Osman Goni Ridwan

AI + Materials Discovery • Generative Models • Crystal Structure Prediction

PhD candidate in Mechanical Engineering (UNC Charlotte). I build symmetry-aware generative models for crystals (LEGO-xtal) and high-throughput optimization workflows using ASE, VASP, and ML force fields (MACE / ALIGNN-FF). I collaborate across Computer Science and Computational Chemistry to accelerate functional materials discovery.

University of North Carolina at Charlotte · Materials Modeling and Informatics (MMI) Lab

Publications Download CV Google Scholar

Osman Goni Ridwan (OG Ridwan) portrait - AI materials discovery researcher

Research Interests

News

Peptide Materials Paper Published in Matter

Mar 2026

Water-mediated reconfigurable topology and mechanics in porous peptide materials is now published in Matter. The work shows humidity-driven, reversible topological reconfiguration and large mechanical changes in porous peptide materials.

Published Journal DOI

Preprint: Crystal Representation in the Reciprocal Space

Jan 2026

4D reciprocal space representation with scattering factors for symmetry-aware crystal structure matching and reconstruction now on arXiv.

Preprint arXiv DOI

AI-Assisted Crystal Generation Paper Published in npj Computational Materials

Jan 2026

AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment is now online. The work presents LEGO-xtal, a symmetry-aware generative framework capable of discovering 1,700+ novel sp²-carbon allotropes. Published in npj Comput. Mater., 12, 7.

Published DOI arXiv

New Preprint: Fully Differentiable Pipeline for Crystal Generation

Jan 2026

Crystal Generation using the Fully Differentiable Pipeline and Latent Space Optimization now available on arXiv. This work extends crystal design with GPU-accelerated optimization in latent space.

Preprint arXiv

Master's Degree Completed

Dec 2025

Completed M.S. in Mechanical Engineering at UNC Charlotte with perfect 4.00/4.00 GPA. Research focused on symmetry-aware generative models and crystal design.

Milestone

Awarded Best Poster at CITRANS Symposium

Aug 2025

Recognition for research presented at the CITRANS Symposium, University of North Carolina at Charlotte.

Award

LEGO-xtal Preprint and Code Released

Jul 2025

Major update to LEGO-xtal preprint with complete code refactor and release on arXiv. Open-source implementation now available for reproducibility.

Preprint arXiv Code

Education

Research Projects

LEGO-xtal: Symmetry-Aware Generative System for Crystal Design

PyTorch, Deep Generative Models, PyXtal, ASE, VASP, HPC

  • Developed a symmetry-informed generative model that respects space-group constraints and periodicity for crystal structure generation.
  • Expanded from 25 known low-energy sp²-carbon allotropes to over 1,700 new candidate structures within 0.5 eV/atom of graphite.
  • Enabled targeted generation toward local chemical environments using geometry-aware latent representations.

Code | paper

LEGO-xtal-GPU: Differentiable CSP and Latent Optimization

GPU parallelization and batching, CVAE, SO(3) descriptors, differentiable optimization

  • Implemented GPU-parallel batched evaluation of structure descriptors and objectives to scale candidate generation and refinement.
  • Built a fully differentiable pipeline that backpropagates through representation-space objectives and couples them with latent-space refinement.
  • Achieved ~5× speedup via batching and GPU acceleration, with improved success in meeting target local-environment constraints.

Code | arXiv

PyXtal-DFTB: High-Throughput Property Screening

ASE, DFTB+, automated workflows

  • Built an automated pipeline for elastic-constant computation of organic crystals.
  • Enabled mechanical screening of peptide hydrates in collaboration with experimental partners.

Code

ML-Assisted X-ray Absorption and Optical Modeling of Boron Nitride

DFT (VASP), first-principles spectroscopy

  • Conducted DFT simulations and collaborated with LLNL on ML-assisted X-ray absorption spectroscopy and optical modeling of boron nitride.

paper

Reciprocal-Space Representations for Structure Matching

Scattering features, geometric embeddings, PyTorch

  • Developed 4D scattering-informed representations for structure matching and reconstruction.
  • Enabled quantitative comparison between simulation outputs and experimental signals.

arXiv

xtal-builder: Agentic Crystal Design Assistant

LLM tool-calling, Ollama, LangChain/LangGraph, ASE, PyXtal, MLIP

  • Building an agent that orchestrates structure generation, simulation, and refinement.
  • Implements log parsing, failure detection, and auto-retry loops on HPC.
  • Goal: autonomous crystal design from minimal user input.

Code

Publications & Preprints

  1. AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment (LEGO-xtal) — 2026. O. G. Ridwan, S. Pitié, M. S. Raj, D. Dai, G. Frapper, H‑F. Xue, Q. Zhu. Published in npj Computational Materials, 12, 7. Journal | arXiv
    published BibTeX
  2. Crystal Generation using the Fully Differentiable Pipeline and Latent Space Optimization — 2026. O. G. Ridwan, G. Frapper, H‑F. Xue, Q. Zhu. arXiv
    preprint BibTeX
  3. Unveiling X‑ray Absorption Signatures of Boron Nitride via First‑Principles Simulation and Machine Learning — 2025. W‑Y Sun, O. G. Ridwan, S. O. Kucheyev, Q. Zhu, L‑W Wan. Accepted for publication in Next Materials. Journal
    preprint BibTeX
  4. Water-mediated reconfigurable topology and mechanics in porous peptide materials — 2026. V. Athiyarath, E. Naranjo, D. Dave, O. G. Ridwan, D. A. A. Rodriguez, Q. Zhu, M. Monti, G. Diaz Miron, D. Banerjee, A. Hassanali, M. C. Neary, E. G. Keeler, S. Zhang, R. V. Ulijn, X. Chen. Published in Matter, 102669 (2026). Journal | DOI
    published BibTeX
  5. Crystal Representation in the Reciprocal Space — 2026. O. G. Ridwan, H. Xue, Y. Chen, H. Cherukuri, Q. Zhu. Preprint available on arXiv. arXiv
    preprint BibTeX
  6. Numerical Computation of Thermal Performance of Earth Pipe Cooling Systems — 2023. O. G. Ridwan, S. S. Sifat, F. E. Idrish, M. M. Karim. Proceedings of the 13th International Conference on Marine Technology (MARTEC 2022). SSRN
    conference BibTeX

Blog & Notes

I write short, reproducible posts showing how to rebuild my results (datasets, code, parameters) and commentary on ML for materials.

All posts (folder)

Contact

Email: oridwan@charlotte.edu

Google Scholar · GitHub (LEGO-xtal) · LinkedIn