evoxels: A differentiable physics framework for voxel-based microstructure simulations

TL;DR

Evoxels is a Python-based differentiable physics framework that integrates voxel microstructure data, physical simulations, and machine learning to advance inverse material design and microstructure-property understanding.

Contribution

It introduces a unified, differentiable voxel-based framework combining microscopy data, physics simulations, and machine learning for materials science applications.

Findings

Enables inverse design of microstructures from desired properties.

Integrates high-resolution microscopy with predictive modeling.

Facilitates data-driven optimization in materials discovery.

Abstract

Materials science inherently spans disciplines: experimentalists use advanced microscopy to uncover micro- and nanoscale structure, while theorists and computational scientists develop models that link processing, structure, and properties. Bridging these domains is essential for inverse material design where you start from desired performance and work backwards to optimal microstructures and manufacturing routes. Integrating high-resolution imaging with predictive simulations and data-driven optimization accelerates discovery and deepens understanding of process-structure-property relationships. The differentiable physics framework evoxels is based on a fully Pythonic, unified voxel-based approach that integrates segmented 3D microscopy data, physical simulations, inverse modeling, and machine learning.