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 with end-to-end training and benchmarking on OC20 and MPTRJ datasets. Using Python, PyTorch, and deep learning techniques, he enabled graph attention-based predictions for energies, forces, and stresses, establishing reproducible baselines for material modeling. Eric also improved experiment reliability by enhancing TensorBoard logging robustness, ensuring stable metric capture across diverse configurations. Additionally, he fixed a broadcasting bug in per-atom MAE and MSE calculations, aligning tensor shapes for accurate metric aggregation. His work demonstrated depth in model architecture, data logging, and metric correctness.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
3Total
Bugs
2
Commits
3
Features
1
Lines of code
5,584
Activity Months3
Your Network
200 peopleWork History
February 2026
1 Commits
Feb 1, 2026February 2026 monthly summary focusing on metric correctness and reliability in FairChem. Fixed a broadcasting bug in per_atom_mae and per_atom_mse that previously produced incorrect 2-D outputs due to an unnecessary unsqueeze operation. This change ensures 1-D metric vectors are returned and accurately aggregated, improving the fidelity of energy-prediction error metrics and the trustworthiness of model evaluation.
1 Commits
Feb 1, 2026February 2026 monthly summary focusing on metric correctness and reliability in FairChem. Fixed a broadcasting bug in per_atom_mae and per_atom_mse that previously produced incorrect 2-D outputs due to an unnecessary unsqueeze operation. This change ensures 1-D metric vectors are returned and accurately aggregated, improving the fidelity of energy-prediction error metrics and the trustworthiness of model evaluation.
February 2026
September 2025
1 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09 focusing on business value and technical achievements for FAIR-Chem/fairchem.
September 2025
1 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09 focusing on business value and technical achievements for FAIR-Chem/fairchem.
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.
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
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Configuration ManagementData LoggingDeep LearningGraph Neural NetworksMachine LearningModel Architecture DesignPyTorchScientific Computingdata sciencemachine learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
facebookresearch/fairchem
Feb 2026 – Feb 2026
1 Month active
Languages Used
Python
Technical Skills
PyTorchdata sciencemachine learning