New Technology– category –
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National University of Singapore Develops AI Method to Learn Macroscopic Material Behavior from Microscopic Data, Accelerating Discovery
National University of Singapore (NUS) Faculty of Science Singapore Overview Researchers at the National University of Singapore developed an innovative AI method that learns complex macroscopic material behaviors from microscopic data. ... -
Tokyo University of Science Unveils AI-Driven Inverse Design for High-Speed Spin Wave Computing
EurekAlert! Japan Overview Researchers at Tokyo University of Science have developed an AI-driven inverse design framework, integrating genetic algorithms with micromagnetic simulations, to optimize magnonic crystal (MC) structures. This... -
Northwestern University Accelerates Electronic and Energy Material Design with Property-Predicting Machine Learning Model ‘LVGP’
Northwestern University (Paula M. Trienens Institute For Sustainability And Energy) USA Overview Northwestern University's Wei Chen research group developed the Latent Variable Gaussian Process (LVGP) machine learning model, which conver... -
Computational Powerhouse: AI and DFT Drive Next-Gen Sodium-Ion Battery Innovation
MDPI Overview A new MDPI review highlights the critical role of theoretical calculations, including Density Functional Theory (DFT) and Molecular Dynamics (MD), alongside Machine Learning (ML), in accelerating the discovery and optimizat... -
CuspAI Completes $450M Series B Funding Co-led by NEA and Kleiner Perkins, Advancing Generative AI Platform ‘MIRA’ for Materials Discovery
New Enterprise Associates (NEA) USA Overview CuspAI, a generative AI platform for materials discovery, completed a $450 million Series B funding round co-led by New Enterprise Associates (NEA) and Kleiner Perkins, with participation from... -
Atomicrex: Open-Source Platform Unlocks Large-Scale Atomic Interaction Models for Faster Materials Discovery
atomicrex Overview A new open-source code, 'atomicrex,' is poised to accelerate computational materials science by dramatically simplifying the construction of interatomic potentials for simulations involving thousands of atoms or more. ... -
Energy-Constrained MLIP Embedding Improves Prediction Accuracy for Hydrogen Evolution Electrocatalysts
ACS Publications (Journal of Chemical Information and Modeling) Overview Researchers developed an "energy-constrained" machine learning framework integrating MLIP-derived energy descriptors with pre-trained Crystal Hamiltonian Graph Neur... -
Machine Learning Interatomic Potentials: Foundation Models Reshape Materials Discovery
Review of Peer-Reviewed Articles Overview A new comprehensive review critically examines Machine Learning Interatomic Potentials (MLIPs), a transformative technology bridging the accuracy of quantum mechanics with the efficiency of class... -
NSF Commits $380M to National Self-Driving Lab Network, Accelerating Discovery 100x
UNC-Chapel Hill USA Overview The National Science Foundation (NSF) has committed $380 million to establish a nationwide network of 'programmable cloud laboratories,' with NC State University's 'SPEED Lab' receiving $20 million to lead ef... -
Physics-Based Generative AI Accelerates Design of High-Performance 3D Porous Media
arXiv Overview A new preprint introduces a physics-based generative AI framework for designing 3D porous media with precise physical properties like porosity and permeability. This framework integrates a property-aware variational autoen...
