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Hello! I am currently a Research Scientist at Google DeepMind.

Previously, I led a Data Science team at Rivian, focusing on ML & analytics for batteries. Prior to Rivian, I spent a little over 4 years at Toyota Research Institute (TRI) as a researcher.

Most of my recent published work is centered on combining machine learning with physics and chemistry to accelerate R&D. Among a few, these two deserve the spotlight as open-source Python libraries:

🚀 github.com/TRI-AMDD/CAMD: an end-to-end autonomous computational platform for closed-loop optimization. It was the Bayesian optimization & workflow engine behind a few papers: 1, 2, 3.

🚀 github.com/TRI-AMDD/piro: a recommendation system that combines physics (of nucleation) with ML-inspired approximations to find feasible synthesis routes for compounds. Check out this paper to learn more.

🤔 My research & intellectual interests these days cover Bayesian & closed-loop optimization methods, physics-informed ML algorithms, and on the materials side predictive synthesis and discovery. I'm also fascinated by network science as a field.

⚡ Fun fact: Looks like I get to update this github account every few years!

Muratahan Aykol's Projects

bayesmark icon bayesmark

Benchmark framework to easily compare Bayesian optimization methods on real machine learning tasks

camd icon camd

Agent-based sequential learning software for materials discovery

cdk icon cdk

The Chemistry Development Kit

custodian icon custodian

A simple, robust and flexible just-in-time job management framework in Python.

libact icon libact

Pool-based active learning in Python

mpsentries icon mpsentries

code, logs and documentation for MP's workflow and builder sentries

mpworks icon mpworks

merges pymatgen, custodian, and FireWorks into a custom workflow for Materials Project

piro icon piro

Software for evaluating pareto-optimal synthesis pathways

pymatgen icon pymatgen

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project.

pymatgen-db icon pymatgen-db

Pymatgen-db provides an addon to the Python Materials Genomics (pymatgen) library (https://pypi.python.org/pypi/pymatgen) that allows the creation of Materials Project-style databases for management of materials data.

pyrank icon pyrank

A python package for rank aggregation methods applied to materials design and selection

qmpy icon qmpy

A suite of computational materials science tools.

skflow icon skflow

Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

structure_view icon structure_view

a chemview wrapper to visualize pymatgen structures in jupyter notebook

tensorflow icon tensorflow

Computation using data flow graphs for scalable machine learning

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