September 2026– date –
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Oak Ridge National Lab’s AI System Autonomously Builds Custom Atomic-Scale Materials, Revolutionizing Electronic and Quantum Material Design
Oak Ridge National Laboratory USA Overview Oak Ridge National Laboratory has developed an AI system that autonomously constructs custom atomic-scale patterns by guiding an ultra-sharp microscope tip to manipulate individual molecules on ... -
SciTechDaily: AI Discovers Two Promising High-Temperature Dielectric Candidates from 150 Million Virtual Materials for Future Electronics, Meeting EV and Aerospace Demands
SciTechDaily USA Overview SciTechDaily reports that an AI-driven method identified two promising dielectric material candidates from 150 million virtual chemical spaces for future electronics. This inverse design strategy combines multim... -
Energy & Environmental Science: AI Optimizes Li-Ion Battery Electrolytes via Data-Driven Methods, Accelerating Discovery of Novel Solvents, Salts, and Solid Electrolytes
Energy & Environmental Science UK Overview A review in Energy & Environmental Science highlights AI's transformative impact on Li-ion battery electrolyte research, leveraging data-driven prediction, mechanistic analysis, and intellig... -
MDPI Review: AI Revolutionizes Li-ion Battery Development with Closed-Loop Discovery, State Prediction, and Trustworthy Management
MDPI Switzerland Overview A review published in MDPI reveals AI is transforming lithium-ion battery (LIB) research into a closed-loop paradigm integrating data-driven prediction, mechanistic interpretation, and experimental validation. T... -
Cypris AI: Generative Models, GNNs, and Autonomous Labs Slash Materials R&D to 1-2 Years; Google DeepMind’s GNoME Predicts 2.4 Million New Materials
Cypris AI USA Overview Cypris AI reports that the integration of generative models, Graph Neural Networks (GNNs), and autonomous labs is drastically cutting materials R&D timelines from 10-20 years to just 1-2 years. Enhanced GNN arc... -
AI-Driven Catalysis Discovery Cuts Drug Development Time by Over a Decade via High-Throughput Experimentation and Machine Learning
News-Medical UK Overview News-Medical reports that modern synthetic tools, particularly AI-driven catalysis discovery, are revolutionizing chemistry and drug development. By combining high-throughput experimentation with machine learning... -
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... -
Oxford PV Achieves 26.9% Efficiency in Residential Panels; University of Queensland Sustains 16.65% Lead-Free Perovskite for Over 1000 Hours
Facebook UK/Australia Overview Oxford PV, an Oxford University spin-off, has recorded a remarkable 26.9% efficiency in residential-sized solar panels by integrating innovative perovskite with traditional silicon. Concurrently, a Universi... -
SNS Insider Report: Perovskite Solar Cell Market Projected to Reach $73 Billion by 2035 with 69.45% CAGR
SNS Insider International Overview This article provides an overview of a market research report published by SNS Insider. The global perovskite solar cell market is projected to grow from $376.7 million in 2025 to $73 billion by 2035, e...
