Six popular machine learning models. (a) Decision tree; (b) feedforward neural network (Trans: transformation; Activ Func: activation functions); (c) convolution neural network (Conv: convolution; ...

Peer-reviewed research finds the company’s novel technology enables faster dataset construction, further shortening Avicenna’s timelines to develop life-saving medicines. “We’re accustomed to hearing ...

Machine learning has huge potential as a tool to investigate new materials and new applications of existing materials, as well as to streamline and focus future experimentation through rapid screening ...

The 2024 Nobel Prize in chemistry recognized Demis Hassabis, John Jumper and David Baker for using machine learning to tackle one of biology's biggest challenges: predicting the 3D shape of proteins ...

Artificial intelligence, building-block chemistry and a molecule-making machine teamed up to find the best general reaction conditions for synthesizing chemicals important to biomedical and materials ...

Imagine you’re a materials scientist and your job is to discover a new material, a combination of atoms no one has ever made. Maybe you’re looking for a metal-organic framework (MOF). They have a lot ...

In March, a paper in the Journal of the American Chemical Society sparked a heated Twitter debate on the value of machine learning for predicting optimal reaction pathways in synthetic chemistry. The ...

(Nanowerk News) Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of ...

Machine-learning tools have taken us closer to understanding electrons and how they behave in chemical interactions, following news that UK-based AI company DeepMind, owned by Google’s parent company ...

Researchers at Stony Brook University’s Materials Science and Chemical Engineering Department have been using computers that are capable of learning to recognize various steps in the complex movement ...