August 2026– date –
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Deletion Method Outperforms Generative AI Approaches for Extracting Atomic Environments in MLIP-Driven MD Simulations
arXiv USA Overview A systematic analysis of optimal methods for extracting small atomic environments suitable for DFT calculations from large structures, a challenge in applying machine learning interatomic potentials (MLIPs) to large-sc... -
Atomic Representations from Pretrained MLIPs Prove Effective for Materials Generative Model Evaluation
arXiv USA Overview This paper demonstrates that atomic average features derived from pretrained machine learning interatomic potentials (MLIPs) like MACE are effective for evaluating the outputs of materials generative models. The resear... -
AI-Driven Battery Chemistry Simulators Evaluated for Adversarial Robustness: Output Validity vs. Chemical Sensitivity
OpenReview USA Overview The first systematic evaluation of adversarial robustness for AI-powered battery chemistry simulators, including MLIPs like MACE-MP-0 and CHGNet, revealed that physically constrained models maintain 90-92% physica... -
Quantum Machine Learning Interatomic Potential Achieves Enhanced Molecular Energy Prediction with Variational Quantum Algorithm
arXiv USA Overview This study applied quantum circuit learning to machine learning interatomic potentials (MLIPs), improving molecular dataset energy predictions. Using a quantum transfer learning architecture, the ANI model was retraine... -
Physics-Informed Generative AI Drives Autonomous Discovery of Porous Oxide Energy Materials
arXiv USA Overview This paper proposes a roadmap for advancing generative AI into physics-informed, application- and synthesis-aware inverse design for next-generation energy storage materials, particularly porous oxide electrodes. It in... -
Yale & PNNL Discover Groundbreaking Solid-State Electrolyte N2116, Reducing Lithium Use by 70% with Microsoft AI
Facebook (Yale Engineering) USA Overview Researchers at Yale University and Pacific Northwest National Laboratory (PNNL), in collaboration with Microsoft AI, have discovered N2116, a novel solid-state electrolyte capable of reducing lith... -
AI Revolutionizes Materials Discovery: Comprehensive Review Maps Generative Design to Autonomous Labs
ACS Chemical Reviews USA Overview A new review article thoroughly examines emerging AI methodologies for accelerating materials discovery, highlighting the convergence of computational design, data infrastructure, synthesis planning, and... -
Chinese Researchers Achieve 29.71% Overall Efficiency and Over 91% Stability After 2000 Hours with Sulfide-Based Four-Terminal Perovskite/CIGS Tandem Solar Cell
pv magazine Global China Overview Chinese researchers have developed a four-terminal perovskite/CIGS tandem solar cell with an impressive overall power conversion efficiency of 29.71%, utilizing sulfide-based coordination engineering. Th... -
Review Paper Highlights Critical Role of Inorganic Hole Transport Materials in Advancing n-i-p Perovskite Solar Cells, Contributing to 27% Efficiency and Enhanced Stability
PMC - NIH Global Overview This review paper focuses on the rapid advancements in perovskite solar cells (PSCs), specifically their achievement of a certified world record efficiency of approximately 27% and their potential for low manufa... -
UK-Swedish POP-PV Project Targets Unlocking Europe’s Underutilized Rooftops with Lightweight Perovskite Solar Cells
Solar Power Portal UK/Sweden Overview The POP-PV project, backed by Innovate UK and Vinnova, has launched to address critical manufacturing and material challenges hindering the commercialization of lightweight perovskite solar cells. Th...
