Martin Seifrid
Assistant Professor
- Email: m_seifrid@ncsu.edu
- Office: 3078 Engineering Building I
- Website: https://mse.ncsu.edu/seifrid
Our group designs organic materials with precisely controlled structures and functions through synthesis and processing. To accelerate materials design, we develop self-driving labs – automated experiments guided by machine learning.
We are a multidisciplinary group whose expertise spans materials informatics, machine learning, automation, synthesis, and characterization.
Our current focus is a new class of materials with applications in sensing, energy storage, healthcare, and neuromorphic computing: organic mixed ionic-electronic conductors.
Publications
- Metadata Analysis to Reveal Environmental Effects in Large-Area Molecular Tunneling Junctions
- Dehghan-Toranposhti, S., Martin, A., Du, C., Zmarzlak, R., Thuo, M., & Seifrid, M. (2026, April 3), ChemRxiv. https://doi.org/10.26434/chemrxiv.15001578/v1
- On the Need for Autonomous Science Instruments: A Call to Action
- Abolhasani, M., Ahmadi, M., Baird, S., Berlinguette, C. P., Brown, K. A., Fenning, D., … Yang, C. (2026, February 2), ChemRxiv, Vol. 2. https://doi.org/10.26434/chemrxiv.10001836/v1
- Robust learning from literature data: Model generalizability and uncertainty for predicting conjugated polymer solution conformation
- Dehghan-Toranposhti, S., Alghamdi, M., Smith, A., & Seifrid, M. (2026, February 25), APL Machine Learning. https://doi.org/10.1063/5.0303721
- Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation
- Dehghan-Toranposhti, S., Alghamdi, M., Smith, A., & Seifrid, M. (2025, September 25), ChemRxiv. https://doi.org/10.26434/chemrxiv-2025-mdtsm
- Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation
- Dehghan-Toranposhti, S., Alghamdi, M., Smith, A., & Seifrid, M. (2025, October 7), ChemRxiv. https://doi.org/10.26434/chemrxiv-2025-mdtsm-v2
- Science acceleration and accessibility with self-driving labs
- Canty, R. B., Bennett, J. A., Brown, K. A., Buonassisi, T., Kalinin, S. V., Kitchin, J. R., … Abolhasani, M. (2025). [Review of , ]. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-59231-1
- Chemspyd : an open-source python interface for Chemspeed robotic chemistry and materials platforms
- Seifrid, M., Strieth-Kalthoff, F., Haddadnia, M., Wu, T. C., Alca, E., Bodo, L., … Aspuru-Guzik, A. (2024, January 1), Digital Discovery. https://doi.org/10.1039/d4dd00046c
- Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices
- Seifrid, M., Lo, S., Choi, D., Tom, G., Le, M. L., Li, K., … Aspuru-Guzik, A. (2024, March 25), ChemRxiv. https://doi.org/10.26434/chemrxiv-2024-d20px
- Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices
- Seifrid, M., Lo, S., Choi, D. G., Tom, G., Le, M. L., Li, K., … Aspuru-Guzik, A. (2024, March 31), ChemRxiv. https://doi.org/10.26434/chemrxiv-2024-d20px-v2
- Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices
- Seifrid, M., Lo, S., Choi, D. G., Tom, G., Le, M. L., Li, K., … Aspuru-Guzik, A. (2024, January 1), Journal of Materials Chemistry A, Vol. 5. https://doi.org/10.1039/d4ta01942c
