Genarris: Random Generation of Molecular Crystal Structures and Fast Screening with a Harris Approximation

TL;DR

Genarris is a Python package that efficiently generates and screens molecular crystal structures using random sampling, Harris approximation for fast energy evaluation, and machine learning for clustering, aiding in crystal structure prediction.

Contribution

It introduces a novel combination of Harris approximation and machine learning clustering within a flexible framework for molecular crystal structure generation and screening.

Findings

Effective clustering of packing motifs using RCD descriptor

Successful demonstration on three test cases

Efficient exploration of potential energy landscape

Abstract

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory (DFT) is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.