June 2026– date –
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Spatial Computing and Generative AI: Propelling the Materials & Chemicals Market to $3.78 Billion by 2026, Intel Forecasts
Intel Market Research 多国籍 Overview Intel Market Research projects the global Spatial Computing Generative AI Materials & Chemicals Technology market to reach $3.78 billion by 2026. This rapid growth is driven by the convergence of... -
arXiv Paper Presents ‘AutoPot’: Automated, Massively Parallel Workflow for Constructing Machine-Learning Potentials
arXiv USA Overview A new preprint on arXiv introduces 'AutoPot,' an automated and massively parallelized workflow for constructing Machine Learning Interatomic Potentials (MLIPs). MLIPs bring quantum accuracy to atomic modeling, enabling... -
arXiv Paper Identifies Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
arXiv USA Overview A new arXiv paper reviews the rapid advancements of machine learning (ML) in atomic modeling and the growing centrality of interatomic potentials in materials science. It discusses six open questions as key challenges ... -
IJCRT.org Paper Argues AI and Mathematical Modeling Fusion Revolutionizes Materials Engineering, Accelerating Discovery
IJCRT.org India Overview A paper published on IJCRT.org argues that the combination of mathematical modeling and AI is revolutionizing materials engineering by providing predictive insights and accelerating materials discovery. Machine l... -
Researchers Develop ‘React-OT’ AI for Fast and Accurate Prediction of Chemical Reaction Transition States
The Research Code Unknown Overview Researchers have developed 'React-OT,' a machine learning approach capable of predicting chemical reaction transition states with unprecedented speed and accuracy. This technology will significantly acc... -
Dunia Innovations Invests €280M in Berlin GigaLab to Industrialize AI-Driven Materials Discovery
Scouts by Yutori Germany Overview Dunia Innovations has invested €280 million in the Berlin GigaLab facility, marking a significant step towards industrializing AI-driven materials discovery. This massive investment focuses on applied re... -
NUS Designs Catalyst for Urea Fertilizer Production from CO2 and Waste Nitrates, Integrating LLMs, DFT, and Experimentation
NUS Faculty of Science Singapore Overview Researchers at the National University of Singapore (NUS) developed a computationally guided strategy, integrating Large Language Models (LLMs), Density Functional Theory (DFT), and experimental ... -
University of Washington Accelerates Stacked Atomic Sheet and Quantum Computer Material Development by Fusing AI and Quantum Computing
University of Washington USA Overview University of Washington researchers have demonstrated how combining artificial intelligence (AI) and quantum computing significantly accelerates the development of quantum materials. One study (PNAS... -
German Federal Ministry of Education and Research Funds ASCEND Project with €30M to Accelerate AI-Driven Catalyst Development via Autonomous Labs
e-conversion Germany Overview The German Federal Ministry of Education and Research (BMFTR) launched the 'ASCEND' project, investing €30 million across six research and industry partners, including Helmholtz-Zentrum Berlin and BASF, to a... -
U.S. DOE National Labs Accelerate Materials Science & Energy Research by Fusing AI and Human Expertise
AI & Tech News Engine USA Overview A new generation of materials scientists is accelerating materials discovery in energy storage, aerospace, and manufacturing by combining traditional metallurgy expertise with cutting-edge computational...
