mlsanityData to 14 Oct 2026

Accelerating Material Design with the Generative Toolkit for Scientific Discovery

arXiv 2207.03928 ↗First posted 8 Jul 2022cs.LG cs.AI cs.SEcited by 16

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Abstract (arXiv)

With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on material design.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 27 of its 62 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

27 of 62 references.

References · 27

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  7. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  8. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
  9. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
  10. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 652
  11. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 516
  12. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
15 more
  1. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 364
  2. 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 311
  3. 2020GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationcited by 305
  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. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 264
  7. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 178
  8. 2022Biological Sequence Design with GFlowNetscited by 174
  9. 2017Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Modelscited by 117
  10. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Modelscited by 98
  11. 2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 71
  12. 2020Optimizing Molecules using Efficient Queries from Property Evaluationscited by 50
  13. 2022Regression Transformer: Concurrent sequence regression and generation for molecular language modelingcited by 50
  14. 2022TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoverycited by 45
  15. 2020Molecular Inverse-Design Platform for Material Industriescited by 5

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Citing papers per year: 2023: 5; 2024: 4; 2025: 6; 2026: 1.

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  1. 2023Unifying Molecular and Textual Representations via Multi-task Language Modellingcited by 93
  2. 2024Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptionscited by 47
  3. 2024Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistrycited by 31
  4. 2025Large Language Models for Combinatorial Optimization: A Systematic Reviewcited by 20
  5. 2023Generative Fractional Diffusion Modelscited by 12
  6. 2023M2Hub: Unlocking the Potential of Machine Learning for Materials Discoverycited by 8
  7. 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Toolscited by 7
  8. 2023Language models in molecular discoverycited by 6
  9. 2024Chemistry-Inspired Diffusion with Non-Differentiable Guidancecited by 6
  10. 2025Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language modelscited by 3
  11. 2025Fractional Diffusion Bridge Modelscited by 3
  12. 2025We Need Improved Data Curation and Attribution in AI for Scientific Discoverycited by 1
4 more
  1. 2023Human-AI Co-Creation Approach to Find Forever Chemicals Replacementscited by 0
  2. 2024Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approachcited by 0
  3. 2025From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimationcited by 0
  4. 2026DiffGA: Steering Frozen Diffusion Models for Materials Design via Evolutionary Searchcited by 0