Spring, 2025
Stijn De Baerdemacker says his research in theoretical chemistry is “fairly fundamental” and yet it doesn’t find itself very far away from real-world applications.
“Typically, when we think about chemistry, it's about tubes and beakers and people in lab coats, and that’s still a big portion of what of what we do,” De Baerdemacker says. “However, it's not only running the experiments, it's also exploring what molecules you can make and how they can solve problems.”
In other words, he’s always looking to develop something new — a better material for a device, or a better drug for a disease, for example. And if you’re searching for the “holy grail” that will solve your problem, it’s very time-consuming, he says, because you have to assess all of the options.
“This is where computations come in, because on a computer, we are not bound by safety requirements,” he says. “We can just go in and try to simulate what will happen. That speeds up the discovery process by many orders of magnitude.”
De Baerdemacker applies various mathematical models that describe the structure of molecules to determine which ones would provide the most accurate results.
“We make sure the methods are grounded in proper theory,” he says. “Once we have the theory down, we run tests and we use ACENET’s resources to put everything into code. We test it on our own local systems and then try them on a bigger system like ACENET,” he says. They then send the code to the chemist, who runs computer simulations using various molecules. This enables the chemist to narrow the options to the most promising candidates before running experiments.
De Baerdemacker says that when he talks to people about high-performance computing and asks them who they think might be HPC’s biggest clients, they’re surprised to learn that chemists are among them. And, he adds, his colleagues in computational chemistry are definitely more frequent users, but even fundamental chemists make good use of services at ACENET.
“We also run a lot of simulations,” he says.
Using a concrete example, he says a lot of diseases are associated with how the proteins in our bodies work and the way they are folded in the cell is also are important for their functionality. Alzheimer’s is one example where a protein starts to curl up and then pierces through a cell membrane,” he says.
“We're actively looking for a drug that will inhibit that behaviour, but in order to do that, we need to come up with candidates and do a lot of simulations,” he says. “When we’re doing those simulations, we use ACENET.”
But where he has used ACENET to the fullest extent is with machine learning, which he says chemists have been using for 20 years.
“When we’re working on computations, it’s not one variable, it’s millions,” he says. “On machine learning, we’ve discovered that the machine has figured out the kind of molecule we were looking at all by itself. It looked at the data and it found patterns. At this point, ACENET is really crucial.”
