Inferring interaction potentials from stochastic particle trajectories
Accurate interaction potentials between microscopic components such as colloidal particles or cells are crucial to understanding a range of processes, including colloidal crystallization, bacterial colony formation, and cancer metastasis. Even in systems where the precise interaction mechanisms are...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-04-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.7.023075 |
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| author | Ella M. King Megan C. Engel Caroline Martin Alp M. Sunol Qian-Ze Zhu Sam S. Schoenholz Vinothan N. Manoharan Michael P. Brenner |
| author_facet | Ella M. King Megan C. Engel Caroline Martin Alp M. Sunol Qian-Ze Zhu Sam S. Schoenholz Vinothan N. Manoharan Michael P. Brenner |
| author_sort | Ella M. King |
| collection | DOAJ |
| description | Accurate interaction potentials between microscopic components such as colloidal particles or cells are crucial to understanding a range of processes, including colloidal crystallization, bacterial colony formation, and cancer metastasis. Even in systems where the precise interaction mechanisms are unknown, effective interactions can be measured to inform simulation and design. However, these measurements are difficult and time-intensive, and often require conditions that are drastically different from in situ conditions of the system of interest. Moreover, existing methods of measuring interparticle potentials rely on constraining a small number of particles at equilibrium, placing limits on which interactions can be measured. We introduce a method for inferring interaction potentials directly from trajectory data of interacting particles. We explicitly solve the equations of motion to find a form of the potential that maximizes the probability of observing a known trajectory. Our method is valid for systems both in and out of equilibrium, is well-suited to large numbers of particles interacting in typical system conditions, and does not assume a functional form of the interaction potential. We apply our method to infer the interactions of colloidal spheres from experimental data, successfully extracting the range and strength of a depletion interaction from the motion of the particles. |
| format | Article |
| id | doaj-art-3193d2687a8f4a7797c4388b89fac722 |
| institution | DOAJ |
| issn | 2643-1564 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-3193d2687a8f4a7797c4388b89fac7222025-08-20T03:14:47ZengAmerican Physical SocietyPhysical Review Research2643-15642025-04-017202307510.1103/PhysRevResearch.7.023075Inferring interaction potentials from stochastic particle trajectoriesElla M. KingMegan C. EngelCaroline MartinAlp M. SunolQian-Ze ZhuSam S. SchoenholzVinothan N. ManoharanMichael P. BrennerAccurate interaction potentials between microscopic components such as colloidal particles or cells are crucial to understanding a range of processes, including colloidal crystallization, bacterial colony formation, and cancer metastasis. Even in systems where the precise interaction mechanisms are unknown, effective interactions can be measured to inform simulation and design. However, these measurements are difficult and time-intensive, and often require conditions that are drastically different from in situ conditions of the system of interest. Moreover, existing methods of measuring interparticle potentials rely on constraining a small number of particles at equilibrium, placing limits on which interactions can be measured. We introduce a method for inferring interaction potentials directly from trajectory data of interacting particles. We explicitly solve the equations of motion to find a form of the potential that maximizes the probability of observing a known trajectory. Our method is valid for systems both in and out of equilibrium, is well-suited to large numbers of particles interacting in typical system conditions, and does not assume a functional form of the interaction potential. We apply our method to infer the interactions of colloidal spheres from experimental data, successfully extracting the range and strength of a depletion interaction from the motion of the particles.http://doi.org/10.1103/PhysRevResearch.7.023075 |
| spellingShingle | Ella M. King Megan C. Engel Caroline Martin Alp M. Sunol Qian-Ze Zhu Sam S. Schoenholz Vinothan N. Manoharan Michael P. Brenner Inferring interaction potentials from stochastic particle trajectories Physical Review Research |
| title | Inferring interaction potentials from stochastic particle trajectories |
| title_full | Inferring interaction potentials from stochastic particle trajectories |
| title_fullStr | Inferring interaction potentials from stochastic particle trajectories |
| title_full_unstemmed | Inferring interaction potentials from stochastic particle trajectories |
| title_short | Inferring interaction potentials from stochastic particle trajectories |
| title_sort | inferring interaction potentials from stochastic particle trajectories |
| url | http://doi.org/10.1103/PhysRevResearch.7.023075 |
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