December 1 - 6, 2024
Boston, Massachusetts
Symposium Supporters
2024 MRS Fall Meeting & Exhibit
NM07.11.07

Classification of cOmplex Local Environments in Systems of Particle Shapes Through Shape-sYmmetry Encoded Data Augmentation

When and Where

Dec 6, 2024
3:30pm - 3:45pm
Hynes, Level 2, Room 201

Presenter(s)

Co-Author(s)

Sun-Ting Tsai1,Shih-Kuang Lee1,Sharon Glotzer1

University of Michigan1

Abstract

Sun-Ting Tsai1,Shih-Kuang Lee1,Sharon Glotzer1

University of Michigan1
Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for better order parameters in complex systems that are challenging to study using traditional approaches. However, the application of machine learning to self-assembly on systems of particle shapes is still underexplored. To address this gap, we propose a simple, physics-agnostic, yet powerful approach that involves training a multilayer perceptron (MLP) as a local environment classifier for systems of particle shapes, using input features such as particle distances and orientations. Our MLP classifier is trained in a supervised manner with a shape symmetry-encoded data augmentation technique without the need for any conventional roto-translations invariant symmetry functions. We evaluate the performance of our classifiers on four different scenarios involving self-assembly of cubic structures, 2-dimensional and 3-dimensional patchy particle shape systems, hexagonal bipyramids with varying aspect ratios, and truncated shapes with different degrees of truncation. The proposed training process and data augmentation technique are both straightforward and flexible, enabling easy application of the classifier to other processes involving particle orientations. Our work thus presents a valuable tool for investigating self-assembly processes on systems of particle shapes, with potential applications in structure identification of any particle-based or molecular system where orientations can be defined.

Symposium Organizers

Qian Chen, University of Illinois at Urbana-Champaign
Sijie Chen, Karolinska Institutet
Bin Liu, National University of Singapore
Xin Zhang, Pacific Northwest National Laboratory

Symposium Support

Silver
ZepTools Technology Co., Ltd.

Session Chairs

Lintong Wu
Xin Zhang

In this Session