High-throughput Screening– tag –
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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... -
Lonza and Entropic Unveil Scalable 96-Well 3D Human Skin Organoid Platform for Accelerated Screening
Scientist.com Switzerland Overview Lonza and Entropic have jointly developed a scalable 96-well 3D human skin organoid model, dramatically improving the efficiency of skin barrier function and injury screening. Utilizing Entropic's ZYRAL... -
Angiogenic Tumor Organoids Revolutionize Model Design in Cell Therapy Development, RegMedNet Reports Critical Importance
RegMedNet UK Overview RegMedNet reports on the critical role of model design in cell therapy development, highlighting how the emergence of angiogenic tumor organoids is revolutionizing research. These advanced organoids enable researche... -
Machine Learning Revolutionizes Magnetic Functional Alloy Design: Generative GNNs and High-Throughput DFT Discover 2D Magnets
AIP Advances USA Overview This article details recent advancements in the design of magnetic functional alloys (MFAs) leveraging machine learning (ML), highlighting how ML has contributed to the design of magnetocaloric, magnetostrictive... -
AI-Driven Catalysis Discovery Cuts Drug Development Time by Over a Decade via High-Throughput Experimentation and Machine Learning
News-Medical UK Overview News-Medical reports that modern synthetic tools, particularly AI-driven catalysis discovery, are revolutionizing chemistry and drug development. By combining high-throughput experimentation with machine learning... -
AI Accelerates Material Discovery: Novel GNN Model Generates Electronic Fingerprints for High-Throughput Catalyst & Battery Research
Facebook USA Overview A novel Graph Neural Network (GNN) model has been developed to rapidly generate electronic fingerprints, significantly accelerating the discovery of new materials for catalysts and batteries at a fraction of previou... -
AI-Powered Autonomous Labs Face Bottleneck: Hypothesis Generation Outpaces Physical Validation
vertexaisearch.cloud.google.com Unknown Overview Autonomous laboratories, integrating robotics, high-throughput experiments, and AI, are revolutionizing scientific discovery by enabling continuous, adaptive learning cycles. While these s... -
DeepH-pack Unites Ab Initio Calculations and Deep Learning to Accelerate AI-Driven Electronic Structure Modeling
Facebook (reposting about DeepH-pack) USA Overview DeepH-pack has been introduced as a general-purpose neural network package that combines ab initio calculations with deep learning to accelerate electronic structure modeling. This tool ... -
AI Revolutionizes Digital Discovery of MOFs: Integrating Computational Modeling and High-Throughput Screening to Slash Development Times
ACS Publications USA Overview Artificial intelligence (AI) and machine learning are revolutionizing the discovery and optimization of metal-organic framework (MOF)-based multifunctional materials. AI enables rapid exploration of MOFs' va... -
Graph Neural Network Accelerates Novel Material Discovery with Reduced Computational Cost for Catalysts and Batteries
PRX Intelligence USA Overview A new Graph Neural Network (GNN) model published in PRX Intelligence significantly accelerates novel material prediction and discovery while drastically lowering computational costs. The model leverages proj...
