Materials Project– tag –
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Scilight Press Introduces ‘Harness’ Framework Integrating LLM Agents and Materials Project to Enhance Perovskite Bandgap Prediction
Scilight Press Unknown Overview Scilight Press has proposed "Harness," a novel framework to integrate Large Language Model (LLM) agents with existing material databases in materials science. This framework translates LLM agent proposals ... -
National University of Singapore Builds ‘AI Foundry’ to Transform Materials Discovery via Continuous Prediction, Experimentation, and Learning Cycle
EurekAlert! Singapore Overview Professor Ong is leading the Materialyze.AI Lab at NUS to establish an 'AI Foundry' for transforming materials discovery, aiming to connect various AI capabilities to create a continuous cycle of prediction... -
Google DeepMind GNoME and Microsoft MatterGen Accelerate Materials Discovery, Predicting Millions of Novel Stable Structures
Facebook (MIT DMSE) USA Overview Generative AI models, including Google DeepMind's GNoME and Microsoft's MatterGen, are significantly accelerating materials discovery by proposing novel material combinations and predicting their stabilit... -
Google DeepMind’s GNoME Discovers 2.2 Million New Crystals: Accelerating Materials Science by 800 Years, Offering Novel Superconductor and Battery Candidates
Facebook (DeepMind) UK Overview Google DeepMind's AI tool, GNoME, has reportedly discovered 2.2 million new inorganic crystals, accelerating materials science progress by 800 years. GNoME leveraged AI models trained on existing materials... -
Universal MLIPs Face Generalization Challenges, ACS Study Highlights Need for Iterative Fine-Tuning of Material-Specific Models
ACS Publications USA Overview An ACS Publications paper highlights that while Universal Machine Learning Interatomic Potentials (MLIPs) are rapidly becoming general tools for atomic simulations, their role in quantitative material modeli... -
Massive Discovery of 9,139 Low-Dimensional Materials from Materials Project via Universal Computational Strategy, Including 887 Exfoliable 2D Materials
ACS Chemistry of Materials USA Overview This research combined universal machine learning interatomic potentials (UMLIPs) with an advanced force constant (FC)-based dimensionality classification method to massively discover new low-dimen... -
Northwestern University Accelerates Electronic and Energy Material Design with Property-Predicting Machine Learning Model ‘LVGP’
Northwestern University (Paula M. Trienens Institute For Sustainability And Energy) USA Overview Northwestern University's Wei Chen research group developed the Latent Variable Gaussian Process (LVGP) machine learning model, which conver... -
arXiv Presents Structural Novelty Analysis for AI-Generated Crystals, Reveals Bias Towards Known Prototypes
arXiv USA Overview A new workflow for assessing the structural novelty of inorganic crystals generated by AI models has been published on arXiv. The study evaluates whether AI-generated crystals are duplicates, reproducible via elemental... -
Hugging Face Spotlights CrystalCLR and CHGNet for Enhanced Materials Property Prediction via Machine Learning
Hugging Face USA Overview Hugging Face highlights significant advancements in materials property prediction with the CrystalCLR framework and CHGNet machine learning interatomic potential. CrystalCLR improves material representations thr... -
AtomGPT.org Launches Open-Access Agentic AI Platform ‘AGAPI-Agents’ to Accelerate Materials Design
The Journal of Physical Chemistry Letters USA Overview AtomGPT.org has launched 'AGAPI-Agents,' an open-access agentic AI platform integrating open-source Large Language Models (LLMs) with scientific tools and databases for accelerated m...
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