By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...
Six popular machine learning models. (a) Decision tree; (b) feedforward neural network (Trans: transformation; Activ Func: activation functions); (c) convolution neural network (Conv: convolution; ...
(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-based neural network potentials often cannot describe long-range interactions. Here the authors present an approach for building neural network potentials that can describe the ...
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 ...
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 ...
A new machine learning tool can calculate the energy required to make -- or break -- simple molecules with higher accuracy than conventional methods. Extensions to more complicated molecules may help ...
The Schrödinger equation rewrote the rules of matter and forever changed the field of chemistry. Donald Truhlar, a chemist at the University of Minnesota, calls it the “greatest advance of the 20th ...
As compared to organic chemistry, which is a study dedicated to carbon-containing compounds, the area of inorganic chemistry examines the properties and behaviors of all other compounds including ...
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