2026– date –
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Forbes Highlights Emerging Computing Ecosystem: AI, Quantum, Biological, and Chemical Convergence Accelerates Scientific Discovery
Forbes USA Overview Forbes reports on an emerging computing ecosystem where AI, quantum, biological, and chemical computing paradigms converge to accelerate scientific discovery. AI acts as a strategic intelligence layer, optimizing algo... -
arXiv Paper Evaluates Universal MLIPs, Finds DFT Fine-Tuning Essential for Accuracy in Reactive Processes
arXiv USA Overview A new arXiv study evaluated five universal machine-learning interatomic potentials (MLIPs) for quantitative materials modeling involving reactive events. It revealed that while universal MLIPs are becoming general-purp... -
arXiv Presents Structural Novelty Analysis for AI-Generated Crystals, Reveals Bias Towards Known Prototypes
arXiv USA Overview A new workflow for assessing the structural novelty of inorganic crystals generated by AI models has been published on arXiv. The study evaluates whether AI-generated crystals are duplicates, reproducible via elemental... -
North Carolina State University Unveils ‘Flex-Cat’ Autonomous Chemistry Lab, Discovering Catalysts That Can Switch Products On-Demand via AI and Robotics
North Carolina State University News USA Overview Researchers at North Carolina State University have developed Flex-Cat, a self-driving chemistry lab integrating robotics, high-pressure chemical reactors, automated analysis, and AI. Thi... -
MIT Develops Information Theory-Based MLP to Significantly Enhance Metal Alloy Behavior Modeling Accuracy
MIT News USA Overview MIT researchers have developed a novel framework for modeling metallic behavior using machine learning potentials (MLPs) trained on datasets that efficiently capture diverse atomic environments in chemically disorde... -
MLIP Enhanced Sampling Simulations Uncover Dynamic Conformations and Catalytic Implications of Au9 Nanocluster Confined in UiO-66-NH2 MOF
ChemRxiv USA Overview This research meticulously investigated the structural and dynamic behavior of an Au9 nanocluster confined within a UiO-66-NH2 MOF using machine learning interatomic potential (MLIP)-driven enhanced sampling simulat... -
LLM Automates Thermal Transport Screening in Co-Cr-Ni Medium-Entropy Alloys, Proving Concept for Autonomous Materials Discovery Workflow
ChemRxiv USA Overview This study presents a reproducible, closed-loop workflow integrating a Large Language Model (LLM) decision module with molecular dynamics simulations to automate thermal transport screening in Co-Cr-Ni medium-entrop... -
U.S. Department of Energy (DOE) Partners with Microsoft to Leverage AI for Battery Electrolyte and Clean Energy Material Discovery
OSTI (Office of Scientific and Technical Information) USA Overview The U.S. Department of Energy (DOE) is actively advancing its AI innovation ecosystem, collaborating with Microsoft to identify new battery electrolyte materials through ... -
SpinQ Launches Gate-Model Quantum Computing Platform to Enhance Quantum Simulation, Accelerating Materials Science and Drug Discovery
SpinQ China Overview SpinQ has unveiled a gate-model quantum computing platform designed to enhance quantum simulation for chemistry and materials science. The platform emphasizes its ability to more accurately model molecules and conden... -
ACS Publications Reveals MACE-QEq Potential, Addressing MLIP Challenges in Long-Range Electrostatics and Charge Transfer, Boosting Accuracy for ZnO and Water Systems
Journal of Chemical Theory and Computation (ACS Publications) USA Overview This research augments the equivariant Multi-Atomic Cluster Expansion (MACE) potential with a charge equilibration (QEq) framework to address challenges in machin...
