Exploring ‘chemical space’ with Professor Anatole von Lilienfeld – U of T Engineering News

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Professor Anatole of Lilienfeld (Chemistry, MSE) sails through space, but instead of exploring the depths of the universe, her work is here on Earth in “chemical space.”

And instead of searching for stars, galaxies, and other unknown celestial objects, their focus is on the untapped potential of undiscovered chemical combinations. To do this job, he is not equipped with a powerful telescope; his preferred tool is artificial intelligence (AI).

Von Lilienfeld is the inauguration of the Clark Chair in Advanced Materials at the Vector Institute and the University of Toronto, and a core member of the U of T Acceleration Consortium (AC). Jointly appointed to the Department of Chemistry in the College of Arts and Sciences and the Department of Materials Science and Engineering at U of T Engineering, he is a leading expert in the use of computers to understand the vastness of chemical space.

Von Lilienfeld, who was recently appointed President of Canada CIFAR AI, was a speaker at AC’s first annual Accelerate conference last month at the U of T.

This four-day program focused on the power of autonomous laboratories (SDLs), an emerging technology that combines AI, automation, and advanced computing to accelerate the discovery of materials and molecules. The Accelerate conference brought together more than 200 people and featured talks and panels with more than 60 experts from academia, industry, and government who are shaping the emerging field of accelerated science.

Erin Warner, communications specialist for the Acceleration Consortium, recently spoke with von Lilienfeld about the conference and the digitization of chemistry.

How big is ‘chemical’ space?

We are surrounded by materials and molecules. Consider the chemical compounds that make up our clothes, the pavement we walk on, and the batteries in our electric cars. Now think about the possible new combinations that are waiting to be discovered, such as catalysts for the effective capture and utilization of atmospheric CO2, low-carbon cement, lightweight biodegradable compounds, membranes for water filtration, and powerful molecules for treating waste. cancer and bacteria. -resistant disease

In a practical sense, chemical space is infinite and searching for it is no small feat. A lower estimate says that it contains 1060 compounds, more than the number of atoms in our solar system.

Why do we need to accelerate the search for new materials?

Many of the most used materials no longer serve us. Most of the plastic waste generated in the world to date has not yet been recycled. But hopefully the materials that will power the future will be sustainable, circular, and affordable.

Conventional chemistry is a slow, often tedious series of trial and error that limits our ability to explore beyond a small subset of possibilities. However, AI can speed up the process by predicting which combinations might result in a material with the desired set of characteristics we’re looking for (eg, conductive, biodegradable, etc.).

This is just one step in autonomous labs, an emerging technology that combines artificial intelligence, automation, and advanced computing to reduce the time and cost of discovering and developing materials by up to 90%.

How can human chemists and AI work together effectively?

AI is a tool that humans can use to speed up and improve their own research. It can be considered as the fourth pillar of science. The pillars, which complement each other, include experimentation, theory, computer simulation and AI.

Experimentation is the base. We experiment with the goal of improving the physical world for humans. Next comes theory to give shape and direction to your experiments. But the theory has its limitations. Without computer simulation, the amount of computation needed to support scientific research would take much longer than a lifetime. But even computers have limitations.

With difficult equations comes the need for high-performance computing, which can be quite expensive. This is where AI comes in. AI is a less expensive alternative. It can help scientists predict both an experimental and a computational outcome. And the more theory we build into the AI ​​model, the better the prediction. AI can also be used to power a robotic lab, allowing the lab to run 24/7. Human chemicals will not be replaced; instead, they can spend tedious hours of trial and error to focus more on designing goals and other higher-level analyses.

Professor Anatole von Lilienfeld at the Accelerate Conference at the University of Toronto. (Photo: Clifton Li, Acceleration Consortium)

Are there limitations to AI, like the ones you described in the other pillars of science?

Yes, it is important to note that AI is not a silver bullet and has an associated cost that can be measured in data acquisition. You can’t use AI without data. And data acquisition requires experimenting and recording the result in a way that can be processed by computers. Like a human, the AI ​​then learns by reviewing the data and making an extrapolation or prediction.

Data acquisition is expensive, both financially and in terms of its carbon footprint. To address this, the goal is to improve the AI. If you can encode our understanding of physics into AI, it becomes more efficient and requires less data to learn, but provides the same predictive qualities. If less data is needed for training, the AI ​​model becomes smaller.

Instead of just using AI as a tool, the chemist can also interrogate it to see how well its data captures the theory, perhaps leading to the discovery of a new relative law for chemistry. While this interactive relationship is not that common, it may be on the horizon and could improve our theoretical understanding of the world.

How can we make AI for discovery more accessible?

The first way is open source research. In the emerging field of fast science, there are many advocates for open source access. Journals not only provide access to research papers but also, in many cases, to data, which is an important component in making the field more accessible. There are also repositories for models and code like GitHub. Providing access could lead to scientific breakthroughs that would ultimately benefit all of humanity.

A second way to expand AI for discovery is to include more students. We need to teach basic computer science and coding skills as part of a chemistry or materials science education. Schools around the world are beginning to update their curricula in this regard, but we still need more people to incorporate this essential training. The future of science is digital.

How do initiatives like the Acceleration Consortium and a conference like Accelerate help advance the field?

We are at the dawn of the true digitization of chemical sciences. Coordinated joint efforts, such as the Acceleration Consortium, will play a crucial role in synchronizing efforts not only at the technical level but also at the societal level, enabling the global implementation of an “updated” version of chemical engineering with unprecedented benefits for The humanity. usually.

The consortium also serves to connect academia and industry, two worlds that could benefit from a closer relationship. Visionaries in the commercial sector can dream of opportunities, and the consortium will be there to help put the science to work. The innovative nature of AI is that it can be applied to any sector. AI is on track to have an even bigger impact than the advent of computers.

Accelerate, the consortium’s first annual conference, was a great gathering event for the community and was a reminder that great things can come from a meeting of brilliant minds. While Zoom has done a lot for us during the pandemic, it can’t easily replicate the excitement and enthusiasm often cultivated at an in-person conference and needed to drive research and encourage a group to pursue a complex goal. .

What area of ​​’chemical space’ fascinates you the most?

Catalysts, which allow a certain chemical reaction to occur but remain unchanged in the process. A century ago, Haber and Bosch developed a catalytic process that would allow the transformation of nitrogen, the dominant substance in the air we breathe, into ammonia. Ammonia is a crucial starting material for chemical industries, but also for fertilizers. It made the mass production of fertilizer possible and saved millions of people from starvation. Large fractions of humanity would not exist right now were it not for this catalyst.

From a physics point of view, what defines and controls the activity and components of the catalyst are fascinating questions. They can also be instrumental in helping us address some of our most pressing challenges. If we were to find a catalyst that could use sunlight to convert nitrogen to ammonia quickly and efficiently, we could solve our energy problem by using ammonia as a fuel. You can think of the reactions that catalysts enable as ways to travel through chemical space and connect different states of matter.

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