Curriculum Vitae

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Research Profile

Computational materials scientist integrating high-throughput simulations and machine learning with self-driving labs for closed-loop materials discovery. Experience in automated DFT workflows, semi-supervised learning for synthesizability, and GNNs for property prediction. Committed to open science and bridging the gap between theory and experiment.

Education

Ph.D. Materials Engineering, Purdue University Aug 2021 - Dec 2025

  • Committee: Dr. Alejandro Strachan, Dr. Arun Mannodi, Dr. Babak Anasori, Dr. Rahim Rahimi
  • Specialization in Computational Science and Engineering

B.S. Materials Science & Applied Physics, Ohio State University Aug 2017 - May 2021

  • Magna Cum Laude
  • Honors Research Distinction

Publications

  • Wyatt, B., Nykiel, K., Desai, R., et al. Atomic-scale design of non-van der Waals layered ceramics by thermal fusion of MXenes. Submitted (2026).

  • Nykiel, K., Wyatt, B., Anasori, B. & Strachan, A. Exploration of Hexagonal, Layered Carbides and Nitrides as Ultra-High Temperature Ceramics. Preprint at https://doi.org/10.48550/arXiv.2508.18455 (2025).

  • Nykiel, K. & Strachan, A. High-throughput density functional theory screening of double transition metal MXene precursors. Sci Data 10, 827 (2023).

  • Chen, C.-C., Appleton, R. J., Mishra, S., Nykiel, K. & Strachan, A. Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning. npj Comput Mater 11, 191 (2025).

  • Lee, B. H., Nykiel, K., Hallberg, A. E., Rider, B. & Strachan, A. Thermodynamic fidelity of generative models for Ising system. Journal of Applied Physics 137, 124901 (2025).

  • Chen, C.-C., Appleton, R. J., Nykiel, K., Mishra, S., Yao, S., & Strachan, A. How accurate is density functional theory at high pressures? Computational Materials Science 247, 113458 (2025).

  • Wyatt, B. C., Thakur, A., Nykiel, K., Hood, Z. D., Adhikari, S. P., Pulley, K. K., Highland, W. J., Strachan, A. & Anasori, B. Design of Atomic Ordering in Mo2Nb2C3Tx MXenes for Hydrogen Evolution Electrocatalysis. Nano Lett. (2023).

Research Experience

Postdoctoral Researcher, Adolf Martens Fellow, Federal Institute for Materials Research and Testing (BAM)

Oct 2026 - Present
Berlin, Germany
Advisor: Prof. Janine George

  • Integrating density functional theory and machine learning into the decision-making of BAM’s MINERVA self-driving lab, in collaboration with Dr. Bastian Rühle, for closed-loop discovery of mixed-metal oxide nanoparticles
  • Developing semi-supervised (positive-unlabeled) synthesizability models that are iteratively refined with experimental data from the self-driving lab
  • Targeting visible-light photocatalysts for reactive oxygen species generation in water treatment, following safe and sustainable by design principles

Graduate Research, Purdue University

Aug 2021 - Dec 2025 Advisor: Dr. Alejandro Strachan

  • Established group infrastructure for high-throughput density functional theory workflows supporting machine-learning driven materials discovery, with over 140,000 completed calculations in the database
  • Implemented automated workflows for geometric relaxation, elastic constant, phonons, electronic bandstructure, convex hull stability, FTIR, SQS, equation of state, and MLIP training
  • Collaborated with experimental researchers to study stability and synthesizability of 2D MXenes, their precursors, and layered ceramics in computation-guided discovery efforts
  • Developed workflows for Quantum ESPRESSO in nanoHUB, with over 200 users and 120,000 simulations
  • Applied graph neural networks and active learning for closed-loop discovery of high-entropy carbides

Technical Skills

Electronic Structure
VASP, Q-Chem, Quantum ESPRESSO

Workflow Automation
atomate2, jobflow, FireWorks, pymatgen

Machine Learning
PyTorch, GNNs, semi-supervised learning, MLIPs, local LLMs

Data Infrastructure
high-throughput workflow orchestration, databases, Docker, Kubernetes

Honors and Awards

Adolf Martens Fellowship Oct 2026

  • Postdoctoral fellowship at the Federal Institute for Materials Research and Testing (BAM), Berlin, Germany

CIGP Fall Symposium Doctoral Talk Award Nov 2025

  • Awarded for the best talk at the Computational Interdisciplinary Graduate Program Fall 2025 Symposium at Purdue University

MaRDA Best Graduate Student Poster Award Mar 2023

  • Awarded for presenting at the Spring 2023 Materials Research Data Alliance Conference

Presentations

  • Oral Presentation, “Accelerated Prediction of Elastic Tensors in High-Entropy Rock-salt Carbides using an Equivariant Graph Neural Network,” Materials Research Society, Dec 2025, Boston, MA.

  • Oral Presentation, “Exploration of Magnetism in Rare Earth and Mn-doped Double Transition Metal MXenes,” Materials Research Society, Dec 2025, Boston, MA.

  • Oral Presentation, “Synthesis of Novel Rare-Earth MXenes Using Density Functional Theory and Optimal Experiment Design,” Materials Research Society, Apr 2025, Seattle, WA.

  • Poster Presentation, “Exploration of Stacked MXenes as Precursors to Ultra-High Temperature Ceramics,” EPW School on Electron-Phonon Physics, Jun 2024, Austin, TX.

  • Oral Presentation, “Exploration of Stacked MXenes as Precursors to Ultra-High Temperature Ceramics,” Materials Research Society, Apr 2024, Seattle, WA.

  • Oral Presentation, “Semi-Supervised Prediction of Double-Transition Metal MXene Stability,” Materials at Purdue Symposium, May 2023, West Lafayette, IN.

  • Oral Presentation, “Semi-Supervised Prediction of Double-Transition Metal MXene Stability,” Materials Research Society, Apr 2023, San Francisco, CA.

  • Oral Presentation, “Semi-Supervised Prediction of Double-Transition Metal MXene Stability,” Materials Research Data Alliance Conference, Mar 2023, Virtual.

Teaching Experience

Graduate Teaching Assistant, MSE 367, Purdue University Jan 2023 - May 2023

  • Taught laboratory sessions covering processing of metals, ceramics, and polymers

  • Led laboratory instruction and coordinated final design projects

Mentoring Experience

Graduate Student Mentor, MNT Collaborative Undergraduate Research Network May 2023 - Sept 2024

  • Mentored three undergraduate researchers in computational analysis projects
  • Used statistical natural language processing and LLMs to analyze trends of expert selection in articles

Graduate Student Mentor, nanoHUB, Purdue University May 2022 - Dec 2022

  • Mentored a student in simulation of the 2D Ising model using Markov Chain Monte Carlo methods
  • Project led to generative model training on Ising trajectories and a co-authored publication