# Neural Network Accelerated Process Design of Polycrystalline Microstructures

arXiv: https://arxiv.org/abs/2305.00003 · HTML: https://mlsanity.com/paper/2305.00003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 42 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization](https://mlsanity.com/paper/2009.09706/) (2020)

## Built on by (most cited first, 2 of 2)

- [Machine learning for structure-guided materials and process design](https://mlsanity.com/paper/2312.14552/) (2023)
- [Machine-learning-enabled interpretation of tribological deformation patterns in large-scale MD data](https://mlsanity.com/paper/2512.05818/) (2025)

## Abstract (arXiv)

Computational experiments are exploited in finding a well-designed processing path to optimize material structures for desired properties. This requires understanding the interplay between the processing-(micro)structure-property linkages using a multi-scale approach that connects the macro-scale (process parameters) to meso (homogenized properties) and micro (crystallographic texture) scales. Due to the nature of the problem's multi-scale modeling setup, possible processing path choices could grow exponentially as the decision tree becomes deeper, and the traditional simulators' speed reaches a critical computational threshold. To lessen the computational burden for predicting microstructural evolution under given loading conditions, we develop a neural network (NN)-based method with physics-infused constraints. The NN aims to learn the evolution of microstructures under each elementary process. Our method is effective and robust in finding optimal processing paths. In this study, our NN-based method is applied to maximize the homogenized stiffness of a Copper microstructure, and it is found to be 686 times faster while achieving 0.053% error in the resulting homogenized stiffness compared to the traditional finite element simulator on a 10-process experiment.