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 ...

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 ...

A machine-learning tool can easily spot when chemistry papers are written using the chatbot ChatGPT, according to a scientific study. The study in Cell Reports Physical Science said that a specialised ...

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 ...

If nonliving materials can produce rich, organized mixtures of organic molecules, then the traditional signs we use to ...

UPTON, NY--Chemistry is a complex dance of atoms. Subtle shifts in position and shuffles of electrons break and remake chemical bonds as participants change partners. Catalysts are like molecular ...

Computational chemistry lets chemists predict molecules’ properties without measuring them in the lab. Some of the most accurate computational chemistry tools use quantum chemistry, but these ...

Say you’re a pharmaceutical company. You’ve figured out that a novel molecule could be effective in treating an illness — but that molecule only exists in a simulation. How do you actually make it, ...

Researchers at the University of Toronto's Faculty of Applied Science & Engineering have used machine learning to design nano ...

Achieving autonomous multi-step synthesis of novel molecular structures in chemical discovery processes is a goal shared by many researchers. In this Comment, we discuss key considerations of what an ...