Identifying optimal catalyst materials for specific reactions is crucial to advance energy storage technologies and sustainable chemical processes. To screen catalysts, scientists must understand ...

A team led by Guoyin Yin at Wuhan University and the Shanghai Artificial Intelligence Laboratory recently proposed a modular machine learning ...

Harvard researchers bring the accuracy, sample efficiency, and robustness of deep equivariant neural networks to the simulate 44 million atoms. This is achieved through a combination of innovative ...

In a recent study published in Scientific Reports, researchers developed a machine learning-based heart disease prediction model (ML-HDPM) that uses various combinations of information and numerous ...

NTT Research and NTT R&D scientists presented 12 papers at ICML 2025, one of the world’s most prestigious conferences on AI and machine learning. Three papers co-authored by NTT Research Physics of AI ...

AI methods are increasingly being used to improve grid reliability. Physics-informed neural networks are highlighted as a ...