Utilizing SciPy and other open source packages to provide a powerful API for materials manipulation in the Schr\"odinger Materials Suite

TL;DR

This paper discusses integrating open source scientific packages into the Schrödinger Materials Suite to enhance materials discovery workflows, including machine learning applications, for faster and more efficient research.

Contribution

It introduces a framework for incorporating open source tools into the Schrödinger Materials Suite, enabling improved workflows and machine learning implementations for materials discovery.

Findings

Open source packages streamline materials discovery workflows.

Machine learning applications are enhanced using open source tools.

Results are achieved more quickly and efficiently.

Abstract

The use of several open source scientific packages in the Schr\"odinger Materials Science Suite will be discussed. A typical workflow for materials discovery will be described, discussing how open source packages have been incorporated at every stage. Some recent implementations of machine learning for materials discovery will be discussed, as well as how open source packages were leveraged to achieve results faster and more efficiently.