History¶
Experience¶
Postdoctoral Researcher (11/2023 – Present)
Developed a machine learning force field methodology that integrates electronic structure information to accurately model coupled electronic and ionic charge transport, enabling large-scale molecular dynamics simulations of solid-state lithium-ion batteries. Work carried out within the SolBat project.
Postdoctoral Researcher (12/2022 – 11/2023)
Built a machine learning force field framework(ACE+Q, based on the Atomic Cluster Expansion) to capture long-range charge transfer and electrostatics; implemented in TensorFlow within the pacemaker Python package; matched the accuracy of state-of-the-art approaches.
Scientist (12/2022 – 11/2023)
ACEworks GmbH, Bochum
Extended the PhD-developed methodology to handle multi-component (binary) materials; delivered production-ready code to Materials Design Inc..
Education¶
PhD in Physics (12/2017 – 12/2022)
Doctoral student as a part of the International Max Planck Research School for Interface Controlled Materials for Energy Conversion and working in the group Atomistic Modelling and Simulation . Designed and implemented a machine learning force field framework for non-collinear spin-lattice dynamics using TensorFlow (magnetic ACE); integrated within the pacemaker Python package; achieved 2× accuracy improvement over prior models and enabled large-scale (4,000 atoms) simulations on the nanosecond timescale; built an ab initio magnetic structure training database comprising 70,000 entries.
MSc in Condensed Matter Physics (10/2015 – 10/2017)
Conducted first-principles DFT simulations of CO and CO₂ adsorption on metal-organic frameworks; quantified binding energies and vibrational properties to support experimental spectroscopic data.
BSc in Physics (10/2012 – 09/2015)
Developed a Fortran-based tight-binding framework to compute electronic band structure of 2D materials under applied magnetic fields.