materials Science Artificial Intelligence Computation and Language Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

MatExpert: Decomposing Materials Discovery by Mimicking Human Experts
por: Ding, Qianggang, et al.
Publicado: (2024)

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
por: Miret, Santiago, et al.
Publicado: (2025)

Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
por: Wang, Hongchen, et al.
Publicado: (2024)

Generative Chemical Language Models for Energetic Materials Discovery
por: Salij, Andrew, et al.
Publicado: (2026)

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery
por: Rothfarb, Samuel, et al.
Publicado: (2025)

Automated Extraction of Material Properties using LLM-based AI Agents
por: Ghosh, Subham, et al.
Publicado: (2025)

LLEMA: Evolutionary Search with LLMs for Multi-Objective Materials Discovery
por: Abhyankar, Nikhil, et al.
Publicado: (2025)

BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning
por: Hage, Tarjei Paule, et al.
Publicado: (2026)

Aligning Reasoning LLMs for Materials Discovery with Physics-aware Rejection Sampling
por: Hyun, Lee, et al.
Publicado: (2025)

Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence
por: Wang, Fiona Y., et al.
Publicado: (2026)

Sustainable Materials Discovery in the Era of Artificial Intelligence
por: Mannan, Sajid, et al.
Publicado: (2026)

Diverse LLMs or Diverse Question Interpretations? That is the Ensembling Question
por: Rosales, Rafael, et al.
Publicado: (2025)

Higher-Order Knowledge Representations for Agentic Scientific Reasoning
por: Stewart, Isabella A., et al.
Publicado: (2026)

Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
por: Buehler, Markus J.
Publicado: (2025)

Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
por: Vuong, An, et al.
Publicado: (2025)

AI scientists produce results without reasoning scientifically
por: Ríos-García, Martiño, et al.
Publicado: (2026)

LLMatDesign: Autonomous Materials Discovery with Large Language Models
por: Jia, Shuyi, et al.
Publicado: (2024)

Evaluating LLMs on Real-World Forecasting Against Expert Forecasters
por: Lu, Janna
Publicado: (2025)

Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review
por: Nematov, Dilshod, et al.
Publicado: (2025)

ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges
por: Qian, Cheng, et al.
Publicado: (2025)

Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery
por: Jia, Yunze, et al.
Publicado: (2025)

LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery
por: Boyar, Onur, et al.
Publicado: (2025)

PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration
por: Lai, Zheyuan, et al.
Publicado: (2025)

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search
por: Kim, Edward, et al.
Publicado: (2024)

Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction
por: Hira, Kausik, et al.
Publicado: (2023)

Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework
por: Guan, Zihan, et al.
Publicado: (2026)

Foundational Large Language Models for Materials Research
por: Mishra, Vaibhav, et al.
Publicado: (2024)

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

WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia
por: Hou, Yufang, et al.
Publicado: (2024)

AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials
por: Lv, Taoyuze, et al.
Publicado: (2025)

Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs
por: Zhou, Wei, et al.
Publicado: (2026)

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

Leveraging Text Guidance for Enhancing Demographic Fairness in Gender Classification
por: Krishnan, Anoop
Publicado: (2025)

FlowMM: Generating Materials with Riemannian Flow Matching
por: Miller, Benjamin Kurt, et al.
Publicado: (2024)

Teaching LLMs How to Learn with Contextual Fine-Tuning
por: Choi, Younwoo, et al.
Publicado: (2025)

CauScientist: Teaching LLMs to Respect Data for Causal Discovery
por: Peng, Bo, et al.
Publicado: (2026)

The Illusion of Readiness in Health AI
por: Gu, Yu, et al.
Publicado: (2025)

CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning
por: Wu, Mengsong, et al.
Publicado: (2025)

SPARC: Subspace-Aware Prompt Adaptation for Robust Continual Learning in LLMs
por: Jayasuriya, Dinithi, et al.
Publicado: (2025)

On the Performance of LLMs for Real Estate Appraisal
por: Geerts, Margot, et al.
Publicado: (2025)