Exploring the intersection of chemistry, data science, and AI models.
I design open-source tools that combine scientific rigor and digital innovation. From molecular discovery to AI-powered assistants, my goal is to help scientists push the boundaries of their research.
I am independant, and always open to collaborations.
- Machine learning for chemistry and molecular discovery
- Photochemistry, photocatalysis and light-driven processes
- Biostimulants and sustainable agriculture through data-driven insights
- Intelligent assistants and productivity tools for science (LLMs, automation, dashboards)
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A chat interface to discuss with local models that maintain persistent memory, based on Local LLM Memorization with automatic update and retrieving of memory.
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A project aiming to enhance interactions with local models by automatically memorizing and summarizing past conversations to generate precise and context-aware prompts.
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Building an automated bibliographic-database system that integrates scientific papers into a searchable graph for advanced literature analysis.
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This project aims to develop an interface for the prediction of chemical reaction yields by combining molecular structure information, experimental conditions and machine learning models.
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A comprehensive database for artificial photosynthesis molecules and photocatalytic systems, with a focus on biomass valorization.
A project aiming to predict the properties, stability, and efficiency of photocompounds, using the homemade dataset Photocompounds Database.
An open-source repository for biostimulants and active compounds from bio-sources.
An AI-powered tool for predicting optimal biostimulant formulations, integrating chemical, biological, and agronomic data for maximum efficacy.
If you're working on projects related to sustainable chemistry, clean energy, or molecular innovation, feel free to reach out. I'd be happy to discuss with like-minded scientists !
