Inverse Design of Inorganic Compounds with Generative AI

Hannes Kneiding; Lucía Morán-González; Nishamol Kuriakose; Ainara Nova; David Balcells

physics.chem-phApr 11, 2026

Inverse Design of Inorganic Compounds with Generative AI

Authors: Hannes Kneiding, Lucía Morán-González, Nishamol Kuriakose, Ainara Nova, David Balcells

Organizations: Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway

Abstract

Machine learning is revolutionizing chemistry. Beyond the value of predictive models accelerating virtual screening, generative AI aims at enabling inverse design, reversing the compound-to-property prediction paradigm into property-to-compound generation. Chemists now have access to a rich AI toolbox for organic chemistry, including drug discovery. However, the application of these methods to inorganic compounds remains limited by the challenges posed by their intrinsic nature. This Review analyzes how these challenges have been addressed, considering widely diverse systems ranging from molecules to crystals, including transition metal complexes and microporous materials. The analysis focuses on how generative AI methods have evolved towards data-representation-model pipelines that address the full complexity of inorganic compounds, including their chemical composition, geometry, symmetry, and electronic structure. Future directions, like benchmark standardization and the development of synthesizability metrics, are also discussed.

Figures & tables

Figure 1: Inorganic compounds covered in this Perspective. a) Transition metal complexes are built around a metal center supported by a set of ligands. b) Microporous inorganic materials have porous reticular structures made with metal node-linker (MOFs) or aluminosilicate (zeolites) building blocks. In MOFs, the metal node can also be a single-atom cation. c) Non-porous inorganic crystals form a compact, periodic structure made of metals or ions.

Name a	Size	Contents	Ref.
CSD	1.4M	Experimental composition and structure data	Groom et al. (2016)
COD	0.5M	Experimental composition and structure data	Gražulis et al. (2011)
ICSD	328k	Experimental & computational composition and structure data	Zagorac et al. (2019)
PCD	395k	Experimental & computational composition and structure data	Villars and Cenzual (2024)
MP	785k	Computational structure, quantum properties, and thermodynamic data	Jain et al. (2013)
OQMD	1.4M	Computational structure, quantum properties, and thermodynamic data	Saal et al. (2013)

Table 1: Datasets available for training generative AI models for inorganic compounds.

Representations	Format a	Connect.	Isomerism	3D geom.	Periodic	DL/GA
SMILES b	String	Yes	Partial c	No	No	Both
SELFIES b	String	Yes	Partial c	No	No	Both
SLICES	String	Yes	Yes	Yes	Yes	Both
Graph d	Graph	Yes	Optional e	Optional e	Yes	Both
3D grid f	Point-wise grid	No	Yes	Yes	Yes	DL
Voxel f	Cubic grid	No	Yes	Yes	Yes	DL

Table 2: Selected representations for generative AI methods for inorganic compounds.

Figure 2: Timeline of selected generative AI methods, including genetic algorithms (GAs), generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models (DMs), and large language models (LLMs), for inorganic compounds, including transition metal complexes (TMCs), non-porous crystals, metal organic frameworks (MOFs), and zeolites.

Figure 3: Selected generative AI methods for TMCs. a) GA using genetic operations, fitness optimization, and selection in Pareto front problems; b) The PL-MOGA GA for directional multi-objective optimization over selected regions of the Pareto front; c) VAE encoding and decoding metal ligands through a compressed latent space representation; d) The CatDRX VAE encoding both TMCs and reaction conditions for the inverse design of novel catalysts.

Figure 4: Diffusion models for non-porous inorganic crystals. a) Noising using of crystal structure representation with Gaussian noise and learned denoising with E(3)-equivariant graph neural networks; b) The MatterGen diffusion model doing unconditional generation with nearly DFT quality and conditional generation on multiple properties, using SUN metrics for evaluation and experiments for verification.

Figure 5: LLM models for MOFs. a) Whereas the classical approach encodes the materials information with tokens, quantum natural language processing (QNLP) uses a qubit representation. b) The generative process leverages a four-category classification task (low, moderate low, moderate high, and high) over two target properties (pore volume and CO 2 Henry’s constant).

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A Padding Method for Enhanced Encoding of Inorganic Structures with Varying Chemical Compositions

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Representation Learning Materials Science Crystal Structure Generation

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Designing novel inorganic materials through generative models remains an important challenge for material science, driven by the complexity and diversity of inorganic structures across expansive chemical compositions and structural landscape. The vast combinatorial space of inorganic compounds demands innovative, AI-driven approaches to overcome limitations in generative accuracy and efficiency. To address this, we introduce a novel method that redefines the encoding and generation of inorganic materials by utilizing domain-specific symmetry-aware representation. Our approach not only refines the representation of intricate inorganic structures but also contributes to the field of material discovery by enhancing the precision and stability of generated candidates. Central to our methodology is a novel padding technique that exploits crystal symmetry information to enhance the encoding process. By integrating Wyckoff position length-aware padding into an encoder architecture, we achieve a more robust informed representation of inorganic materials. This symmetry-driven enhancement improves deep learning models to generate stable, previously unexplored inorganic structures with superior accuracy and computational efficiency. Furthermore, we introduce an end-to-end system that leverages the machine learning potential models to seamlessly generate novel, even those unseen in the training data, and stable inorganic materials from initial data to validated output. This pipeline integrates advanced generative models with stability analysis, marking a significant leap forward in the automated exploration and design of next-generation inorganic materials. Our method improved reconstruction accuracy 5.3% in proton conductor data, and generated 63.5% more novel stable inorganic material to baseline model on the perov-5 dataset.

Abstract

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