Sfoglia categorie
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Italiano
$
USD
Danish Ilyas
@danisheikh990
DFT Calculations, GCMC Simulations, Machine Learning for Materials
Pakistan
Inglese, Urdu
Chi sono
Welcome! I’m Danish Ilyas, a Computational Chemist and Materials Scientist. I deliver publication-ready DFT calculations in Gaussian, GCMC adsorption simulations in RASPA2, and ML models for materials/chemical data. Tools: Zeo++, PoreBlazer, VMD, Python (scikit-learn, XGBoost, TPOT). Clear plots, organized files, and a short report included. Message me with your goal, structures, and deadline.
Competenze
Danish Ilyas
offline •
Tempo di risposta medio: 1 ora
Consulta i miei servizi
Machine learning
I will build a python ml model with shap
Portfolio
Esperienza lavorativa
Open-Source Developer, ML for Chemical Property Prediction (BDE Prediction ML)
GitHub • Lavoratore autonomo
Feb 2026 - Present • 3 mos
Developed an open-source, modular Python pipeline to predict C–X bond dissociation energies (BDE) using AutoML (TPOT), SHAP interpretability, and external validation. Implemented reproducible training/validation scripts, saved artifacts (models/scalers), and documented end-to-end usage for deployment. Reported strong predictive performance (test R² ~0.94, external validation R² ~0.94+). Project includes structured src/ modules, scripts, notebooks, and results outputs.
Python Scripting, Research Workflow Automation
Freelancing Career • Freelance
May 2025 - Present • 1 yr
Python automation for computational chemistry and materials research workflows: batch job setup, data cleaning, result parsing, plotting, and report-ready outputs. Build reproducible scripts/notebooks for DFT/GCMC/ML pipelines. Currently exploring autonomous research frameworks (OpenClaw, AutoResearchClaw) for literature triage, structured notes, and workflow orchestration.
Computational Chemistry Researcher (MSc Thesis, DFT, GCMC, ML)
Educational Engagement • Lavoratore autonomo
Aug 2024 - Present • 1 yr 9 mos
Defended MSc thesis on screening Metal–Organic Frameworks (MOFs) for CO2/H2 separation using multiscale modeling. Performed DFT studies (Gaussian) and GCMC adsorption simulations (RASPA2), including pore/structure analysis (Zeo++, PoreBlazer, VMD). Built Python workflows for data processing, visualization, and ML-based property prediction to support high-throughput screening and publication-ready reporting.
