Materials Informatics– category –
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AIP Publishing Reveals New Vibrational Entropy Metric Dramatically Improves Empirical Interatomic Potential Accuracy by 80%, Revolutionizing MLIP Validation
AIP Publishing USA Overview A new study published by AIP Publishing introduces a novel method for developing and validating empirical interatomic potentials, achieving an 80% error reduction in dynamic properties compared to existing MOF... -
Argonne National Lab’s AI Discovers N2116 Electrolyte in 80 Hours, Enabling Faster Charging, Higher Energy Density Batteries
Argonne National Laboratory USA Overview Argonne National Laboratory researchers have used an AI-driven method to drastically accelerate materials discovery for battery development. The AI identified 18 stable material candidates from 50... -
Materials Informatics Weekly Report August 30, 2026
▼ ▼ ▼ If the infographic makes you want to read the full report, please click the download button below. ▼ ▼ ▼ 📄 Weekly Report August 30, 2026 (PDF) — Download Weekly Report August 30, 2026 (PDF) — DownloadDownload 🎙 Podcast August 30, ... -
ACS Paper: Modular ML Workflow Achieves High-Efficiency Hypothesis Generation for Lithium Solid Electrolytes, Predicting Ionic Conductivity
The Journal of Physical Chemistry C | ACS Publications USA Overview A research published in 'The Journal of Physical Chemistry C' (ACS Publications) presents a modular machine learning (ML) workflow for generating and prioritizing lithiu... -
Preprints.org Publishes Review on High-Entropy Alloy Design: ML Addresses Combinatorial Explosion in Discovery
Preprints.org Unknown Overview A review paper published on Preprints.org explores the convergence of physical metallurgy and data-driven methods in high-entropy alloy (HEA) design. The paper details how AI and machine learning (ML) addre... -
Mitsubishi Chemical Forges Hybrid AI-Quantum Path for Next-Gen EUV Photoresists
Mitsubishi Chemical Japan Overview Mitsubishi Chemical has unveiled a pioneering research initiative integrating generative AI, quantum computing, and GPU-accelerated platforms to revolutionize the design of next-generation EUV photoresi... -
Autonomous Laboratories Spark Paradigm Shift in Materials Science, Accelerating Development Through Evolving Algorithms and Hardware
Facebook Unknown Overview Autonomous Laboratories (SDLs) are fundamentally transforming materials science, enabling unprecedented acceleration in materials discovery through autonomous exploration. By integrating advanced AI—from machine... -
AI-Powered Platform from IIT Madras Forges World’s Largest Open Alloy Database, Turbocharging Sustainable Materials Discovery
Open Source For U India Overview Researchers at IIT Madras have developed an AI-driven, open-source platform that leverages large language models (LLMs) to extract and organize scientific data from over 10,000 papers. This initiative has... -
MIT Unveils CrysVCD Framework, Boosting AI-Designed Material Stability by 70% and Dramatically Reducing Computational Costs
MIT News USA Overview MIT researchers have developed 'CrysVCD' (crystal generator with valence-constrained design), a framework that significantly improves the stability rate of AI-generated materials. This approach ensures designs satis... -
Japan to Launch National AI Platform, Aiming to Accelerate Material Discovery Tenfold by 2027
RootData Japan Overview Japan's Ministry of Economy, Trade and Industry is spearheading the launch of a national AI data-sharing platform in fiscal year 2027. This initiative aims to centralize and analyze corporate materials data with A...
