# Designing Composites with Target Effective Young's Modulus using Reinforcement Learning

arXiv: https://arxiv.org/abs/2110.05260 · HTML: https://mlsanity.com/paper/2110.05260/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 122 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Deep Reinforcement Learning for De-Novo Drug Design](https://mlsanity.com/paper/1711.10907/) (2017)
- [Two-Scale Topology Optimization with Microstructures](https://mlsanity.com/paper/1706.03189/) (2017)
- [Prediction and optimization of mechanical properties of composites using convolutional neural networks](https://mlsanity.com/paper/1906.00094/) (2019)
- [Autonomous robotic nanofabrication with reinforcement learning](https://mlsanity.com/paper/2002.11952/) (2020)
- [Taichi: An Open-Source Computer Graphics Library](https://mlsanity.com/paper/1804.09293/) (2018)
- [PuzzleFlex: kinematic motion of chains with loose joints](https://mlsanity.com/paper/1906.08708/) (2019)

## Built on by (most cited first, 3 of 3)

- [Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools](https://mlsanity.com/paper/2303.00192/) (2023)
- [Surrogate Modeling for the Design of Optimal Lattice Structures using Tensor Completion](https://mlsanity.com/paper/2510.07474/) (2025)
- [Data-Driven Nonlinear Deformation Design of 3D-Printable Shells](https://mlsanity.com/paper/2408.15097/) (2024)

## Abstract (arXiv)

Advancements in additive manufacturing have enabled design and fabrication of materials and structures not previously realizable. In particular, the design space of composite materials and structures has vastly expanded, and the resulting size and complexity has challenged traditional design methodologies, such as brute force exploration and one factor at a time (OFAT) exploration, to find optimum or tailored designs. To address this challenge, supervised machine learning approaches have emerged to model the design space using curated training data; however, the selection of the training data is often determined by the user. In this work, we develop and utilize a Reinforcement learning (RL)-based framework for the design of composite structures which avoids the need for user-selected training data. For a 5 × 5 composite design space comprised of soft and compliant blocks of constituent material, we find that using this approach, the model can be trained using 2.78% of the total design space consists of 225 design possibilities. Additionally, the developed RL-based framework is capable of finding designs at a success rate exceeding 90%. The success of this approach motivates future learning frameworks to utilize RL for the design of composites and other material systems.