Scientists use machine learning to develop an opener for a molecular can

“Foremost, it may enable experimental realizations of theoretical models that were otherwise limited by an inability to finely tune interaction energies.”

Doing this on a computer by brute force—calculating for all possible parameters, particle by particle, until the desired outcome was reached—would take far too much computational power and time. So the group turned to machine learning to minimize a loss function that represented the tension between the disassembly and the remaining substructure integrity.

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The Morse potential is simple and has three free parameters that can (and must) be selected for the desired situation. Removing the caged particle requires removing one of the shell particles.

They noted their methodology can be broadly applied. “Since we optimize directly with respect to the numerically integrated dynamics, our method is general enough to study a wide range of systems,” they wrote.

Machine learning was used to optimize the design of the shell’s “opener” molecule, which they call the “spider” due to its geometry. As they wrote, “disassembly is central to the dynamic functions of living systems, such as defect repair, self-replication, and catalysis.”

For their analysis, the team assumed the object removing the shell particle was a rigid pyramid-type structure that would fit on top of the 12-sphere cluster. They called this object a “spider.” It consisted of a pentagon-shaped ring of particles that formed the base of the pyramid, with a single “head particle” on top of the pyramid assembly.

When it was optimized as well, the energy required to free the caged particle decreased. They found that a spider with asymmetrically flexible base legs required less energy to release the caged particle compared with a spider with the symmetrical, pentagonal base that was first assumed.

The research, whose lead author is Ryan K. Krueger of Harvard University, but to which each co-author contributed equally, uses differentiable molecular dynamics to design complex reactions to direct the system to specific outcomes.

This process succeeded in producing a rigid spider that could accomplish the removal task. They then allowed the spider to flex, introducing a new free parameter that represented “configurable entropy.”

More information:
Ryan K. Krueger et al, Tuning Colloidal Reactions, Physical Review Letters (2024). DOI: 10.1103/PhysRevLett.133.228201

Journal information:
Physical Review Letters

The patch parameters were tuned so the spider as a whole was neither attracted or repelled by the cluster of shells, but the top-of-the-pyramid particle was attracted to patches on the shell particles by a force that could be varied by distance and strength. The dimensions of the spider and the radii of its head particle and base particles could also be adjusted.

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