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RESEARCH PAPER · npj Computational Materials · 2025

LLM-Prop: predicting the properties of crystalline materials using large language models

Andre Niyongabo Rubungo, Craig B. Arnold, Barry P. Rand, Adji Bousso Dieng

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LLM-Prop: predicting the properties of crystalline materials using large language modelsOriginalData engineering for AI applications in energetic materials4DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys7Machine learning to design metal–organic frameworks: progress and challenges from a data efficiency perspective22Quantitative analysis of Bi2+-activated luminescent materials and their distinction from Bi3+ systems0Knowledge-extractor: a self-evolving scientific framework for hydrogen energy research driven by AI agents9Highly Accurate and Fast Prediction of MOF Free Energy via Machine Learning10From large language models to AI agents in energy materials research: enabling discovery, design, and automation12Data-driven discovery of novel chalcogenide semiconductors for solar absorption0Development and applications of epitaxial heterostructures based on organic nanowires0Foundation Models and LLMs for Materials Science: A Review of Prompting, Fine-Tuning, and What Does Not Transfer0GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models0The Illusion of Generalizability in Foundation Models for Materials Science: A Critique of Zero-Shot Property Prediction0A multimodal dataset of causal mechanisms in materials science literature0Agentic Knowledge Graphs of the LiFePO 4 Cathode for Lithium Ion Battery: Balancing Discovery and Stability with LLMs0A generalizable automated geophysical agent workflow for accessible subsurface hydrology analysis1TopoChat: Enhancing Topological Materials Retrieval with Large Language Model and Multi-Source Knowledge1AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing27Data-driven prediction of ionic conductivity in solid-state electrolytes with machine learning and large language models1From Data to Discovery: Challenges and Solutions in AI-Driven Materials Science0Design of an integrated federated large language models for semantic reasoning and self-organizing 6G networks2Tri-modal machine learning powers efficient catalyst discovery for high-performance energy storage0Node-level cross-attention for multimodal crystal property prediction using structural graphs and textual descriptions0Automated distillation of composition–processing–structure–performance linkages from fragmented and multi-modal materials literature1A survey of agentic materials science and engineering: where are we and where are we going?0Advancing battery failure diagnosis by knowledge-augmented large language models2Machine Learning in Materials Science: Data-Driven Discovery and Functional Applications1Vertical emitting semiconductor nanowire lasers for monolithically integrated photonic circuits0ThermIC: Physics-Informed Graph Reinforcement Learning for Thermal–Mechanical Co-Optimization in 3D-IC Placement0Artificial intelligence for carbon materials in electrochemical energy storage: Methods, applications, and perspectives0A semantics-guided multimodal machine learning framework for predicting the flexural strength of C/C-SiC composites0Large language models accelerated health informatics screening framework predicts infrared triggered drug delivery performance of doped hydroxyapatite1Large language models in materials science: mechanistic understanding, structure–property reasoning and future opportunities0

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