Materials Informatics– category –
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Tech Science Press Journal Features Paradigm Evolution in Materials Science Driven by AI, ML, and Generative Models
Tech Science Press International Overview The latest issue of Tech Science Press's journal 'CMC' (Vol. 88, No. 2, 2026) highlights how AI, machine learning (ML), and generative models are evolving the scientific paradigm of materials sci... -
ACS Paper Introduces Chemistry-Informed ML Framework for High-Accuracy Prediction of Osmabenzene Complex Structural Properties
ACS Publications International Overview Research published in ACS Publications developed a chemistry-informed machine learning (ML) framework for predicting the structural non-planarity of osmabenzene complexes with high accuracy. Utiliz... -
Scientific Generative Language Model LOGOS Integrates Disparate Natural Science Tasks into a Unified Framework, Achieving High Accuracy
arXiv International Overview The scientific generative language model, LOGOS, has been introduced, integrating disparate tasks across natural sciences into a single autoregressive framework. LOGOS encodes diverse scientific objects and t... -
Toward a World Model for Corrosion Science: DeepMind’s GNoME and MatterGen Drive Generative AI Material Models
ResearchGate International Overview Research to build a 'world model' in corrosion science is intensifying, emphasizing the need for generative material models conditioned on corrosion-specific properties. Existing generative AI models l... -
Argonne National Laboratory to Present AI and HPC Integration Research for Accelerated Materials and Molecular Discovery at ISC High Performance 2026
Argonne National Laboratory USA Overview Argonne National Laboratory announced research at ISC High Performance 2026 on integrating Artificial Intelligence (AI), High-Performance Computing (HPC), and quantum computing to accelerate mater... -
InvDesMobility Framework Accelerates Materials Discovery with Reliability-Gated First-Principles Feedback Based on Carrier Mobility
arXiv International Overview InvDesMobility is a closed-loop inverse materials design framework utilizing reliability-gated first-principles feedback for carrier mobility. This framework integrates automated DFT, generative structural pr... -
QNM-Net: Swedish Breakthrough Fuses AI and Physics to Supercharge Photonic Inverse Design
Laser Photonics Rev. スウェーデン Overview Swedish researchers have introduced QNM-Net, a novel method that fuses machine learning with fundamental physics to drastically accelerate the inverse design of photonic components. This hybrid ... -
LLM-Based Autonomous Agent PhyNex Achieves Automated Discovery in Computational Physics, Including Semiconductor Dielectric Spectra Prediction
arXiv International Overview An autonomous agent, PhyNex, has been developed to accelerate scientific discovery in computational physics. By combining LLM-guided search with domain-specific computational tools, PhyNex systematically expl... -
U.S. Educational Organizations Release 10 Best Practices for Generative AI Faculty Development
Every Learner Everywhere and Online Learning Consortium USA Overview Every Learner Everywhere and the Online Learning Consortium have jointly released 10 best practices for faculty development to effectively integrate generative AI into ... -
ChemCopilot’s Generative AI Instantly Generates Novel Molecules from Natural Language Prompts, Automating Molecular Design
ChemCopilot International Overview ChemCopilot unveiled its Generative AI for Molecular Design, enabling scientists to design novel molecules on demand using natural language prompts and instantly generate SMILES strings. This technology...
