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
Contributed to the ClementiGroup/mlcg repository by developing and refining deep learning models for molecular and geometric data, focusing on PyTorch-based implementations of So3krates, MACE, and ScaleShiftMACE. Work included building core interaction layers, attention mechanisms, and spherical harmonic transforms, as well as introducing flexible energy prediction and robust data handling. Enhanced model configurability and maintainability through thoughtful refactoring, improved naming, and validation of hyperparameters. Addressed critical bugs affecting message passing and model stability, and expanded integration options for downstream workflows. Demonstrated expertise in Python, C++, graph neural networks, and geometric deep learning, supporting reliable experimentation and deployment.
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
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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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