Leo Beck avatar

Leo Beck

Computational Materials Scientist

Graduate Researcher @ Heinz Interfaces Lab

About me

View CVView Resume
  • I am a Materials Science & Engineering PhD student at CU Boulder. My research is focused on using molecular dynamics and machine learning to predict structural features of hybrid organic inorganic semiconducting materials.
  • I worked at the Air Force Research Lab (AFRL) as a High Performance Computing researcher Summer of 2024, focused on calculating shear properties of MXenes using molecular dynamics.
  • I enjoy music, hiking, climbing, and geography games in my free time.

    • Technical Toolkit

    Methods I use to build computational materials workflows, from crystal structures and molecular simulations to machine learning models.

    Simulation

    Building, running, and analyzing atomistic models for materials systems.

    LAMMPSNAMDInterface Force FieldHPCbashSlurmmolecular dynamics

    Machine Learning

    Turning small, structured materials datasets into validated predictive models.

    PythonXGBoostfeature extractionSMOTEscikit-learnPandas

    Scientific Computing

    Writing numerical workflows for simulation, optimization, and high-performance analysis.

    scientific computingnumerical PDEsconvex optimizationnumerical optimizationparallel C++MATLAB

    Experimental Context

    Connecting computational work to fabrication, characterization, and collaborator needs.

    photolithographydevice fabricationelectrical characterizationorganic memristors

    Projects

    Current Projects

    Predicting Stability and Structure of Hybrid Organic Inorganic Perovskites

    Predicting Stability and Structure of Hybrid Organic Inorganic Perovskites

    Using Molecular Dynamics to calculate formation energies and structural features.

    LAMMPSPythonHPCbashMachine Learning
    Crystal feature extraction and dimensionality prediction in hybrid metal halides

    Crystal feature extraction and dimensionality prediction in hybrid metal halides

    Using the HybriD3 and Cambridge Structural databases to build ML models to predict hybrid metal halide crystal features.

    PythonFeature ExtractionXGBoostMachine Learning

    Past Projects

    Fabrication of Polypyrrole Electrochemical Memristors

    Fabrication of Polypyrrole Electrochemical Memristors

    Fabricated 3-terminal thin-film organic memristors, then performed electrical characterization.

    PhotolithographyChemical EtchingDevice Fabrication
    Enhancing Dimensionality Prediction in Hybrid Metal Halides

    Enhancing Dimensionality Prediction in Hybrid Metal Halides

    Used the HybriD3 database to build machine learning models to predict hybrid metal halide dimensionality.

    PythonFeature ExtractionXGBoostMachine Learning
    Quantifying MXene Interfacial Properties

    Quantifying MXene Interfacial Properties

    Used NAMD to simulate dopamine binding on the surface of Ti3C2 MXenes under various surface conditions.

    PythonMXenesNAMDbashLAMMPS
    Roadmap: Computational Studies of MXenes

    Roadmap: Computational Studies of MXenes

    Contributed to computational section of roadmaps paper.

    Molecular DynamicsDFTMXenes

    Get In Touch

    I'm always interested in new opportunities and collaborations. Feel free to reach out!

    Contact Information

    Location

    Boulder, CO

    Connect With Me

    LinkedIn
    GitHub
    ORCiD
    ResearchGate

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