MatOpt: A Python Package for Nanomaterials Design Using Discrete Optimization

Area of Science:

• Materials Science
• Computational Materials Science
• Optimization

Background:

• Advances in synthesis and computational methods accelerate materials development.
• Mathematical optimization models can navigate complex atomic arrangement design spaces.
• Previous work demonstrated optimization for nanostructured materials design.

Purpose of the Study:

• To identify common features of materials optimization problems.
• To develop a tool that models these problems using mixed-integer linear optimization.
• To facilitate the on-demand design of nanostructured materials.

Main Methods:

• Highlighting commonalities in materials optimization problems.
• Formalizing design space representation and optimization model formulation.
• Developing the MatOpt Python package for mixed-integer linear optimization.

Main Results:

• Identification of common features in materials optimization.
• Creation of MatOpt, a Python package for nanostructured materials design.
• Demonstration of efficient modeling via mixed-integer linear optimization.

Conclusions:

• MatOpt bridges the gap between materials science and optimization expertise.
• The package lowers barriers to applying numerical optimization in materials development.
• Facilitates rigorous, on-demand design of novel nanostructured materials.