Materials Informatics– category –
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OSTI.GOV: AI and Autonomous Labs Revolutionize Metal-Organic Framework (MOF) Discovery, Streamlining Synthesis to Evaluation
OSTI.GOV USA Overview This perspective highlights AI's role in revolutionizing Metal-Organic Framework (MOF) discovery by integrating AI with automated high-throughput (HT) technologies. It discusses how autonomous labs, combined with La... -
GPUMD 4.0 Achieves High-Performance Versatile Materials Simulations with Integrated Machine-Learned Potentials
Materials Genome Engineering Advances Global Overview The high-performance molecular dynamics package GPUMD 4.0 has been released, integrating advanced machine-learning potentials (MLPs) based on the NeuroEvolution Potential (NEP) framew... -
University of Ottawa Develops Photonic Simulator Running Over 300 Quantum Processes with Light, Accelerating Quantum Materials Prototyping
Quantum Zeitgeist Canada Overview Researchers at the University of Ottawa and the Nexus for Quantum Technologies Institute have developed a groundbreaking photonic simulator capable of executing over 300 distinct quantum processes using ... -
D-Wave Unveils Superconducting Gate-Model Quantum Computing Platform to Accelerate Quantum Chemistry and Materials Science
D-Wave Canada Overview D-Wave announced the development of a gate-model quantum computing platform to complement its annealing systems, aiming to expand the range of complex problems solvable. Based on a superconducting architecture, thi... -
NVIDIA and Key Japanese Partners Leverage Nemotron to Accelerate Localized AI Models and Materials R&D Agent AI Workflows
Quiver Quantitative Japan Overview NVIDIA announced that Japanese companies and research institutions, including ENEOS Holdings and Tokyo University of Science, are developing localized AI applications using NVIDIA Nemotron open models. ... -
University of Toronto’s AI Autonomous Lab Discovers Six New 3D-Printable Metal Alloys, Outperforming Inconel 625 in Weeks, for Aerospace and Advanced Manufacturing
Acceleration Consortium - University of Toronto / U of T Engineering News Canada Overview Researchers at the University of Toronto's Acceleration Consortium used an AI-driven autonomous lab to discover six new 3D-printable metal alloys t... -
ACS Publications Reviews Evolution of Self-Driving Microscopy: Accelerating Nanoscale Research and Synthetics Integration
Accounts of Chemical Research - ACS Publications USA Overview This paper reviews the evolution of Self-Driving Microscopy (SDM), transitioning from automated to fully autonomous systems by integrating AI and machine learning. SDM acceler... -
Argonne National Lab Advances Autonomous Scientific Discovery with AI-Driven Robotics, Accelerating Development Cycles via ‘RAPID’ Lab
Argonne National Laboratory USA Overview Argonne National Laboratory is pioneering autonomous scientific discovery through AI-driven robotics, aiming to revolutionize experiments in biology, chemistry, and materials science. Its 'Robotic... -
AI Reshapes Chemical Engineering: From Reaction Design to Smart Manufacturing, Accelerating R&D with Closed-Loop Autonomous Labs
EurekAlert! Global Overview AI is fundamentally reshaping chemical engineering, from reaction design to smart manufacturing. Foundation models pre-trained on extensive chemical datasets are adapted for property prediction and novel molec... -
arXiv: New ML Potential ‘TWIN’ Achieves Ab Initio Accuracy and Transferability in Biomolecular Simulations
arXiv Global Overview A new arXiv preprint introduces TWIN (Transferable Water Implicit Network), a highly transferable implicit solvent machine-learning potential (MLP) for biomolecular systems. TWIN, trained solely on ab initio and exp...
