Inverse Design of Inorganic Compounds with Generative AI
physics.chem-ph, cond-mat.mtrl-sci, cs.LG
Submitted: 2026-04-11
Updated: 2026-08-26
Terminology
Sources
- An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
- An Introduction to Variational Autoencoders
- A Survey of Large Language Models
- Reinforcement Learning: An Overview
- Accelerating inverse materials design using generative diffusion models with reinforcement learning
- Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
- The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
- Junction Tree Variational Autoencoder for Molecular Graph Generation
- Crystal Structure Prediction by Joint Equivariant Diffusion
- Integrating electronic structure into generative modeling of inorganic materials
- Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
- MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models
- LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models
- PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation
- MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data
- MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
- Perspective: Towards sustainable exploration of chemical spaces with machine learning
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