Nanowerk

Machine learning enables smarter, faster, cleaner catalyst design for sustainable energy

(Nanowerk Spotlight) The integration of machine learning (ML) into materials science is rapidly transforming the way researchers discover and design advanced ...
EurekAlert!

Using AI to improve nickel catalysts for converting carbon dioxide into methane

The conversion of carbon dioxide into clean fuels is regarded as an important route toward carbon neutrality. CO 2 methanation, in particular, has drawn increasing interest due to its favorable ...
EurekAlert!

Reinforcement learning world models for catalyst surface reconstruction: state-of-the-art review

This work presents an AI-based world model framework that simulates atomic-level reconstructions in catalyst surfaces under dynamic conditions. Focusing on AgPd nanoalloys, it leverages Dreamer-style ...
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 Tools Help Bridge Design-To-Manufacturing Gap

More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...