Machine Learning– tag –
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Enzyme Engineering Achieves Leap Forward with AI and Machine Learning: Generative AI Models Predict Sequence-Structure-Function Relationships, Accelerating Novel Biocatalyst Design
ACS Publications USA Overview This review discusses the dramatic evolution of enzyme engineering from classical methods to AI-assisted biocatalyst development. The application of machine learning and generative AI models enables predicti... -
Big Data and AI/Machine Learning Revolutionize Biopharmaceutical Manufacturing, Enabling High-Speed, Efficiency, and Quality through Interoperable Data Management
Frontiers Switzerland Overview This research topic delves into how big data-driven approaches, including interoperable data management solutions, modeling, simulation, and digital twins, are transforming biopharmaceutical process enginee... -
Big Data and Predictive Analytics Revolutionize Bioreactor Performance, Enhancing Robustness, Efficiency, and Decision-Making
Facebook (Infors HT) Switzerland Overview The application of big data and predictive analytics to bioreactors is highlighted as maximizing potential by enhancing process robustness, efficiency, and decision-making. Utilizing real-time da... -
AI-Integrated Bioreactors Revolutionize Allogeneic Cell Therapy Manufacturing, Unlocking Scalability and Affordability
sanandres.uep.edu.py Paraguay Overview Allogeneic cell therapy manufacturing is undergoing a significant transformation, driven by the integration of automated, closed-system bioreactors and AI/machine learning for real-time monitoring. ... -
Machine Learning Potentials Grapple with Long-Range Interactions in Atmospheric Modeling: A DTU Deep Dive
DTU Research Database Denmark Overview Researchers at the Technical University of Denmark (DTU) evaluated AIMNet2 and PaiNN, two machine-learned interatomic potentials (MLIPs), for modeling molecular collisions critical to atmospheric cl... -
Hessian-Based Molecular Conformation Augmentation Improves Scalability and Efficiency of Machine Learning Interatomic Potentials
arXiv (Preprint Server) International Overview This preprint proposes two Hessian-derived data augmentation schemes, isotropic Gaussian displacement (UniAug) and normal mode-weighted displacement (ModeAug), for machine-learning interatom... -
Geometry-Guided Model CDSM Achieves DFT-Comparable Accuracy in Collagen Structure Prediction, Cutting Computational Cost by 400-790x
bioRxiv USA Overview CDSM (Empirical Geometry-Guided Model) enables robust, high-throughput collagen structure prediction by explicitly encoding empirical geometric constraints. The model significantly compresses the structure-prediction... -
Physics-Aware AI Indispensable for Materials Research: Performance Gaps Highlighted in Thermal Conductivity Prediction
Columbia Engineering USA Overview Columbia Engineering emphasizes the critical need for physics-aware AI in materials science, particularly for machine learning interatomic potentials (MLPs) that predict macroscopic properties from quant... -
Multi-Agent LLM ‘MAESTRO’ Designs Single-Atom Catalysts via Reasoning, Breaking Conventional Limits
ACS Publications USA Overview MAESTRO (Multi-Agent-based Electrocatalyst Search Through Reasoning and Optimization) is a reasoning-driven framework where multiple LLMs collaboratively design high-performance single-atom catalysts. LLM ag... -
Seoul National University AI Discovers Two Lead-Free High-k Dielectric Materials from 150 Million Virtual Compositions for Future Electronics
Almerja South Korea Overview Researchers at Seoul National University leveraged AI to screen approximately 150 million virtual chemical compositions, identifying two promising lead-free dielectric materials for future electronics. This '...
