mlbands 1.0.1

A Python package that implements automatic prediction of electronic band gaps for a set of materials based on training data

ML Band Gaps (Materials)

Ideal candidate: skilled ML data scientist with solid knowledge of materials science.

Overview

The aim of this task is to create a python package that implements automatic prediction of electronic band gaps for a set of materials based on training data.

User story

As a user of this software I can predict the value of an electronic band gap after passing training data and structural information about the target material.

Requirements

• suggest the bandgap values for a set of materials designated by their crystallographic and stoichiometric properties
• the code shall be written in a way that can facilitate easy addition of other characteristics extracted from simulations (forces, pressures, phonon frequencies etc)

Expectations

• the code shall be able to suggest realistic values for slightly modified geometry sets - eg. trained on Si and Ge it should suggest the value of bandgap for Si49Ge51 to be between those of Si and Ge
• modular and object-oriented implementation
• commit early and often - at least once per 24 hours

Timeline

We leave exact timing to the candidate. Must fit Within 5 days total.

Notes

• use a designated github repository for version control
• suggested source of training data: materialsproject.org