A Prize-Winning User

Erin Johnson wouldn’t have won the prestigious Steacie Prize, awarded to early-career scientists, in 2021, without the Digital Research Alliance of Canada’s high-performance computing and the services of ACENET.

“That’s a completely fair statement,” says Johnson, who was an early adopter of high-performance computing, dating back to her student days at Carleton University, Queen’s University and Dalhousie University, as well as her post-doctoral work at Duke University. She’s now a professor and Herzberg-Becke chair in theoretical chemistry at Dalhousie University.

Asked how she would explain her job to a guest at a cocktail party, Johnson says she studies intermolecular interactions within the materials all around us.

“These are the weaker interactions between molecules as opposed to the stronger bonds within a molecule,” Johnson explains. “So, we would, for example, at a cocktail party look at the interactions in beer or wine in a glass — those types of interactions within a liquid or a gas or within a molecular crystal.”

She does this to predict such properties as reactivity, hardness, and conductivity, among others.

“Any chemical observable is something that we can predict through this type of modelling,” she says.

Johnson’s lab, however, might surprise those who envision her working in a chemistry lab full of beakers and bunsen burners. Rather, it’s a computer lab from which she’s developed methods for modeling that are some of the most accurate and efficient available. It was those methods that won her the Steacie Prize.

“We then take those methods and apply them to problems in chemistry,” she explains. “One of the particular problems we're focusing on is the problem of molecular crystal structure prediction or how molecules would come together to form a 3D solid.”

She likened the ways molecules come together to the ways in which Lego bricks do.

“There are many ways you could conceive of molecules coming and packing together, but not all of those are going to be stable,” she says. “So the challenge is trying to predict how they will actually pack in a solid.”

Once you solve that puzzle, there are applications across several industries, one of the most recognizable being pharmaceuticals.

“If you are producing a drug and you want it in pill form, you want a solid but soluble state,” Johnson says. “When you try to formulate new drugs, you want to screen for all the possible polymorphs (transformations to another form) and find out which ones are the most stable. What you don't want is for it to easily form a particular polymorph that's not a very stable one and then change over time so that it’s no longer soluble.”

These methods also have applications for electronics, but instead of looking at solubility, the property of interest would be conductivity.

“Like the solubility, the ability of charge to flow through a material is also dependent on the particular polymorph” Johnson explains. “You can think of many applications where the different solid state properties between polymorphs would affect whether a material was promising or not.”

Johnson says her work would be “completely impossible” without ACENET’s services.