To better understand the motions of electrons, the Wisconsin team worked with physicists at the Deutsches Elektronen-Synchrotron who performed theoretical simulations of the protein’s reaction to light. The electrons and atoms within the protein have to move according to the laws of quantum mechanics, which act as something like a rulebook. Comparing their results to a simulation based on those rules helped the team understand which of the allowed moves the protein was performing. This brought them closer to understanding why they saw the motions they did.
The union of quantum theory and AI encapsulated in the new work holds promise for future research into light-sensitive molecules, says Fromme. She emphasizes that a machine learning approach can extract lots of detailed information from seemingly limited experimental data, which may mean that future experiments could consist of fewer long days doing the same thing over and over in the lab. Mukamel agrees: “This is a most welcome development that offers a new path for the analysis of ultrafast diffraction measurements.”
Coauthor Robin Santra, a physicist at the Deutsches Elektronen-Synchrotron and the University of Hamburg, believes that the team’s novel approach could change scientists’ thinking about incorporating data analysis into their work. “The combination of modern experimental techniques with ideas from theoretical physics and mathematics is a promising route towards further progress. Sometimes, this may require scientists to leave their comfort zone,” he says.
But some chemists would like to see the new approach examined in even more detail. Massimo Olivucci, a chemist at Bowling Green State University, points out that PYP’s response to light includes something like a singularity in its energy spectrum—a point where the mathematical equations for calculating the protein’s energy “break.” This kind of occurrence is as important to a quantum chemist as a black hole is to an astrophysicist, because it is another instance in which the laws of physics, as we understand them today, fail to tell us exactly what is happening.
According to Olivucci, many fundamental processes in chemistry and molecular physics involve these “rule-breaking” features. So understanding the minute details of what a molecule is doing when laws of physics can’t offer clarity is really important to scientists. Olivucci hopes that future work with the machine learning algorithm from the new study will compare its “movies” to theoretical simulations that contain atomistic detail—rulebooks specifying what every single atom in the protein can and cannot do. This could help chemists determine the fundamental reasons why some of the smallest parts of PYP perform some of its fastest moves.
Ourmazd also notes that his team’s approach could help uncover even more about PYP’s response to light. He would like to use the algorithm to observe what happens slightly before the protein absorbs light, before it “knows” that it is about to start contorting, rather than immediately after the absorption, when it is locked into the motion. Additionally, he notes, instead of using flashes of x-rays, scientists could throw ultrafast electrons at the protein, then record their bouncing off to produce even more fine-grained snapshots that the AI could analyze to achieve an even more detailed animation of the process.
Ourmazd would also like to tackle astrophysics and astronomy next, two fields in which scientists have long been taking images of a changing universe, and from which an AI might extract useful data—although he doesn’t have a specific experiment in mind yet. “The world’s our oyster, to some extent,” he says. “The question is: What are the most important questions to ask and realistically expect to answer?”
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