June 2026– date –
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Apoha Raises $36M to Scale AI Platform for Designing Proteins, Food Ingredients, and New Materials with Liquid-State Molecular Behavior Data
PPTI News UK/USA Overview London and San Francisco-based startup Apoha has secured $36 million in funding to scale its AI platform for designing proteins, food ingredients, pharmaceuticals, and advanced materials. The company is building... -
ResearchGate: NequIP GNN Predicts Amorphous Material Many-Body Interactions at 10,000x Lower Cost than DFT
ResearchGate Unknown Overview Recent research applied NequIP, an equivariant message passing graph neural network (GNN), to predict many-body interactions in model soft glasses of solvent-free polymer-grafted nanoparticles (PGNs). NequIP... -
NC State’s PoLARIS Autonomous Lab Discovers Lead-Free Nanoplatelets in 12 Hours, Accelerating Scientific Discovery by 100x
NC State News USA Overview NC State University's Self-Driving Lab (SDL) technology intelligently plans and executes experiments to discover optimal "recipes" for new molecules and materials, accelerating discovery up to 100 times faster ... -
University of Toronto’s Acceleration Consortium Drives Material Development with Autonomous Labs (SDLs), Dramatically Cutting Time and Cost
Harrowings - Substack Canada Overview The University of Toronto's Acceleration Consortium (AC) is demonstrating how Self-Driving Labs (SDLs) are maturing into a transformative infrastructural layer for physical sciences, aiming to signif... -
DOE-University Alliance Accelerates Custom Polymer Development via Autonomous AI Inverse Design Workflow and Polybot
Tech Briefs USA Overview Researchers from Argonne National Laboratory (DOE), the University of Chicago, and Purdue University have demonstrated a faster route from target properties to polymer recipes using an autonomous AI inverse desig... -
Argonne National Laboratory Leverages AI and ML for Atomic-Level Design of 2D MXene Materials, Opening Diverse Applications
Argonne National Laboratory USA Overview Scientists at Argonne National Laboratory have unveiled new insights into the design and application of MXene, a rapidly growing class of 2D materials. By utilizing AI and machine learning, resear... -
IBS Develops Crossbreeding Neural Network Enabling AI to Discover Catalysts from Disparate Material Families
Lab Manager South Korea Overview Researchers at the Institute for Basic Science have developed the "Crossbreeding Neural Network (CBNN)" deep learning model to overcome limitations in traditional machine learning for materials. This mode... -
UChicago’s “ElectrolyteGPT” Unleashes AI-Powered Autonomous Generation of Battery Electrolyte Formulations
UChicago News USA Overview Researchers at the University of Chicago Pritzker School of Molecular Engineering have developed "ElectrolyteGPT," an AI model capable of generating entire battery electrolyte compositions autonomously. This AI... -
Argonne National Laboratory Unveils Roadmap for AI-Driven Autonomous Labs to Revolutionize Battery Research with Large Language Models
Argonne National Laboratory USA Overview Researchers at Argonne National Laboratory have outlined a comprehensive technical roadmap for applying Large Language Models (LLMs) to battery research. Integrated into AI-driven autonomous labs ... -
World Economic Forum: AI-Driven Materials Discovery Boosts Industrial Experiment Throughput by 5500%, Cuts R&D to Weeks
The World Economic Forum Switzerland Overview The World Economic Forum announced the third cohort results of its MINDS initiative, reporting that closed-loop autonomous platforms have boosted industrial experiment throughput by up to 5,5...
