# VAE for Modified 1-Hot Generative Materials Modeling, A Step Towards Inverse Material Design

arXiv: https://arxiv.org/abs/2401.06779 · HTML: https://mlsanity.com/paper/2401.06779/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Deep Reinforcement Learning for Inverse Inorganic Materials Design](https://mlsanity.com/paper/2210.11931/) (2022)
- [AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design](https://mlsanity.com/paper/2312.02308/) (2023)

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

- [Artificial Intelligence and Generative Models for Materials Discovery -- A Review](https://mlsanity.com/paper/2508.03278/) (2025)
- [AIMatDesign: Knowledge-Augmented Reinforcement Learning for Inverse Materials Design under Data Scarcity](https://mlsanity.com/paper/2507.00024/) (2025)

## Abstract (arXiv)

We investigate the construction of generative models capable of encoding physical constraints that can be hard to express explicitly. For the problem of inverse material design, where one seeks to design a material with a prescribed set of properties, a significant challenge is ensuring synthetic viability of a proposed new material. We encode an implicit dataset relationships, namely that certain materials can be decomposed into other ones in the dataset, and present a VAE model capable of preserving this property in the latent space and generating new samples with the same. This is particularly useful in sequential inverse material design, an emergent research area that seeks to design a material with specific properties by sequentially adding (or removing) elements using policies trained through deep reinforcement learning.