Chromatography Online

LCGC Blog: Artificial Intelligence: The Good, The Challenging, and The Terrifying

In my Sex, Drugs, and Artificial Intelligence class, I have strived to take a balanced look at various topics, including ...

Ultra‑robust machine‑learning models run stable molecular simulations at extreme temperatures

Researchers at The University of Manchester have created a physics‑informed machine‑learning model that can run molecular ...

We analyzed Philly street scenes and identified signs of gentrification using machine learning trained on longtime residents’ observations

A lot of steps.” These are some of the descriptions that longtime residents of gentrifying neighborhoods in Philly used to describe the new construction popping up around them. Our team posited that ...

Turning CO₂ into methanol: Multilayer machine learning speeds up search for better catalysts

Finding high-performing catalysts, which are used to accelerate processes from chemical manufacturing to energy production, ...
Fabbaloo

PiGRAND Brings Physics Informed Graph Diffusion To AM

A new research model called PiGRAND merges physics guidance with graph neural diffusion to predict and control AM processes.
Chemical Engineering

How SABIC Uses Hybrid Modeling and Machine Learning to Drive Smarter Operations Decisions

View All Webinars > How SABIC Uses Hybrid Modeling and Machine Learning to Drive Smarter Operations Decisions FREE \| April ...
International Atomic Energy Agency

New IAEA Research Project Uses Machine Learning to Better Predict Polymer Changes under Radiation

IAEA launches a new research project on data-driven prediction of structural changes in polymers induced by radiation. The IAEA is inviting research organizations to join a new project that will use ...
IEEE

Machine Learning based Controller using Gaussian Process Regression in Electric Motors: Interpretability, Safety and Reliability

Abstract: In industrial applications, proportional-integral (PI) controllers are frequently employed for controlling permanent magnet synchronous motors (PMSMs) due to their fast response rate and ...
IEEE

Walking Trajectory Prediction Using Signal Processing & Machine Learning Based Models

Abstract: This study compares machine learning (LSTM, GRU) and signal processing (Particle Filter, Kalman Filter) approaches for walking trajectory prediction. We evaluated prediction accuracy, ...