FAIR-Chem/fairchem
Mar 2025 – Sep 2025
2 Months active
Languages Used
PythonC++
Technical Skills
Data LoggingDeep LearningMachine LearningConfiguration ManagementGraph Neural NetworksModel Architecture Design
Eric Qu
PROFILE
Eric Qu
Eric Qu worked on the FAIR-Chem/fairchem repository, where he developed the EsCaIP interatomic potential model architecture, enabling end-to-end training and benchmarking on OC20 and MPTRJ datasets. Leveraging Python, PyTorch, and graph neural networks, he implemented graph attention mechanisms to predict energies, forces, and stresses for material modeling workflows. Eric also addressed experiment reliability by fixing TensorBoard logging robustness, ensuring stable metric capture across diverse logger configurations and improving traceability during model evaluation. His contributions demonstrated depth in model architecture design and scientific computing, resulting in reproducible benchmarking baselines and more robust data logging for deep learning experiments.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
5,576
Activity Months2
Your Network
184 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09 focusing on business value and technical achievements for FAIR-Chem/fairchem.
1 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09 focusing on business value and technical achievements for FAIR-Chem/fairchem.
September 2025
March 2025
1 Commits
Mar 1, 2025March 2025 — FAIR-Chem/fairchem: Key reliability improvement in experiment logging. Implemented a TensorBoard logging robustness fix to ensure stable metric capture across diverse logger configurations. Specifically, the fix handles non-dictionary logger configurations and defaults to -1 for non-wandb loggers to prevent errors, enhancing overall logging stability during model evaluation.
March 2025
1 Commits
Mar 1, 2025March 2025 — FAIR-Chem/fairchem: Key reliability improvement in experiment logging. Implemented a TensorBoard logging robustness fix to ensure stable metric capture across diverse logger configurations. Specifically, the fix handles non-dictionary logger configurations and defaults to -1 for non-wandb loggers to prevent errors, enhancing overall logging stability during model evaluation.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Configuration ManagementData LoggingDeep LearningGraph Neural NetworksMachine LearningModel Architecture DesignPyTorchScientific Computing
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