# Machine learning for structure-guided materials and process design

arXiv: https://arxiv.org/abs/2312.14552 · HTML: https://mlsanity.com/paper/2312.14552/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 62 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design](https://mlsanity.com/paper/2106.03620/) (2021)
- [Solving stochastic inverse problems for property-structure linkages using data-consistent inversion and machine learning](https://mlsanity.com/paper/2010.03603/) (2020)
- [Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties](https://mlsanity.com/paper/2309.00305/) (2023)
- [Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization](https://mlsanity.com/paper/2009.09706/) (2020)
- [An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics](https://mlsanity.com/paper/2004.11948/) (2020)
- [Neural Network Accelerated Process Design of Polycrystalline Microstructures](https://mlsanity.com/paper/2305.00003/) (2023)
- [A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization](https://mlsanity.com/paper/2111.00916/) (2021)
- [A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables](https://mlsanity.com/paper/2311.11343/) (2023)

## Abstract (arXiv)

In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to take into account manufacturing processes and tailor materials design approaches to support downstream process design approaches. As a major step into this direction, we present a holistic and generic optimization approach that covers the entire process-structure-property chain in materials engineering. Our approach specifically employs machine learning to address two critical identification problems: a materials design problem, which involves identifying near-optimal material microstructures that exhibit desired properties, and a process design problem that is to find an optimal processing path to manufacture these microstructures. Both identification problems are typically ill-posed, which presents a significant challenge for solution approaches. However, the non-unique nature of these problems offers an important advantage for processing: By having several target microstructures that perform similarly well, processes can be efficiently guided towards manufacturing the best reachable microstructure. The functionality of the approach is demonstrated at manufacturing crystallographic textures with desired properties in a simulated metal forming process.