arXiv subjects

Shih-Kuang

Publications and source records attributed to Shih-Kuang.

2 recordsLinked to original sources

arXiv2026

Exploratory digital alchemy for colloidal crystal discovery

Digital Alchemy (DA), introduced by Van Anders et al., is a statistical mechanics-based generalized thermodynamic ensemble method that employs computer simulations to optimize colloidal particle design. This approach applies the principles of statistical mechanics to predict and tailor particle attributes that lead to desired self-assembled structures or material properties. However, as an inverse design method, its main limitation is that the target structure must be known \textit{a priori}. Therefore, the optimal design from DA does not guarantee the targeted structure is the most or the only stable one. This highlights the importance of forward design with an exploratory scheme for optimizing novel colloid designs, which becomes more suitable in such cases. In this paper, we introduce Exploratory Digital Alchemy (EDA), an enhanced forward design scheme that begins by releasing the constraint of the target crystal from DA, followed by an exploration-oriented bias that has been extensively used in enhanced sampling methods such as metadynamics (MetaD). We demonstrate the utility of EDA through examples involving particles interacting via a two-dimensional Lennard-Jones Gauss potential (LJGP) and a three-dimensional oscillating pair potential (OPP). We applied EDA to study the free energy landscapes given different potential parameters of LJGP at different temperatures. With the exploratory scheme, we've also successfully identified a wide range of OPP potential parameters that stabilize metastable Frank-Kasper phases. Our approach fuses the standard DA framework with metadynamics, which could potentially be useful for studying alchemical reactions in a generalized ensemble.

cond-mat.soft↗

arXiv2023

Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation

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.

cond-mat.soft↗