New Technology– category –
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Queen Mary University of London Researchers Advance Versatile Quantum Computing with Theoretical Framework for Bose-Hubbard Model Quantum Simulation
Queen Mary University of London UK Overview Researchers including those from Queen Mary University of London have developed a theoretical framework for quantum simulation of the Bose-Hubbard model, aiming for more versatile quantum compu... -
Fraunhofer IWM Advances Materials Research Using IBM Quantum Computers with Hybrid Quantum-Classical Simulation
Fraunhofer IWM Germany Overview Fraunhofer IWM is actively researching the application of quantum computing to materials research, particularly for simulating complex quantum mechanical systems like molecules and solids. Their projects a... -
arXiv Paper “ChemWorld” Introduces Programmable Chemical Worlds for Controlled & Replayable Agent Experimentation in Autonomous Chemistry
arXiv International Overview An arXiv preprint introduces "ChemWorld," a framework for programmable chemical worlds designed to enable controlled and replayable agent experimentation, emphasizing how recent advancements in self-driving l... -
IIT Madras Spinoff Discovered Materials Raises $9 Million Seed to Tackle AI Chip Overheating with AI Agents for Cooler Materials
Business Standard India Overview Discovered Materials, an AI materials startup founded by IIT Madras alumni, raised $9 million in seed funding to develop AI agents for discovering new materials for semiconductor chips. The company aims t... -
EE Times: AI Adoption in Materials R&D Hinges More on People Than Technology, Toyota Example Highlights Accelerated Development
EE Times USA Overview EE Times argues that AI adoption in materials R&D increasingly depends on people and organizational factors rather than technological capabilities, as neural network potentials now offer DFT-comparable accuracy ... -
PMC: Data-Driven Quantum Simulation with Rydberg Atoms Advances Closed-Loop Exploration of Artificial Quantum Materials
PMC International Overview A PMC review highlights Rydberg atom arrays as a versatile platform for data-driven quantum simulation of artificial quantum materials and strongly correlated systems, focusing on engineering and probing materi... -
Royal Society of Chemistry Reviews Data-Driven Interfacial Regulations via Molecular Additive Screening for Batteries and Electrocatalysis
The Royal Society of Chemistry UK Overview A review from The Royal Society of Chemistry highlights data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis. It identifies descriptor tran... -
ACS Publications: OMol25-Trained MLIPs Enable High-Accuracy, High-Throughput Na-ion Battery Electrolyte Solvation Structure Prediction with Experimental Verification
ACS Publications USA Overview An ACS Publications paper demonstrates that OMol25-trained machine learning interatomic potentials (MLIPs) provide an efficient and accurate route to predictive, high-throughput electrolyte simulations for n... -
Deep Intelligent Pharma Expands AI to Materials Science, Accelerating Battery, Semiconductor & Catalyst Development Across 7 Fields
openPR.com China Overview Deep Intelligent Pharma (DIP) is expanding its AI for materials science platform to accelerate the discovery and optimization of advanced materials across seven major fields, including battery materials, semicon... -
ACS Omega: Generative Models & MD Simulations Discover Electrolyte for High-Voltage, Low-Temp Li-ion Batteries
ACS Omega USA Overview Researchers deployed the generative machine learning model G-SchNet, trained on the QM9-GCDQE database, to accelerate electrolyte discovery for lithium-ion batteries operating at high voltages and low temperatures....
