Programming patchy particles for materials assembly design

Author:

King Ella M.1ORCID,Du Chrisy Xiyu23ORCID,Zhu Qian-Ze2,Schoenholz Samuel S.45ORCID,Brenner Michael P.24ORCID

Affiliation:

1. Department of Physics, Harvard University, Cambridge, MA 02139

2. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02139

3. Mechanical Engineering, University of Hawai’i at Mānoa, Honolulu, HI 96822

4. Google Research, Mountainview, CA 94043

5. OpenAI, San Francisco, CA 94110

Abstract

Direct design of complex functional materials would revolutionize technologies ranging from printable organs to novel clean energy devices. However, even incremental steps toward designing functional materials have proven challenging. If the material is constructed from highly complex components, the design space of materials properties rapidly becomes too computationally expensive to search. On the other hand, very simple components such as uniform spherical particles are not powerful enough to capture rich functional behavior. Here, we introduce a differentiable materials design model with components that are simple enough to design yet powerful enough to capture complex materials properties: rigid bodies composed of spherical particles with directional interactions (patchy particles). We showcase the method with self-assembly designs ranging from open lattices to self-limiting clusters, all of which are notoriously challenging design goals to achieve using purely isotropic particles. By directly optimizing over the location and interaction of the patches on patchy particles using gradient descent, we dramatically reduce the computation time for finding the optimal building blocks.

Funder

DOD | USN | Office of Naval Research

National Science Foundation

Publisher

Proceedings of the National Academy of Sciences

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Coarse-grained modeling of DNA-protein interactions helps elucidate DNA compaction;Biophysical Journal;2024-08

2. Machine learning meets physics: A two-way street;Proceedings of the National Academy of Sciences;2024-06-24

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