Index Catalog // Deep Blue Data

Need language around using Deep Blue Data as a repository for your NIH data sharing plan? See Data Sharing Boilerplate

Start Over

Filtering by: Creator Glotzer, Sharon C. Creator Vo, Thi Language Python

A Microscopic Theory of Entropic Bonding – Energy/Simulation Scripts with Representative Data

Creator:

Vo, Thi and Glotzer, Sharon C.

Description:

The goal of this project is to develop a first principle driven approach for predicting the self-assembly behavior of entropically driven crystallization. We first developed a set of mean-field theoretical framework that captures the relevant energetic contributions to the assembly process and then evaluate relevant terms within our framework to determine the excess free energy of formation for each lattice (matlab/octave codes). We then validate theoretical predictions of relevant features like shape and bonding orbitals using standard MD simulations using HOOMD-Blue (simulation scripts). and This research was supported by the Office of the Undersecretary of Defense for Research and Engineering (OUSD(R&E)), Newton Award for Transformative Ideas during the COVID-19 Pandemic, Award number HQ00342010030.

Keyword:

Self-Assembly, Entropy, Thermodynamics, Simulations, and Theory

Citation to related publication:

Vo, T., & Glotzer, S. C. (2021). Microscopic Theory of Entropic Bonding for Colloidal Crystal Prediction. ArXiv:2107.02081 [Cond-Mat]. http://arxiv.org/abs/2107.02081

Discipline:

Science