Scientific computing and data science.

(And sometimes both together)

Last updated: 2022.08.20

I'm a Ph.D. candidate in the HackingMaterials group at LBL. I use computers to help find and design new materials; I design and deploy machine learning pipelines, run quantum-mechanical simulations, and develop open source software to help automate these processes. I also work alongside the Materials Project, an online resarch effort with over 100,000 registered users.

Broad research interests

Formal education


  1. Graduate Student Research Assistant @ LBNL. Using data-mining to elucidate structure-property relationships and accelerate predictions of material properties. Running many thousands of density functional theory (DFT) calculations to evaluate candidate thermoelectrics, communicating results to experimental collaborators. Writing open-source software packages for data mining materials properties and running massively parallel calculations on supercomputers. (2017 - present)

  2. Scientific Software Engineering Consultant @ Toyota Research Institute - Advanced Materials Design and Discovery. (Contracted thru HireArt) Software for predicting Li-ion battery cycling characteristics (lifetimes) with machine learning. (2020 - present)

  3. Consultant @ MaterialsQM Consulting. High-throughput synthesis pathway screening using density functional theory and combinatorics. Communicating with clients, preparing reports, and helping guide discovery of novel semiconductor materials. (2018 - 2019)

  4. Undergraduate Student Research Assistant @ LBNL. Remote position. Wrote a black-box Bayesian optimization (adaptive design) package for use with the workflow software FireWorks. Incorporated several machine learning algorithms as optimization engines, and tested the performance on two example use cases in materials science. (2016 - 2017)

  5. Principal Web Developer @ RYE Limousine, Inc. Remote position. Designed and deployed corporate website serving hundreds of customers per month for limousine service using Wordpress and LimoAnywhere. Website included live chat between RYE employees and customers and ability for customers to interface with remote scheduling system. (2018)

  6. Howard Hughes Medical Institute Undergraduate Researcher @ UCLA. Studied on-chip microscopy at the Ozcan Lab. Investigated techniques for rapidly polymerizing nanolenses inside mobile microscopes to identify nanoparticles (such as viruses). (2015 - 2016)

  7. Lead App Designer @ UCLA Dept. of Anesthesiology Mobile App Team. Lead UX design for a mobile application for perioperative/anesthetic care for UCLA Health. Worked alongside the UCLA Health Center to develop a comprehensive program for wireless bioinformatics. (2015 - 2016)

Peer-reviewed Papers and Books


  1. Dagdelen, J., Dunn, A.. “Algorithms for Materials Discovery” in Accelerated Materials Discovery. De Gruyter. Chapter, Book

  2. Walker, N., Trewartha, A., Huo, H., Lee, S., Cruse, K., Dagdelen, J., Dunn, A., Persson, K., Ceder, G., Jain, Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science Patterns. 3, 4 (2022)

  3. Huo, H., Bartel, C., He, T., Trewartha, A., Dunn, A., Ouyang, B., Jain, A., Ceder, G. “Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions. Chemistry of Materials. 34, 16 (2022)


  1. Dunn, A., Wang, Q., Ganose, A., Dopp, D., Jain, A. Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm npj Comput. Mater. 6, 138 (2020)

  2. Dylla, M. Dunn, A. Anand, S., Jain, A., Snyder, G. J. Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials Research 2020, 6375171, (2020)

  3. Bartel, C. J., Trewartha, A., Wang, Q., Dunn, A., Jain, A., Ceder, G. A critical examination of compound stability predictions from machine-learned formation energies. npj Comput. Mater. 6, 97 (2020)

  4. Ricci, F., Dunn, A., Jain, A., Rignanese, G. M., Hautier, G. Gapped metals as thermoelectric materials revealed by high-throughput screening J. Mater. Chem. A 8, 17579-17594 (2020)

  5. Pohls, J-H., Chanakian, S., Park, J., Ganose, M., Dunn, A., Friesen, N., Bhattacharya, A., Hogan, B., Bux, S., Jain, A., Mar, A., Zevalkink, A. Experimental validation of high thermoelectric performance in RECuZnP2 predicted by high-throughput DFT calculationsi. Materials Horizons 8, 209-215 (2020)


  1. Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong Z., Kononova, O., Persson, K.A., Ceder, G.,& Jain, A. Unsupervised word embeddings capture latent knowledge from materials science literature Nature 571, 95-98 (2019)

  2. Dunn, A., Brenneck, J., Jain, A. Rocketsled: a software library for optimizing high-throughput computational searches. J. Phys. Mater. 2, 034002 (2019).


  1. Ward, L., Dunn, A., Faghaninia, A., Zimmermann, N. E. R., Bajaj, S., Wang, Q., Montoya, J. H., Chen, J., Bystrom, K., Dylla, M., Chard, K., Asta, M., Persson, K., Snyder, G. J., Foster, I., Jain, A. Matminer: An open source toolkit for materials data mining. Comput. Mater. Sci. 152, 60-69 (2018).


  1. Dunn, A. “Machine Learning with Matminer” at Materials Project Workshop 2021, Remote. August 8, 2021

  2. Dunn, A., Jain, A. “Software tools for Accelerating Materials Discovery with Machine Learning” at Foundational and Applied Data Science for Molecular and Material Science Engineering (Lehigh I-DISC Institute for Data, Intelligent Systems, and Computation), Bethlehem, Pennsylvania. May 23, 2019.

  3. Dunn, A., Wang, Q., Ganose, A., Faghaninia, A., Jain, A. “An Automatic Materials Science Machine Learning Tool for Benchmarking and Prediction” at AI-based Investigation of Material Properties (TMS 2019), San Antonio, Texas. March 12, 2019

  4. Dunn A., Faghaninia, A. “Matminer: Data Mining for Materials Science” at Materials Project Workshop 2018, Berkeley, California. August 10, 2018

  5. Dunn A., Bajaj, S., Jain, A. “Automatic Optimization Algorithms for Maximum-Throughput Materials Design and Discovery” at Science Undergraduate Laboratory Internship Program, Berkeley, California. August 5, 2016

  6. Dunn, A., Ray, A., Daloglu, M.U., Ozcan, A. “The Development of Polymer-based Nanolenses Towards Enhanced Nanoparticle Imaging” at UCLA HHMI Day, Los Angeles, California. May 31, 2016

  7. Ganose A., Dunn, A. “Data Mining for Materials” at Materials Project Workshop 2019, Berkeley, California. August 2, 2019

Leadership, Memberships, and Awards

  1. NERSC User Group Executive Committee - Elected member of executive committee which administrates supercomputing policy at NERSC. (2019 - 2022)
  2. UCLA Chemical and Biomolecular Engineering Alumni Association - VP of Technology (2021 - present)
  3. UC Berkeley Graduate Data Visualization Contest Overall Winner - Won schoolwide competition by creating interactive website for graduate financial data. (2019)
  4. Computational Materials Science at Berkeley - Co-Founder, officer (2018)
  5. Magna Cum Laude - UCLA (2017)
  6. Tau Beta Pi CA Epsilon - Distinguished member (2016)
  7. Edward and Doris Rhoad Scholarship - Selected recipient (2014)
  8. National AP Scholar with Distinction - (2014)
  9. Regent’s and Chancellor’s Scholarship at Berkeley - Selected but declined (2014)




Data science libraries

Other software and frameworks (ordered by experience)

Open source software