Machine-learning interatomic potentials (MLIPs) trained by directly learning the total interatomic interaction energies can suffer from limited transferability,

Large Language Models like ChatGPT, Claude, and Gemini have ruined some parts of university education, but they have also improved my teachi...

The part of university lecturing that I find most interesting is to figure out how to explain something. What background material is needed...

I taught 2nd year physical chemistry for the first time this year. The course includes an instructor-led weekly tutorial in addition to thre...

Theoretical chemist’s insights are now embedded into computer software used to predict the behaviour of molecules from pharmaceuticals to construction materials

Many machine learning potentials (MLPs) rely on representations of the total energy in terms of the positions of the atoms in their local environment, using either a cutoff radius or a limited number ...

Welcome to the web page of the Rowley Group at Carleton University in Ottawa, Canada. Our group uses computer simulations […]

Until this year, my computational chemistry courses only covered electronic structure and molecular simulation. Machine learning is now used...

Before we look to take advantage of fleeing American talent, let’s nurture the Canadian talent we have here by increasing R&D spending

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and adva...

Neural network potentials (NNPs) are an innovative approach for calculating the potential energy and forces of a chemical system. In principle, these methods are capable of modeling large systems with...

The Physical, Theoretical and Computational Chemistry (PTC) subject division is hosting a series of online seminars in the field of physical, theoretical, and computational chemistry. Following in the...