Machine Learning– tag –
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Era of AI-Driven Biocatalyst Design: Advances in High-Throughput Sequencing, Modeling, and Machine Learning Revolutionize Enzyme Engineering
(Scientific journal/review) Unknown Overview A new review discusses the evolution of enzyme engineering from classical strategies to AI-driven biocatalyst design. Advancements in high-throughput sequencing, modeling, and machine learning... -
AI Drives Bioprocess Optimization, Enhancing Titer, Rate, and Yield (TRY) in Pharmaceutical Manufacturing
Corvic AI (Facebook) Global Overview Artificial intelligence (AI) is emerging as a pivotal technology transforming pharmaceutical manufacturing by dramatically boosting efficiency and quality in bioprocess development. Machine learning m... -
ACS Publications Establishes Data-Driven AI Design Framework for Mg-Sr-X Ternary Anodes in High-Performance Mg-Air Batteries
ACS Publications - Industrial & Engineering Chemistry Research USA Overview A paper in ACS Publications establishes an AI-driven alloy design framework for data-driven discovery of ternary anodes for high-performance Mg-air batteries. In... -
Aluminum Alloy Components Boost AI Sensor Reliability in Extreme Temperatures, Optimized by Machine Learning Design
Industry Today China Overview Aluminum alloy components play a crucial role in ensuring the reliability of AI sensors in extreme temperature environments. Machine learning algorithms optimize the thermal performance of these parts, while... -
ArXiv Presents ‘ED-CSP’: A Machine Learning Framework for Crystal Structure Prediction from Sparse Electron Diffraction
arXiv International Overview The paper "ED-CSP" introduces a machine learning framework for predicting crystal structures from sparse electron diffraction (ED) observations. This model co-predicts lattice parameters and fractional atomic... -
Matlantis Webinar Accelerates Computational Materials Design with Integrated CALPHAD, DFT, MLIPs, and AI Agents
Matlantis Japan Overview Matlantis offers an on-demand webinar accelerating computational materials design by integrating CALPHAD, Density Functional Theory (DFT), Machine Learning Interatomic Potentials (MLIPs), and AI-assisted simulati... -
4th CEMDI-PAIMS Symposium Accelerates Materials Discovery Through Computational Materials Science, AI, Data, Experimental Insights, and International Collaboration
EurekAlert! USA Overview The 4th CEMDI-PAIMS Symposium in Montreal discussed advancements in materials discovery through the fusion of computational materials science, AI, data, experimental insights, and international collaboration. Thi... -
Royal Society of Chemistry Unveils Open-Source Software Platform for Automating Massively Parallel Battery Cycling Experiments
The Royal Society of Chemistry UK Overview The Royal Society of Chemistry has announced a scalable open-source software platform for managing and automating massively parallel battery cycling experiments. This platform accelerates new ma... -
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... -
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....
