The search for new materials: can AI find us alternatives for plastic?
Nov 18, 2022
Exploring ‘chemical space’ with U of T's Anatole von Lilienfeld.
An image from the Milky Way galaxy overlayed with molecules, such as ammonia, phosphine, carbon dioxide, or aspirin.
It is not hard to be drawn in by the beauty and unprecedented detail of the photographs being captured by NASA’s James Webb Space Telescope — images that also have the potential to enhance our understanding of the cosmos. When we can see our surroundings, we can better understand where we are going — or want to go — and how to best get there.
Anatole von Lilienfeld also navigates space, but rather than exploring the depths of the universe, his work is here on Earth in “chemical space.” His focus is on the untapped potential of undiscovered chemical combinations, and his powerful “telescope” is artificial intelligence (AI).
The Clark Chair in Advanced Materials: using computers to discover new molecules
Von Lilienfeld is the inaugural Clark Chair in Advanced Materials at the Vector Institute and the University of Toronto, and a pivotal member of U of T’s Acceleration Consortium (AC). Appointed jointly to the Department of Chemistry in the Faculty of Arts & Science and the Department of Materials Science & Engineering in the Faculty of Applied Science & Engineering, he is one of the world’s brightest visionaries on the use of computers to understand the vastness of chemical space.
He came to U of T thanks to the Clark Chair, a professorship made possible through a generous gift from long-time U of T donor Edmund Clark, with additional support from the Faculty of Arts & Science and the Faculty of Applied Science & Engineering.
Von Lilienfeld, who was recently named a Canada CIFAR AI Chair, was a speaker at the Acceleration Consortium’s first annual Accelerate conference last month at U of T.
Anatole von Lilienfeld is one of the world’s brightest visionaries on the use of computers to understand the vastness of chemical space.
This four-day program centred around the power of self-driving labs (SDLs), an emerging technology that combines AI, automation, and advanced computing to accelerate materials and molecular discovery. The Accelerate conference brought together over 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 at the Acceleration Consortium, recently spoke with von Lilienfeld about the conference and the digitization of chemistry.
Anatole von Lilienfeld is one of the world’s brightest visionaries on the use of computers to understand the vastness of chemical space.
How big is ‘chemical’ space?
We are surrounded by materials and molecules. Consider the chemical compounds that make up our clothing, the pavement we walk on, and the batteries in our electric cars. Now think about the new possible combinations that are out there waiting to be discovered, such as catalysts for effective atmospheric CO2 capture and utilization, low-carbon cement, lightweight biodegradable composites, membranes for water filtration, and potent molecules for treatment of cancer and bacterial-resistant disease.
In a practical sense, chemical space is infinite and searching it is no small feat. A lower estimate says 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 widely used materials no longer serve us. Most of the world’s plastic waste generated to date has not yet been recycled. But the materials that will power the future will hopefully be sustainable, circular and inexpensive.
Most of the world’s plastic waste generated to date has not yet been recycled. But the materials that will power the future will hopefully be sustainable, circular and inexpensive.
Conventional chemistry is slow, a series of often tedious trial and error that limits our ability to explore beyond a small subset of possibilities. However, AI can accelerate the process by predicting which combinations might result in a material with the set of desired characteristics we are looking for (e.g., conductive, biodegradable, etc.).
This is but one step in self-driving laboratories, an emerging technology that combines AI, automation, and advanced computing to reduce the time and cost of discovering and developing materials by up to 90 per cent.
Most of the world’s plastic waste generated to date has not yet been recycled. But the materials that will power the future will hopefully be sustainable, circular and inexpensive.
How can human chemists and AI work together effectively?
AI is a tool that humans can use to accelerate and improve their own research. It can be thought of as the fourth pillar of science. The pillars, which build on each other, include experimentation, theory, computer simulation and AI.
Experimentation is the foundation. We experiment with the aim of improving the physical world for humans. Then comes theory to give your experiments shape and direction. But theory has its limitations. Without computer simulation, the amount of computation needed to support scientific research would take far longer than a lifetime. But even computers have constraints.
With difficult equations come the need for high-performance computing, which can be quite costly. This is where AI comes in. AI is a less costly alternative. It can help scientists predict both an experimental and 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 the ability to run 24/7. Human chemists will not be replaced; instead, they can hand off tedious hours of trial and error to focus more on designing the objectives and other higher-level analysis.
How do initiatives like Acceleration Consortium help advance the field?
We are at the dawn of truly digitizing the chemical sciences. Co-ordinated, joint efforts, such as the Acceleration Consortium, will play a crucial role in synchronizing efforts not only at the technical but also at the societal level, thereby enabling the worldwide implementation of an ‘updated’ version of chemical engineering with unprecedented advantages for humanity at large.
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 up opportunities, and the consortium will be there to help make the science work. The groundbreaking nature of AI is that it can be applied to any sector. AI is on a trajectory to have an even greater impact than the advent of computers.
What area of ‘chemical space’ fascinates you the most?
Catalysts, which enable 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.
Catalysts made the mass production of fertilizers possible and saved millions of people from starvation.
Ammonia is a crucial starting material for chemical industries, but also for fertilizers. It made the mass production of fertilizers possible and saved millions of people from starvation. Major fractions of humanity would not exist right now if it were not for this catalyst.
Catalysts made the mass production of fertilizers possible and saved millions of people from starvation.
From a physics point of view, what defines and controls catalyst activity and components are fascinating questions. They might also be critical for helping us address some of our most pressing challenges. If we were to find a catalyst that could use sunlight to turn nitrogen rapidly and efficiently into ammonia, we might be able to solve our energy problem by using ammonia for fuel. You can think of the reactions that catalysts enable as ways of travelling through chemical space and to connect different states of matter.
