Computational Chemistry

A Prize-Winning User

“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.

Quantum Chemistry of Molecular Evolution

Ph.D. candidate Lázaro A. M. Castanedo and his adviser Chérif F. Matta are trying to answer one of the fundamental questions of molecular evolution: Why did nature pick the particular DNA structure humans have now as a carrier of our genetic information instead of other equally plausible choices?

“In the absence of constraints, nature’s chemical reactions always choose the path that is the least costly in terms of Gibbs (free) energy,” explains Matta, who is a professor at the Department of Chemistry and Physics at Mount Saint Vincent University. Gibbs (free) energy balances the tendency of nature to increase randomness as time progresses and its tendency to seek the lowest possible energy. “Could nature have made less costly choices than those that led to today’s DNA? If so, why didn’t it?”

Describing the project, Castanedo, who is pursuing his PhD at Saint Mary’s University in collaboration with Mount Saint Vincent, says he compares the energies of different combinations of building blocks of nucleic acids (that is to say DNA and RNA). In addition, chemistry textbooks provide ample descriptions of the structural and chemical differences between DNA and RNA, but Castanedo and Matta want to know why that is, and if the observed forms are somehow energetically advantageous.

As an extension of this work, they are also investigating whether some of the nucleotides nature hasn’t chosen have applications in drug development.

“One of the applications of discovering new nucleotides is that they can be used to produce similar molecular structures for drug discovery,” says Castanedo, who received the 2020 Abe Leventhal Research Bursary from the Alzheimer Society of Nova Scotia for his work in developing molecules to prevent and detect dementias. “They could be used to develop new treatments for Alzheimer’s, cancer, and other diseases.”

Castanedo says he could do this research in a lab by synthesizing and comparing the components, which would take decades worth of human labour, or, he could approach it theoretically using computers, taking a fraction of the time.

“We predict what happens in the ‘real world’ using the computational infrastructure of the Digital Research Alliance of Canada and ACENET, which has thousands of cores and a panoply of state-of-the-art computational quantum chemistry software,” he says. “So, you can actually create a model of this molecule on the computer and obtain, among many other properties, its Gibbs energy.”

Castanedo is one of Saint Mary’s heavier users — with 235,000 CPU hours in 2022 alone (about 27 years of CPU time) — because he’s been analyzing a total of 2,530 molecules for this project.

“For each of these molecules, a CPU needs to run continuously anywhere from, say, 20 to 200 CPU hours. These calculations are computationally intense, as we use high levels of quantum chemical theory, such as density functional theory (DFT), to obtain accurate predictions,” he says.

“They generate a lot of data,” Matta says, “so you need to know what you are looking for — a needle in the haystack so to speak.”

While preliminary data has already been made publicly available, much of this work is yet to be published.

Simulating Solutions: The Power of Computational Chemistry

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.”

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.

Designing New Molecules

Jason Masuda has something in common with the alchemists of old. But instead of trying to turn lead into gold, he’s building substances that have never been seen on Earth before. Masuda is a chemistry professor at Saint Mary’s University who is building new molecules from atoms. He says the idea is to use new combinations of atoms to make an actual physical compound with unique chemical properties. “In organic chemistry there are a number of functional groups – specific groups of atoms within molecules that have very characteristic properties, such as aldehydes, ketones, and alcohols – but what we’re doing is making functional groups that have not been made before. Organic chemists understand how the normal functional groups react. We make a new arrangement of atoms and then we study them and how they react with other molecules. That gives us insights into their reactivity. That’s what chemistry is about. Reactions.” The majority of Masuda’s work is done in the lab using glove boxes in an inert environment because many of the molecules he creates are reactive to water and air. “The compounds that we make are not transient things,” he says. “They are actually something you can physically store in a lab.” Once a new molecule has been created, Masuda studies it and catalogs its properties in a library of molecules. “We purify these molecules, we crystallize them and then we analyze them using a variety of techniques. One of the key methods is by using an Xray defractometer on single crystals. That lets us see the places of the atoms in three dimensions.” Masuda also uses ACENET to conduct electronic structure calculations using software called Gaussian09 – a program that places groups of atoms into a structure that is the most stable configuration.“We use that alongside our experimental work to predict the reactivity of these molecules,” he says. Masuda will often use Gaussian to see ahead of time if a molecule will be stable enough to exist in nature. “Gaussian09 is a great tool to save us from wasting time. Because we’re pushing the boundaries of what nature allows, quite often I will generate a molecule and get it to optimize on ACENET and then, if it looks promising, we’ll make it in the lab. There’s a saying that two hours in the library saves you two months in the lab. It’s the same with ACENET. Sometimes a couple of hours with Gaussian09 will save you weeks or months in the lab.” While Masuda creates molecules in the name of pure science, he says giving scientists such as cancer researchers a palette of molecules to work with will ultimately have many practical applications as well. “Basic science leads to new discoveries,” he says. “Often we have no idea what those discoveries will be ahead of time.”

Oil Sands Upgrading Methodogies

The Alberta oil sands are the world’s third largest proven source of oil in the world and comprise 140,200 square kilometres. The majority of oil extracted there involves pumping high bitumin concentrated underground deposits to the surface, where the bitumin is then extracted from other components. Once this is done, it is upgraded to Synthetic Crude Oil, which is then refined for products we use daily. However, the extraction process is dirty, containing harmful chemicals such as sulphur and nitrogen. Upgrading and refining the oil for fuel and heat consumption means removing those dirty components. At the moment, the process of upgrading oil from Alberta’s oil sands is neither as efficient, nor as environmentally friendly as it should be. This has led to domestic and international criticism. For the past year and a half, Dr. Kai Ylijoki at Saint Mary’s University has been working with a team at the Institute for Oil Sands Innovation (IOSI) at the University of Alberta. IOSI’s vision is to have “Oil sands operations with a reduced environmental footprint by minimizing water use, consuming less energy, lowering greenhouse gas and other emissions, yielding high quality products at lower cost.” (http://www.iosi.ualberta.ca) Key to achieving this vision is understanding the complexes in the oil – what they are, their molecular structure, and the mechanism by which they work in upgrading. Dr. Ylijoki is, among other things, a computational chemist and his work involves studying these complexes with a view to identifying better catalysts. He does this by studying their properties to better understand their behaviour. The large size of the molecules renders them impossible to study on standard desktop computers, so he uses ACENET and Compute Canada’s advanced computing resources. Once he understands the complexes, then it’s a matter of finding very active catalysts for bond activation that don’t require large quantities, do the job more efficiently and are more environmentally friendly. IOSI has had success in this area – breaking certain complexes selectively – and finding ways to quicken the catalyst. Through the computational work of Dr. Ylijoki and his two students, the IOSI team is excited to be seeing some other unique aspects to the complexes that could be applied to other industries. Finding more efficient, environmentally better ways of cleaning oil from Alberta’s oil sands will help Canada economically, environmentally, and internationally. Dr. Ylijoki has received additional computing resources for 2015 through Compute Canada’s national Resource Allocation Competition.

Pushing the Envelope (or Molecule) with Quantum Chemistry

Dr. Jason Pearson’s research team of seven at the University of Prince Edward Island strives to understand the properties of molecules at the electronic level; why they behave the way they do, why some interactions are strong while others are not, and ultimately predict how molecules will behave when interacting with other molecules. It’s a broad scope. They do this by developing and applying computational algorithms. For example, an experimental researcher might have samples from which they are searching for new compounds. Along the way, they might find something with interesting properties, but need to identify the chemical substance. This is where Dr. Pearson’s group can take the experimental data, build every possibility into models and then run simulations with the data that ultimately allows them to understand the molecule. Researchers then can examine the molecule through a new lens so to speak. But it doesn’t stop there. Along the way, Pearson is developing new computational tools and new simulation techniques useful for other researchers. Already a resource to colleagues at the University, Pearson’s long-term focus is to utilize and develop computational technology for the purpose of designing new molecules and materials. His group employs a two-pronged approach whereby state-of-the-art simulation techniques are applied to probe the molecular level of detail in chemical systems, allowing for accurate quantifications of structure, interactions, and mechanisms of action. Simultaneously, they design the next generation of computational algorithms and methodologies for the investigation of electronic structure, specifically focused on the concept of the electron pair. The methods developed in Pearson’s lab can be applied to almost all matter, and therefore can be used in many fields, such as designing new materials, new industrially relevant catalysts and nanotechnologies to name a few. Pearson hopes to further push the bounds of what’s possible in computational chemistry by gathering large data sets in the chemical sciences, creating a database for others to access and utilizing the data to develop new simulation techniques that are faster and more accurate. For example, this approach could enable very accurate simulations on large systems like biomolecules and other polymers. His goal is to develop algorithms that behave like a robot, travelling through the data sets, collecting data and solving problems. The chemistry research that is enabled by Dr. Pearson’s group is addressing a broad scope of the world’s most important technological and societal challenges, such as climate change, energy, security, food supply and health.

New Tools For Designing Safer Chemical Catalysts

Dr. Ghislain Deslongchamps leads a small group of researchers at the University of New Brunswick, adapting computational chemistry tools normally used in drug design for discovering new catalysts with a wide range of applications – and not just any kind of catalyst. A catalyst is a small molecule that accelerates a particular chemical reaction. One type of extremely valuable catalyst is one that promotes a chemical reaction to generate a molecule as a single stereoisomer. Stereoisomers are molecules that are identical except that they mirror each other’s structure. The analogy used by Deslongchamps is a pair of gloves. The two gloves are exactly the same except that the left glove is a mirror image of the right; each glove can only fit its respective hand. The same applies to drug molecules and how they interact with the body, so they typically need to be produced in the correct “handedness” in order to be safe and effective. For example, thalidomide was a drug sold in the late 1950’s to treat morning sickness in pregnant women. It was manufactured as a 50:50 mixture of its two stereoisomers; one had the desired medicinal properties whereas the other was later found to cause severe birth defects. Thus, the ability to manufacture drugs as single stereoisomers has become a critically important issue for the modern pharmaceutical industry. It’s more difficult to make a molecule as a single stereoisomer, however, Deslongchamps is developing computer-based tools and methods for doing just that – developing asymmetric organocatalysts (purely metal-free organic catalysts) that can produce a molecule of “single handedness”. His is the only research group in the world retraining computer-based drug design tools for the purpose of organocatalyst discovery. Because organocatalysts are metal-free, they may produce less toxic and more environmentally sound drugs, important components of green chemistry. While the catalysts he is designing are of great interest to the pharmaceutical industry, Deslongchamps’s computational tools can also be applied to many other areas where molecules with very specific chemical shapes, features and properties are required. The tools he’s creating — one called “reverse-docking” and the other called “virtual screening” – are inspired by those used in computer-assisted drug design. The computations carried out in his lab can be extremely time onerous and require very advanced computing resources. “We are extremely fortunate to be part of ACENET and having access to the Compute Canada resources to do the research that we do,” Deslongchamps says. “A lot of the work we do might otherwise take months of calculating as opposed to days. It’s almost impractical to do this research on a single computer.” Deslongchamps has a long-standing collaborative relationship with Chemical Computing Group Inc. (CCG), a leading drug software company based in Montreal. CCG is one of only a handful of such companies in the world and Dr. Deslongchamps is utilizing and adapting their software for designing and discovering new catalysts.

Unlocking the Secrets of a Vital Plant Hormone

A major high performance computing event may seem like an unusual place to showcase landmark research about plants. But that’s exactly what happened at this year’s High Performance Computing Symposium in Halifax. The research, carried out by a team at Saint Mary’s University in Halifax and the University of Jyväskylä in Finland, has unlocked the secrets of ethylene formation using a rather unconventional tool. The team used the ACENET Data Cave located at SMU to create a virtual 3-D model of the molecules involved in the process; a model that allowed them to visualize the process more accurately. Ethylene is a key hormone in plants – a chemical compound that plays a critical role in causing fruit to ripen. But until recently, the chemical process that created ethylene in plants remained a mystery. For one thing, the biosynthesis process that creates ethylene gives off carbon dioxide along with cyanide, a deadly poison that should kill the enzyme that serves as a catalyst in the ethylene creation process. Dr. Jason Clyburne, a chemistry professor and Canada Research Chair in Environmental Science and Materials at Saint Mary’s University, headed up the research study. It was the first time he has used the ACENET Data Cave as part of his research. “Understanding the reaction mechanisms catalyzed by enzymes is a very difficult process, usually requiring a host of chemical, spectroscopic and other techniques,” says Clyburne. “By creating a visual model in the Data Cave, we were able to examine the active site and its environment. It was an extremely useful tool for us.” A number of developments have come from Clyburne’s landmark research. For one thing, the study confirmed the existence of a molecule called cyanoformate, a fragile and elusive ion long speculated to exist, that neutralizes the deadly properties of cyanide. Cyanoformate is formed when molecules of carbon dioxide and cyanide combine. It breaks down quickly, but before it does it carries the cyanide away from the enzyme before it can cause damage to the plant cell. “We learned how nature handles cyanide, a process that we didn’t really understand before,” says Clyburne. The research has some potential practical applications as well, including a better understanding of how fruit ripens – knowledge that could have major benefits to the global agricultural industry. The cyanoformate process also opens up possibilities for a new carbon capture process that could be used to help combat greenhouse gas emissions. Because cyanoformate forms an extremely weak bond between the carbon dioxide and cyanide, the process is attractive to carbon capture technologies that need to be able to easily transport and then release the carbon dioxide so that it can be recycled cheaply and easily. Because crystal structures are freely available on the Internet in a variety of file formats and can be projected in 3-D using the tools available in the ACEnet Data Cave, Clyburne predicts that this form of research will probably become more commonplace in years to come. “The Data Cave really opened my eyes up to the possibilities of seeing inside a material to explore its properties.