ClementiGroup/mlcg
Jul 2025 – Sep 2025
3 Months active
Languages Used
C++Python
Technical Skills
Deep LearningGraph Neural NetworksMachine LearningMolecular ModelingPyTorchSpherical Harmonics
Alessandro Caruso
PROFILE
Alessandro Caruso
Over three months, Antonio Caruso developed and refined deep learning models for the ClementiGroup/mlcg repository, focusing on geometry-aware molecular modeling. He implemented the core So3krates model in PyTorch, integrating interaction layers, attention mechanisms, and spherical harmonic transforms to enable robust message passing and accurate geometry-based filtering. Antonio introduced the ScaleShiftMACE variant, adding flexible energy prediction through scale and shift transformations, and improved code clarity with targeted refactoring. His work included major model refactoring for configurability, enhanced integration with graph neural network workflows, and improved handling of edge and node features, demonstrating strong skills in Python, PyTorch, and model architecture.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
7Total
Bugs
2
Commits
7
Features
3
Lines of code
1,593
Activity Months3
Your Network
9 peopleShared Repositories
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Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ClementiGroup/mlcg focusing on key deliverables, stability, and business value. Overview: Delivered a major refactor of MACE and ScaleShiftMACE to enhance configurability and integration with downstream graph-model workflows. The work emphasizes flexible processing pathways, clearer configuration surfaces, and robust readout/interactions handling across edge and node features.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ClementiGroup/mlcg focusing on key deliverables, stability, and business value. Overview: Delivered a major refactor of MACE and ScaleShiftMACE to enhance configurability and integration with downstream graph-model workflows. The work emphasizes flexible processing pathways, clearer configuration surfaces, and robust readout/interactions handling across edge and node features.
September 2025
August 2025
3 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focused on delivering flexible energy prediction capabilities within the MACE architecture and improving code clarity for long-term maintainability.
August 2025
3 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focused on delivering flexible energy prediction capabilities within the MACE architecture and improving code clarity for long-term maintainability.
July 2025
3 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for ClementiGroup/mlcg focused on delivering core So3krates capabilities and stabilizing message passing in geometry-aware modules. Delivered a PyTorch-based So3krates model with core components (interaction layers, attention mechanisms, spherical harmonic transforms) plus robust utilities and default hyperparameters, including validation of hidden channel dimensions. Implemented a critical fix for sender-receiver inversion in So3kratesInteraction and ConvAttention to ensure correct message passing and accurate geometry-based filtering/attention. These efforts improve training stability, inference accuracy, and overall reliability, enabling faster experimentation and deployable geometry-aware learning. Technologies demonstrated include PyTorch, graph neural networks, spherical harmonics transforms, attention mechanisms, and robust data handling.
3 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for ClementiGroup/mlcg focused on delivering core So3krates capabilities and stabilizing message passing in geometry-aware modules. Delivered a PyTorch-based So3krates model with core components (interaction layers, attention mechanisms, spherical harmonic transforms) plus robust utilities and default hyperparameters, including validation of hidden channel dimensions. Implemented a critical fix for sender-receiver inversion in So3kratesInteraction and ConvAttention to ensure correct message passing and accurate geometry-based filtering/attention. These efforts improve training stability, inference accuracy, and overall reliability, enabling faster experimentation and deployable geometry-aware learning. Technologies demonstrated include PyTorch, graph neural networks, spherical harmonics transforms, attention mechanisms, and robust data handling.
July 2025
Activity
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Quality Metrics
Correctness88.6%
Maintainability85.6%
Architecture88.6%
Performance77.2%
AI Usage22.8%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Code OrganizationDeep LearningGeometric Deep LearningGraph Neural NetworksMachine LearningModel RefactoringMolecular ModelingPyTorchRefactoringSpherical Harmonics
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