From guesswork to guidance: How machine learning speeds dopant design for water-splitting photocatalysts

MLIP calculations successfully identify suitable dopants for a novel photocatalytic material, report researchers from the ...

Mapping 3D-super-enhancers with machine learning to pinpoint regulators of cell identity

Scientists usually study the molecular machinery that controls gene expression from the perspective of a linear, ...

Machine learning models identify key predictors of driving under the influence of alcohol or cannabis

The frequency of substance use, early age of initiation, and cannabis-related memory impairments are among the primary ...
BMJ

Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...
Mirage News

AI Model Poised to Transform Flood Forecasting

The research was supported by the National Science Foundation, State of Minnesota Weather Ready Extension, and Minnesota Pollution Control Agency. The research was done in collaboration with the ...
Science Daily

Exploring factors affecting workers' acquisition of exercise habits using machine learning approaches

Researchers analyzed data from middle-aged workers who had received Specific Health Guidance -- a revolutionary system implemented by the Japanese Ministry of Health, Labor, and Welfare to improve ...
Morningstar

Machine Learning Can Forecast Which Stock Factors Will Outperform

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
Chromatography Online

AI/ML in Practice: Boudewijn Hollebrands on Deep Learning Models to Predict Food Peptide Retention Times

LCGC International’s interview series on the evolving role of artificial intelligence (AI)/machine learning (ML) in separation science continues with Boudewijn Hollebrands from Unilever Foods R&D, ...
mccormick.northwestern.edu

New Machine Learning Framework Enables Data-Efficient Design of Advanced Metamaterials

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
Semiconductor Engineering

Machine Learning-Based IR Drop Prediction Approach

A new technical paper titled “Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate” was published by researchers at KTH Royal Institute of Technology and ...