Independent Research - ML-Assisted Parameterization and LLM-AI Workflow
Fine Tuning
During independent research in force field development, I utilized ML-assisted parameterization involving Gaussian Process Regression (GPR) to optimize parameters against quantum mechanical reference data. My responsibilities included integrating labeled data and utilizing LLM-assisted workflows to improve accuracy and efficiency. This work directly contributed to the creation and refinement of AI models in computational chemistry settings.
• Used GPR for parameter labeling and optimization.
• Integrated LLM-based AI workflows for code and parameter selection.
• Generated labeled QM datasets using ORCA and Gaussian.
• Prepared labeled training data for AI-driven force field models.
During independent research in force field development, I utilized ML-assisted parameterization involving Gaussian Process Regression (GPR) to optimize parameters against quantum mechanical reference data. My responsibilities included integrating labeled data and utilizing LLM-assisted workflows to improve accuracy and efficiency. This work directly contributed to the creation and refinement of AI models in computational chemistry settings. • Used GPR for parameter labeling and optimization. • Integrated LLM-based AI workflows for code and parameter selection. • Generated labeled QM datasets using ORCA and Gaussian. • Prepared labeled training data for AI-driven force field models.
2024 - Present