New Technology– category –
-
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 ... -
Machine Learning Revolutionizes Magnetic Functional Alloy Design: Generative GNNs and High-Throughput DFT Discover 2D Magnets
AIP Advances USA Overview This article details recent advancements in the design of magnetic functional alloys (MFAs) leveraging machine learning (ML), highlighting how ML has contributed to the design of magnetocaloric, magnetostrictive... -
Oak Ridge National Lab’s AI-Guided System Autonomously Arranges Molecules to Build Functional Materials, Expanding Electronic and Quantum Material Possibilities
Facebook (Oak Ridge National Laboratory) USA Overview Oak Ridge National Laboratory announced an AI-guided system that autonomously arranges individual molecules to construct functional materials. This technology, where AI guides an ultr... -
Berkeley Lab Revolutionizes Solid Electrolyte Design with Hybrid ML and Experimental Data, Discovering Ion Transport Dominated by Dissociation Mechanisms, Not Viscosity
Facebook (Berkeley Lab Energy Storage Center) USA Overview A Berkeley Lab team adopted a hybrid machine learning approach combining experimental data and computational descriptors, revealing that ion transport in ionic liquids is primari... -
SES AI’s ‘Molecular Universe’ Platform Accelerates Drone Battery Development by Years, Identifies Optimal Materials from 100 Million Molecules in Weeks
UST USA Overview UST reports SES AI is revolutionizing battery development by applying AI to efficiently identify materials with specific application requirements. Their 'Molecular Universe' platform maps approximately 100 million potent... -
SandboxAQ’s AQCat AI Model on Claude Science Accelerates Catalyst Screening 20,000x, Considers Magnetic Behavior, Trained on High-Fidelity Data
Quantum Zeitgeist USA Overview SandboxAQ has released AQCat, an AI model running on Claude Science, capable of accelerating potential catalyst screening by up to 20,000 times faster than traditional lab methods. AQCat predicts catalyst e...
