A fast and accurate surrogate model screens over 10,000 possible metal-oxide supports for a platinum nanocatalyst to prevent sintering under high temperatures. Metal nanoparticles catalyze reactions ...

A physics informed machine learning model predicts thermal conductivity from infrared images in milliseconds, enabling fast, ...

Artificial intelligence (AI) is a two-edged sword. While AI and machine learning (ML) models are powerful, they can be known to make egregious mistakes. For example, the integration of AI and ...

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...

Influence of MRIs performed in a 6-week interval on the histopathological detection of prostate cancer with different PIRADS classifications: A real-world data analysis.

This guest essay reflects the views of Nirali Somia, a graduate student at Cold Spring Harbor Laboratory. It is part of a series of essays from current researchers at the Cold Spring Harbor Laboratory ...

The calculation and/or the measurement of the thermal conductivity of materials is a fundamental challenge in materials science, essential for developing technologies in energy management, electronics ...

This new article publication from Acta Pharmaceutica Sinica B, discusses establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features.

William Chiu (MSiA '13) works in the fast-paced world of finance, where algorithms often make decisions that impact millions of lives and even more dollars. Now he's helping MLDS students develop ...

Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.