mlsanityData to 14 Oct 2026

Inverse Design of Inorganic Compounds with Generative AI

arXiv 2604.11827 ↗First posted 11 Apr 2026physics.chem-ph cond-mat.mtrl-sci cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 42 of its 274 references that are in the map. First posted on arXiv 11 Apr 2026.

Builds on

42 of 274 references.

References · 42

  1. 2023A Survey of Large Language Modelscited by 2,576
  2. 2019An Introduction to Variational Autoencoderscited by 1,000
  3. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 652
  4. 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 297
  5. 2018Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Modelscited by 275
  6. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 270
  7. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  8. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 200
  9. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 148
  10. 2022Equivariant 3D-Conditional Diffusion Models for Molecular Linker Designcited by 122
  11. 2022The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalystscited by 95
  12. 2024Fine-Tuned Language Models Generate Stable Inorganic Materials as Textcited by 93
30 more
  1. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimizationcited by 73
  2. 2020The Synthesizability of Molecules Proposed by Generative Modelscited by 68
  3. 2020An invertible crystallographic representation for general inverse design of inorganic crystals with targeted propertiescited by 53
  4. 2023ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesiscited by 51
  5. 2019Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materialscited by 46
  6. 2024Space Group Informed Transformer for Crystalline Materials Generationcited by 41
  7. 2018CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networkscited by 31
  8. 2020Machine learning for electronically excited states of moleculescited by 30
  9. 2022Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraintscited by 30
  10. 2023MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Designcited by 24
  11. 2025LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Modelscited by 23
  12. 2021Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworkscited by 20
  13. 2020Big-Data Science in Porous Materials: Materials Genomics and Machine Learningcited by 19
  14. 2022Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Designcited by 17
  15. 2024Structure-based out-of-distribution (OOD) materials property prediction: a benchmark studycited by 17
  16. 2021Inverse design of two-dimensional materials with invertible neural networkscited by 15
  17. 2025LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generationcited by 13
  18. 2021MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworkscited by 11
  19. 2025MOFGPT: Generative Design of Metal-Organic Frameworks using Language Modelscited by 11
  20. 2023A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capturecited by 10
  21. 2025Establishing baselines for generative discovery of inorganic crystalscited by 9
  22. 2025MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Modelscited by 7
  23. 2025MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modelingcited by 7
  24. 2025Generative Models for Crystalline Materialscited by 4
  25. 2025PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generationcited by 3
  26. 2024Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoderscited by 2
  27. 2025MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Datacited by 2
  28. 2025Enhancing Materials Discovery with Valence Constrained Design in Generative Modelingcited by 2
  29. 2024Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processingcited by 1
  30. 2025Synergizing chemical and AI communities for advancing laboratories of the futurecited by 1

Built on by

Not cited yet.