Nicholas R. David

#2001 Herbert H. Dow

2300 Hayward Street

Ann Arbor, MI 48109

I am a second year Ph.D. student studying Materials Science Engineering and Scientific Computing at the University of Michigan.
My research focuses on data-driven materials discovery though the use of machine learning, natural language processing, and bayesian statistics.
In particular, I’m interested in developing new predictive synthesis models that not only gauge the `synthesizability’ of a material, but also yield the recipes to do so.
I received my B.S. in Materials Science Engineering, with an additional major in Applied Physics, from Carnegie Mellon University in Pittsburgh, PA.

news

Apr 04, 2024	I am awarded the NSF Graduate Research Fellowship! Total award amount: $159,000 / 3 years
Nov 13, 2023	I present graduate admissions research at the MIDAS Data Science Summit!
Aug 01, 2022	I start my Ph.D. in Materials Science Engineering and Scientific Computing at the University of Michigan.

latest posts

Jan 28, 2024	Public facing dynamic visualizations
Jun 06, 2023	Configuring SSH key authentication on a linux server
Jun 06, 2023	Connecting to a Docker Container via SSH

selected publications

The promise and pitfalls of AI for molecular and materials synthesis

Nicholas David , Wenhao Sun , and Connor W. Coley

Nature Computational Science, 2023

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As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its progress. Despite hidden variables and ‘unknown unknowns’ in datasets that may impede the realization of a digital twin for the laboratory flask, there are many opportunities to leverage AI and large datasets to advance synthesis science.

@article{david_promise_2023,
  url = {https://doi.org/10.1038/s43588-023-00446-x},
  doi = {10.1038/s43588-023-00446-x},
  title = {The promise and pitfalls of AI for molecular and materials synthesis},
  author = {David, Nicholas and Sun, Wenhao and Coley, Connor W.},
  journal = {Nature Computational Science},
  volume = {3},
  number = {5},
  pages = {362--364},
  year = {2023},
  date = {2023-05-01},
  publisher = {American Association of Physics Teachers},
  dimensions = {true}
}