September 2026– date –
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Citrine Informatics Partners with Forum Rathenau to Accelerate Germany’s Green Deep Tech Ecosystem with AI Platform
AZoM Germany Overview Forum Rathenau and Citrine Informatics announced a strategic partnership to accelerate Germany's green deep tech ecosystem. This collaboration supports the 'Scale New Chemistry' bootcamp, aiming to transform challen... -
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... -
Self-Driving Labs: Exploring the Feasibility of Closed-Loop Experimentation in Pharma R&D, Addressing XRD Data Challenges
Sakara Digital USA Overview This article discusses the feasibility of autonomous closed-loop experimentation in pharmaceutical R&D, identifying criteria for suitability and practical bottlenecks. The A-Lab team emphasizes demonstrati... -
X2DB: Unifying Experimental and Computational 2D Materials Data to Accelerate AI-Driven Discovery
DTU Research Database Denmark Overview A new open infrastructure, X2DB, has been established to integrate experimental and computational data for 2D materials on a massive scale, centralizing previously dispersed knowledge. This robust d... -
Argonne National Laboratory Accelerates AI-Driven Materials Discovery: 18 Promising Battery Candidates Identified from 500,000 Materials in 80 Hours
Facebook (Argonne National Laboratory) USA Overview Argonne National Laboratory dramatically accelerated materials discovery using an AI-driven method, identifying 18 promising battery development candidates from 500,000 stable materials... -
Autonomous Labs Accelerate Materials Discovery: Key US Labs Invent New Materials with AI-Driven Robots
Architect Magazine USA Overview AI-driven autonomous laboratories are rapidly advancing, exemplified by Lawrence Berkeley National Laboratory's A-Lab, Carnegie Mellon University's Materials Innovation Cloud Lab (MICL), and Texas A&M ... -
Tohoku University Unveils AI-Powered ‘Self-Driving Lab’ for Accelerated Polymer Discovery
Tohoku University (WPI-AIMR) Japan Overview Researchers at Tohoku University's WPI-AIMR have developed an integrated, closed-loop AI system that dramatically accelerates the discovery of new polymer materials. By seamlessly linking polym... -
Machine Learning Accelerates Discovery of Phase-Stable Formamidinium-Based Perovskites with Automated Synthesis Platform
Energy & Environmental Science, The Royal Society of Chemistry UK Overview This study demonstrated that machine learning (ML) prediction can accelerate the discovery of phase-stable formamidinium-based perovskites. By leveraging datasets... -
ChemCopilot Q3 2026 Report Highlights ELLIS Finland and Acceleration Consortium Leading Chemical AI and Autonomous Labs
ChemCopilot USA Overview ChemCopilot released its Q3 2026 report on chemical AI, autonomous labs, and digital R&D transformation, spotlighting ELLIS Institute Finland for its expertise in predictive signal extraction in low-data envi... -
Unveiling Physical Meaning in Materials AI: Beyond Prediction to Mechanism Identification and Design Guidance
ACS Materials Au USA Overview This perspective paper explores how materials AI can move beyond mere property prediction to contribute to physical interpretation, mechanism identification, and design guidance. It advocates for evaluating ...
