FAIR-Chem/fairchem
Oct 2024 – Aug 2025
2 Months active
Languages Used
JinjaPython
Technical Skills
Code RefactoringDeep LearningMachine LearningPyTorchTestingData Visualization
Brandon Wood
PROFILE
Brandon Wood
During their work on FAIR-Chem/fairchem, Ben Wood developed a registry-based loss and evaluation metrics system, introducing RMSE and per-atom MAE/MSE to support more accurate and extensible model evaluation. He refactored metric calculations into a modular, standardized pipeline using Python and PyTorch, improving correctness and enabling future metric expansion. Ben also delivered an automated diatomic potential energy curve plotting script that computes and visualizes relative energies for diatomic molecules using a machine-learned interatomic potential model. His contributions emphasized code refactoring, data visualization, and scientific computing, resulting in more reproducible workflows and streamlined analysis for machine learning-driven chemistry research.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
891
Activity Months2
Your Network
2240 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for FAIR-Chem/fairchem: Delivered the Diatomic Potential Energy Curve Plotting Script, enabling automated computation of relative energies for diatomic molecules across distance ranges using an MLIP model, plus generation of per-pair and aggregate plots. All outputs (results and plots) are saved to a target directory, enabling reproducible analysis and streamlined workflows. No major bugs were reported for this feature. Overall impact: accelerates PES analysis, improves data visualization and model evaluation, and enhances reproducibility for diatomic energy studies. Technologies demonstrated: Python scripting, MLIP model integration, data visualization, plotting automation, and test coverage (including diatomic tests).
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for FAIR-Chem/fairchem: Delivered the Diatomic Potential Energy Curve Plotting Script, enabling automated computation of relative energies for diatomic molecules across distance ranges using an MLIP model, plus generation of per-pair and aggregate plots. All outputs (results and plots) are saved to a target directory, enabling reproducible analysis and streamlined workflows. No major bugs were reported for this feature. Overall impact: accelerates PES analysis, improves data visualization and model evaluation, and enhances reproducibility for diatomic energy studies. Technologies demonstrated: Python scripting, MLIP model integration, data visualization, plotting automation, and test coverage (including diatomic tests).
August 2025
October 2024
1 Commits • 1 Features
Oct 1, 2024Month: 2024-10. Delivered a registry-based Loss and Evaluation Metrics System in FAIR-Chem/fairchem, adding RMSE and per-atom MAE/MSE. Refactored metric calculations into a standardized, modular pipeline to improve correctness and extensibility. Fixed related correctness issues in metrics and aligned workflows with tests/docs. Business impact: more accurate, extensible evaluation tools enabling faster metric iteration and reliable model comparisons; technical highlights include registry design, metric standardization, and traceable commits (commit f490f6ce75d03ee905c6610998f083840c286f0c).
October 2024
1 Commits • 1 Features
Oct 1, 2024Month: 2024-10. Delivered a registry-based Loss and Evaluation Metrics System in FAIR-Chem/fairchem, adding RMSE and per-atom MAE/MSE. Refactored metric calculations into a standardized, modular pipeline to improve correctness and extensibility. Fixed related correctness issues in metrics and aligned workflows with tests/docs. Business impact: more accurate, extensible evaluation tools enabling faster metric iteration and reliable model comparisons; technical highlights include registry design, metric standardization, and traceable commits (commit f490f6ce75d03ee905c6610998f083840c286f0c).
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability85.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JinjaPython
Technical Skills
Code RefactoringData VisualizationDeep LearningMachine LearningPyTorchPython ScriptingScientific ComputingTesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline