# Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials

arXiv: https://arxiv.org/abs/1911.05020 · HTML: https://mlsanity.com/paper/1911.05020/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 30 of 46)

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- [Scalable Diffusion for Materials Generation](https://mlsanity.com/paper/2311.09235/) (2023)
- [Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints](https://mlsanity.com/paper/2203.14352/) (2022)
- [Scalable deeper graph neural networks for high-performance materials property prediction](https://mlsanity.com/paper/2109.12283/) (2021)
- [Towards Symmetry-Aware Generation of Periodic Materials](https://mlsanity.com/paper/2307.02707/) (2023)
- [Deep Reinforcement Learning for Inverse Inorganic Materials Design](https://mlsanity.com/paper/2210.11931/) (2022)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules](https://mlsanity.com/paper/2501.03278/) (2025)
- [Enhancing Vision-Language Compositional Understanding with Multimodal Synthetic Data](https://mlsanity.com/paper/2503.01167/) (2025)
- [End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction](https://mlsanity.com/paper/2401.03862/) (2024)
- [Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey](https://mlsanity.com/paper/2505.16379/) (2025)
- [Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials](https://mlsanity.com/paper/2204.11953/) (2022)
- [Artificial Intelligence and Generative Models for Materials Discovery -- A Review](https://mlsanity.com/paper/2508.03278/) (2025)
- [Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy](https://mlsanity.com/paper/2305.05247/) (2023)
- [Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling](https://mlsanity.com/paper/2602.20210/) (2026)
- [MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art](https://mlsanity.com/paper/2109.04007/) (2021)
- [Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes](https://mlsanity.com/paper/2202.13309/) (2022)
- [Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie](https://mlsanity.com/paper/2601.12554/) (2026)
- [Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm](https://mlsanity.com/paper/2309.06710/) (2023)
- [Learning from Synthetic Data for Visual Grounding](https://mlsanity.com/paper/2403.13804/) (2024)
- [Generative Learning of Densities on Manifolds](https://mlsanity.com/paper/2503.03963/) (2025)
- [Generative Models for Crystalline Materials](https://mlsanity.com/paper/2511.22652/) (2025)
- [Interpretable discovery of new semiconductors with machine learning](https://mlsanity.com/paper/2101.04383/) (2021)
- [A Binded VAE for Inorganic Material Generation](https://mlsanity.com/paper/2112.09570/) (2021)
- [Deep learning for size-agnostic inverse design of random-network 3D printed mechanical metamaterials](https://mlsanity.com/paper/2212.12047/) (2022)
- [Generative Design of inorganic compounds using deep diffusion language models](https://mlsanity.com/paper/2310.00475/) (2023)
- [MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations](https://mlsanity.com/paper/2406.12368/) (2024)

## Abstract (arXiv)

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84.5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules. Our algorithm could be used to speed up inverse design or computational screening of inorganic materials.