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Machine learning could yield faster, cheaper lithium-ion battery development

Researchers claim model can cut years from testing cycles Scientists have developed a machine learning method that could ...

Automating microfluidic chip design: Hybrid approach combines machine learning with fluid mechanics

Researchers led by Assoc. Prof. Dr. Savaş Taşoğlu from the Department of Mechanical Engineering at Koç University have ...

PSCI Examines Staffing And Consulting Approaches To AI And Automation

Wilmington, Delaware - February 03, 2026 - PRESSADVANTAGE - PSCI shared perspective on staffing and consulting ...
C&EN

Machine learning helps improve accuracy and efficiency of small-molecule calculations

When experiments are impractical, density functional theory (DFT) calculations can give researchers accurate approximations of chemical properties. The mathematical equations that underpin the ...
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 ...
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 ...
ascopubs.org

Electronic health records (EHR)-based machine learning (ML) approach to predict risk of progression to metastatic melanoma after initial diagnosis.

ctDNA versus 18F-FDG PET-CT in predicting long-term disease control in patients with advanced melanoma undergoing immune checkpoint blockade therapy. Delineating the role of the microbiome and tumor ...
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 ...
Electronic Design

A Neural-Network-Based Approach to Smarter DPD Engines

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.