Materials Informatics– category –
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Self-Driving Labs Accelerate Material Discovery with Closed-Loop AI and Robotics, Reducing Weeks to Days
Architect Magazine USA Overview A new paradigm of Self-Driving Labs (SDLs) is automating the material discovery process by combining robotic experimentation with closed-loop AI feedback. Facilities like Lawrence Berkeley National Laborat... -
Predictive First-Principles Simulations and AI Accelerate Co-Design of Next-Gen Energy-Efficient AI Hardware
Applied Physics Letters | AIP Publishing USA Overview Predictive first-principles simulations are playing a crucial role in the co-design of next-generation energy-efficient AI system hardware. Machine learning-based material representat... -
ORNL Develops AI-Guided Autonomous System for Inverse Design of Functional Materials from Single Molecules
Facebook (Oak Ridge National Laboratory) USA Overview Researchers at Oak Ridge National Laboratory (ORNL) have developed an AI-guided system that autonomously arranges individual molecules to construct functional materials. This system a... -
LLM Agents Unleash Autonomous Materials Discovery and Design
Unknown International Overview Large language model (LLM)-powered agents are now autonomously executing complex scientific workflows in computational materials science, significantly enhancing research efficiency and reliability. These a... -
AI Revolutionizes Lithium-Ion Battery Electrolyte Discovery with Data-Driven Predictions and Autonomous Experimentation
Energy & Environmental Science International Overview This review highlights artificial intelligence's transformative role in lithium-ion battery electrolyte research, enabling accelerated discovery through data-driven predictions, mecha... -
Materials Informatics Weekly Report September 6, 2026
▼ ▼ ▼ If the infographic makes you want to read the full report, please click the download button below. ▼ ▼ ▼ 📄 Weekly Report September 06, 2026 (PDF) — Download Weekly Report September 06, 2026 (PDF) — DownloadDownload 🎙 Podcast Septe... -
University of Amsterdam Drastically Accelerates Heat Storage Material Discovery with Machine Learning, Rapidly Identifying Promising Salt Hydrates for Thermochemical Storage
University of Amsterdam Netherlands Overview University of Amsterdam scientists have developed a new machine learning approach that significantly accelerates the discovery of heat storage materials. This method requires only the chemical... -
Royal Society of Chemistry Review: AI Accelerates Nanomaterial Design, Synthesis, and Characterization, Boosting Synthesis Efficiency with Closed-Loop Experimentation
The Royal Society of Chemistry UK Overview A Royal Society of Chemistry review highlights how AI integration is revolutionizing nanomaterial science, accelerating discovery, design, synthesis, and characterization. Machine learning, deep... -
Google DeepMind and MOFGen Synthesize Hundreds of Thousands of AI-Designed MOFs in Landmark Breakthrough
Springer Nature Community UK Overview A collaboration between Google DeepMind and MOFGen has led to the successful synthesis of the largest set of AI-designed materials to date. The MOFGen AI agent system autonomously proposes new Metal-... -
Asia Research News Explains AI-Driven Polymer Material Discovery Framework, Promoting Data-Driven Innovation
Asia Research News Singapore Overview Asia Research News presented an AI-driven framework for polymer material discovery, detailing a hierarchically integrated workflow foundational to data-driven polymer innovation. Starting with a poly...
