Curriculum Vitae

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Research Profile

Computational materials scientist specializing in automated high-throughput workflows and ML-driven materials discovery. Experience in using 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

  • 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

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

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