Machine Learning– tag –
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eXoZymes Selected for DOE’s Genesis Mission to Revolutionize Cell-Free Biomanufacturing with AI-Powered Digital Twins
Stock Titan USA Overview eXoZymes has been chosen for the US Department of Energy's Genesis Mission to advance AI-powered digital twins for cell-free biomanufacturing. The company will leverage hybrid models integrating enzyme kinetics, ... -
Unlocking Real-World Performance: Thermodynamics-Based ML for Energy Material Discovery
arXiv Overview A new perspective article advocates for 'thermodynamics-based machine learning' to overcome the zero-temperature limitations of current AI models in energy material discovery. By integrating crucial factors like entropy an... -
New Ontology Proposed to Standardize Machine Learning Interatomic Potentials (MLIPs), Boosting Reproducibility and Comparability
arXiv Overview An arXiv preprint introduces the 'MLIPs ontology,' an OWL 2 DL framework designed to systematically describe Machine Learning Interatomic Potentials (MLIPs), their hyperparameters, and training/benchmarking data. By standa... -
South China University of Technology and Xi’an Jiaotong University Introduce ‘HULU’ to Accelerate Clean Energy Material Discovery
EurekAlert! China Overview Researchers from South China University of Technology and Xi'an Jiaotong University have developed 'HULU,' a flexible Python framework integrating advanced Machine Learning Potentials (MLPs) with Monte Carlo ad... -
University at Buffalo Secures DOE Genesis Grants to Accelerate AI and Quantum Technology Research in Carbon-Based Fuel Catalysts and Quantum Materials
University at Buffalo (UB) RENEW (Department of Energy Genesis Mission grants) USA Overview University at Buffalo (UB) researchers secured three DOE Genesis mission grants focused on AI and quantum technology. One project, "CLEAR-AI," is... -
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... -
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... -
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... -
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. ...
