SGML: A Python library for solution-guided machine learning

Abstract

Researchers have long been concerned with the extrapolation capabilities of machine learning (ML) models, particularly when dealing with insufficient training data. The recently proposed solution-guided machine learning (SGML) method addresses this issue by integrating existing solutions as additional features to supplement limited training data. We have applied this method to solve the strong nonlinearity in nanoindentation and present an approximate solution to the tangential entropic force in an asymmetrical two dimensional bilayer. To make this method more accessible, we developed a user-friendly Python library called SGML, available on GitHub and PyPI. This paper introduces the architecture and functionality of the library, provides a usage example, and discusses its potential impact and applications.