In my career as a chemist, I owe a huge debt to serendipity. In 2012, I was in the right place (IBM’s Almaden research lab in California) at the right time—and I did the “wrong” thing. I was supposed to be mixing three components in a beaker in the hope of systematically uncovering a combination of chemicals, meaning to replace one of the chemicals with a version that was derived from plastic waste, in an effort to increase the sustainability of thermoset polymers.
Instead, when I mixed two of the reagents together, a hard, white plastic substance formed in the beaker. It was so tough I had to smash the beaker to get it out. Furthermore, when it sat in dilute acid overnight, it reverted to its starting materials. Without meaning to, I had discovered a whole new family of recyclable thermoset polymers. Had I considered it a failed experiment, and not followed up, we would have never known what we had made. It was scientific serendipity at its best, in the noble tradition of Roy Plunkett, who invented Teflon by accident while working on the chemistry of coolant gases.
Today, I have a new goal: to reduce the need for serendipity in chemical discovery. Nature is posing some real challenges in the world, from the ongoing climate crisis to the wake-up call of COVID-19. These challenges are simply too big to rely on serendipity. Nature is complex and powerful, and we need to be able to accurately model it if we want to make the necessary scientific advances.
Specifically, we need to be able to understand the energetics of chemical reactions with a high level of confidence if we want to push the field of chemistry forward. This is not a new insight, but it is one that highlights a major constraint: accurately predicting the behavior of even simple molecules is beyond the capabilities of even the most powerful computers.
This is where quantum computing offers the possibility of major advances in the coming years. Modeling energetic reactions on classical computers requires approximations, since they can’t model the quantum behavior of electrons over a certain system size. Each approximation reduces the value of the model and increases the amount of lab work that chemists have to do to validate and guide the model. Quantum computing, however, is now at the point where it can begin to model the energetics and properties of small molecules such as lithium hydride, LiH—offering the possibility of models that will provide clearer pathways to discovery than we have now.
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