Materials Informatics– category –
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Google DeepMind and Meta Spearhead Curated AI Resources for Scientific Breakthroughs
GitHub USA Overview A new 'awesome-ai-for-science' GitHub repository, a meticulously curated list of AI tools and datasets, has been released to accelerate scientific discovery across physics, chemistry, biology, and materials science. S... -
Accelerating Materials Discovery: Ensemble Uncertainty Quantification Enhances Neural Network Interatomic Potentials
arXiv USA Overview A recent comparative study rigorously evaluates ensemble-based uncertainty quantification (UQ) methods for Neural Network Interatomic Potentials (NNIPs), aiming to develop robust machine learning interatomic potentials... -
Chalmers Pushes Automated Machine Learning for Breakthroughs in Computational Chemistry and Materials Science
Chalmers University of Technology (AIMLeNS) スウェーデン Overview A recent AI4Science seminar at Chalmers University of Technology underscored the transformative impact of machine learning (ML) on computational chemistry and materials re... -
Accelerating Innovation: Lawrence Berkeley’s A-Lab Leverages AI for Self-Driving Materials Discovery
TheSequence USA Overview Self-driving labs are fundamentally transforming materials science by autonomously executing experiments and learning from both successes and failures. Lawrence Berkeley National Laboratory's A-Lab exemplifies th... -
AI-Driven Design Transforms Metal-Organic Materials to Dynamic Networks, Powering Self-Driving Labs for Accelerated Discovery
MDPI International Overview This paper proposes extending AI-driven design of metal-organic materials (MOMs) beyond traditional crystalline MOFs to dynamic coordination networks, such as metal-polyphenol networks (MPNs). It integrates pr... -
AI and Machine Learning Drive Breakthroughs in Sustainable Catalyst Design, Expanding Applications from CO2 Conversion to Polymer Recycling
MDPI Switzerland Overview The integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming catalyst design and sustainable chemical process development. These technologies accelerate new catalyst di... -
IBM Unveils Sub-1nm ‘NanoStack’ Semiconductor Technology, Targeting 50% AI Chip Performance Boost or 70% Energy Reduction
Data Center Knowledge USA Overview IBM has announced a sub-1-nanometer 'NanoStack' semiconductor technology, designed to extend chip scaling for AI workloads. This research device employs sequential 3D integration to vertically stack tra... -
Unsupervised ML Overcomes Dimensionality Bottlenecks to Fast-Track Novel Lithium Battery Materials
未公表研究 Unknown Overview A groundbreaking statistical framework leverages unsupervised machine learning (ML) to significantly accelerate the discovery of novel lithium-based battery materials. This approach uniquely mitigates informat... -
Materials Informatics Weekly Report June 27, 2026
▼ ▼ ▼ If the infographic makes you want to read the full report, please click the download button below. ▼ ▼ ▼ 📄 Weekly Report June 27, 2026 (PDF) — Download Weekly Report June 27, 2026 (PDF) — DownloadDownload 🎙 Podcast June 27, 2026 (... -
QUASIMODO Project: Vilnius University and International Team Engineer Next-Gen Quantum Simulators with Multicomponent Ultracold Atoms
Vilnius University (VU) リトアニア Overview An international team, spearheaded by physicists from Vilnius University, is developing next-generation quantum simulators under the QUASIMODO project. This ambitious initiative leverages multi...
