Materials Informatics– category –
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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... -
Materials Informatics Weekly Report August 2, 2026
▼ ▼ ▼ If the infographic makes you want to read the full report, please click the download button below. ▼ ▼ ▼ 🎙 Podcast August 02, 2026 (MP3) — Play & Download MaterialsInformaticsEnglishPodcast_20260802.mp3Download View Past Weekly Rep... -
Fujitsu: Explosive Growth of AI Workloads Redefines Semiconductor Industry, Increasing Importance of Silicon Interposers and Next-Gen Thermal Materials
Fujitsu Global Japan Overview Fujitsu states that the explosive growth of AI workloads is fundamentally redefining the semiconductor industry. The article emphasizes the increasing importance of advanced packaging technologies and materi... -
University of Tennessee Secures $20M NSF Grant for ‘ATHENA’ AI-Driven Materials Lab, Poised to Accelerate Material Characterization by 30x
AI Loop USA Overview The University of Tennessee has secured a $20 million National Science Foundation (NSF) grant to establish 'ATHENA,' a new AI-driven materials lab specializing in nanoscale and atomic-scale research. As a key hub in ... -
Unlocking Real-World Performance: Thermodynamics-Based ML for Energy Material Discovery
arXiv Overview A new perspective article advocates for 'thermodynamics-based machine learning' to overcome the zero-temperature limitations of current AI models in energy material discovery. By integrating crucial factors like entropy an... -
New Ontology Proposed to Standardize Machine Learning Interatomic Potentials (MLIPs), Boosting Reproducibility and Comparability
arXiv Overview An arXiv preprint introduces the 'MLIPs ontology,' an OWL 2 DL framework designed to systematically describe Machine Learning Interatomic Potentials (MLIPs), their hyperparameters, and training/benchmarking data. By standa... -
South China University of Technology and Xi’an Jiaotong University Introduce ‘HULU’ to Accelerate Clean Energy Material Discovery
EurekAlert! China Overview Researchers from South China University of Technology and Xi'an Jiaotong University have developed 'HULU,' a flexible Python framework integrating advanced Machine Learning Potentials (MLPs) with Monte Carlo ad... -
University at Buffalo Secures DOE Genesis Grants to Accelerate AI and Quantum Technology Research in Carbon-Based Fuel Catalysts and Quantum Materials
University at Buffalo (UB) RENEW (Department of Energy Genesis Mission grants) USA Overview University at Buffalo (UB) researchers secured three DOE Genesis mission grants focused on AI and quantum technology. One project, "CLEAR-AI," is...
