IBM/materials
Mar 2025 – Jan 2026
4 Months active
Languages Used
MarkdownPythonSVG
Technical Skills
Deep LearningGraph Neural NetworksJupyter NotebookJupyter NotebooksMachine LearningPyTorch
Rodrigo Neumann
PROFILE
Rodrigo Neumann
Rafael Neumann contributed to the IBM/materials repository by developing and enhancing deep learning models for materials science applications. Over four months, he delivered features such as POS-EGNN model improvements for energy and force calculations, a Raman spectra prediction module leveraging crystallographic data, and streamlined model deployment for arXiv submission. His work emphasized reproducibility and maintainability, including comprehensive documentation, onboarding assets, and dependency management. Using Python, PyTorch, and Jupyter Notebooks, Rafael addressed both feature development and stability, such as resolving device initialization issues in graph neural networks. His contributions demonstrated depth in scientific computing and practical solutions for research workflows.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
17Total
Bugs
1
Commits
17
Features
5
Lines of code
8,561
Activity Months4
Your Network
16 peopleSame Organization
@br.ibm.com5
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on IBM/materials: Key feature delivered was preparing the repository for arXiv submission and enabling direct download of model checkpoints, along with updates to README/docs, adjustments to model loading, and cleanup of unnecessary files. No explicit bugs reported for this month; the work centered on packaging, reproducibility, and documentation to support compliant submissions and easier distribution.
1 Commits • 1 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on IBM/materials: Key feature delivered was preparing the repository for arXiv submission and enabling direct download of model checkpoints, along with updates to README/docs, adjustments to model loading, and cleanup of unnecessary files. No explicit bugs reported for this month; the work centered on packaging, reproducibility, and documentation to support compliant submissions and easier distribution.
January 2026
September 2025
1 Commits
Sep 1, 2025September 2025 (IBM/materials): Delivered a stability-focused bug fix for POSEGNN by reverting initialization to the CPU default and reintroducing the torch_scatter import, eliminating GPU auto-detection issues and ensuring consistent graph processing workflows. This work reduces runtime errors, improves reproducibility across environments, and solidifies baseline reliability for production runs.
September 2025
1 Commits
Sep 1, 2025September 2025 (IBM/materials): Delivered a stability-focused bug fix for POSEGNN by reverting initialization to the CPU default and reintroducing the torch_scatter import, eliminating GPU auto-detection issues and ensuring consistent graph processing workflows. This work reduces runtime errors, improves reproducibility across environments, and solidifies baseline reliability for production runs.
August 2025
2 Commits • 2 Features
Aug 1, 2025Monthly performance summary for 2025-08 focused on IBM/materials. Key deliverables include a new Raman spectra prediction capability driven by crystallographic information and a dependency upgrade pass to improve compatibility and potential performance. No major bugs were fixed in this period. Overall impact: expanded modeling capabilities within the materials framework, improved maintainability, and a stronger foundation for scalable Raman predictions.
2 Commits • 2 Features
Aug 1, 2025Monthly performance summary for 2025-08 focused on IBM/materials. Key deliverables include a new Raman spectra prediction capability driven by crystallographic information and a dependency upgrade pass to improve compatibility and potential performance. No major bugs were fixed in this period. Overall impact: expanded modeling capabilities within the materials framework, improved maintainability, and a stronger foundation for scalable Raman predictions.
August 2025
March 2025
13 Commits • 2 Features
Mar 1, 2025March 2025—IBM/materials: Delivered end-to-end POS-EGNN enhancements, including core model and calculator with invariant embeddings and a ready-made sample dataset (3BPA.xyz). Expanded documentation, tutorials, and assets to accelerate adoption; completed end-to-end notebook demonstrations with stored outputs. Improved onboarding, traceability, and maintainability, delivering clear business value through faster material property insights and reproducible workflows.
March 2025
13 Commits • 2 Features
Mar 1, 2025March 2025—IBM/materials: Delivered end-to-end POS-EGNN enhancements, including core model and calculator with invariant embeddings and a ready-made sample dataset (3BPA.xyz). Expanded documentation, tutorials, and assets to accelerate adoption; completed end-to-end notebook demonstrations with stored outputs. Improved onboarding, traceability, and maintainability, delivering clear business value through faster material property insights and reproducible workflows.
Activity
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Quality Metrics
Correctness97.6%
Maintainability91.8%
Architecture94.2%
Performance90.6%
AI Usage77.6%
Skills & Technologies
Programming Languages
MarkdownPythonSVG
Technical Skills
Data ScienceDeep LearningGraph Neural NetworksJupyter NotebookJupyter NotebooksMachine LearningPyTorchPythonPython package managementPython programmingScientific ComputingUI designdata analysisdata modelingdata processing
Repositories Contributed To
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