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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 laboratory in California) at the right time—and I did the “wrong” thing. I was supposed to be mixing three ingredients in a beaker in the hope of creating a known material. The goal was to replace one of the usual ingredients with a version derived from plastic waste, in an effort to increase the sustainability of strong plastics called thermoset polymers.
Instead, when I mixed two of the ingredients 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 precursor 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 fortuity at its best, in the noble tradition of Roy Plunkett, who accidentally invented Teflon while working on the chemistry of coolant gases.
Today I have a new goal: to reduce the need for serendipity in chemical discovery. Challenges such as the climate crisis and COVID-19 are so big that our responses can’t depend on luck alone. Nature is complex and powerful, and we need to be able to model it precisely if we want to make the scientific advances we need. Specifically, if we want to push the field of chemistry forward, we need to be able to understand the energetics of chemical reactions with a high level of confidence. This is not a new insight, but it highlights a major constraint: predicting the behavior of even simple molecules with total accuracy is beyond the capabilities of the most powerful computers. This is where quantum computing offers the possibility of significant advances in the coming years.
Modeling chemical reactions on classical computers requires approximations because they can’t perfectly calculate the quantum behavior of more than just a couple of electrons—the computations are too large and time-consuming. 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, works differently. Each quantum bit, or qubit, can map onto a specific electron’s spin orbitals; quantum computers can take advantage of quantum phenomena such as entanglement to describe electron-electron interactions without approximations. Quantum computers are now at the point where they can begin to model the energetics and properties of small molecules such as lithium hydride —offering the possibility of models that will provide clearer pathways to discovery than we have now.

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