# Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

arXiv: https://arxiv.org/abs/2011.02551 · HTML: https://mlsanity.com/paper/2011.02551/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics](https://mlsanity.com/paper/2209.14803/) (2022)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
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- [Copolymer Informatics with Multi-Task Deep Neural Networks](https://mlsanity.com/paper/2103.14174/) (2021)
- [Inverse Design of Copolymers Including Stoichiometry and Chain Architecture](https://mlsanity.com/paper/2410.02824/) (2024)
- [AI-guided inverse design and discovery of recyclable vitrimeric polymers](https://mlsanity.com/paper/2312.03690/) (2023)
- [POINT2: A Polymer Informatics Training and Testing Database](https://mlsanity.com/paper/2503.23491/) (2025)
- [polyGen: A Learning Framework for Atomic-level Polymer Structure Generation](https://mlsanity.com/paper/2504.17656/) (2025)
- [Sustainable Materials Discovery in the Era of Artificial Intelligence](https://mlsanity.com/paper/2601.21527/) (2026)
- [A Synthetically-accessible Universe of Chemically Recyclable Polymers](https://mlsanity.com/paper/2608.00149/) (2026)
- [PolymerGPT: Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design](https://mlsanity.com/paper/2608.01431/) (2026)
- [Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation](https://mlsanity.com/paper/2609.16028/) (2026)

## Abstract (arXiv)

The design/discovery of new materials is highly non-trivial owing to the near-infinite possibilities of material candidates, and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient, and severely constrained by the candidates that human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical and energy storage applications. This approach to learn from (and augment) human ingenuity is general, and can be extended to discover polymers with other targeted properties and performance measures.