Research authored by partners from the Bottle Consortium and published in Nature Communications this month aims to challenge ...
Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...
The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20^100 ...
Microelectromechanical systems (MEMS) electrothermal actuators are widely used in applications ranging from micro-optics and microfluidics to nanomaterial testing, thanks to their compact size and ...
Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...
Imagine having a super-powered lens that uncovers hidden secrets of ultra-thin materials used in our gadgets. Research led by University of Florida engineering professor Megan Butala enables a novel ...
Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
A research team of mathematicians and computer scientists has used machine learning to reveal new mathematical structure within the theory of finite groups. By training neural networks to recognise ...
Retrospective validation of a novel multimodal AI prognostic tool integrating digital pathology and clinical data against real world data and Oncotype DX in a Swiss breast cancer cohort. This is an ...
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