Materials Informatics– category –
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Integrated Modeling Framework Proves Effective for Liquid Electrolyte Design, Bioengineer.org Publishes Reusability Report
Bioengineer.org Overview A reusability report by Bioengineer.org evaluates a unified machine learning framework for liquid electrolyte formulation design. This framework integrates molecular structure representation and composition-level... -
Forbes Reports LAM Research and Applied Materials Enhance Semiconductor Manufacturing Productivity by up to 35x with AI Integration; Synopsys, Siemens, and NVIDIA Strengthen Collaboration
Forbes USA Overview Forbes highlights how leading semiconductor firms like LAM Research and Applied Materials integrate AI to boost design and manufacturing productivity. LAM's "Semiverse" and Applied Materials' "Ai^x" leverage AI and di... -
AI-Powered Simulations Unlock Next-Gen Battery Design: How MLIPs Bridge the Gap Between DFT and Classical MD
ResearchGate Overview A recent review highlights how Machine Learning Interatomic Potentials (MLIPs) are effectively addressing persistent challenges in computational materials science for battery development. By bridging the gap between... -
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...
