John Keith on Muck Rack

John Keith

Pittsburgh
As seen in: Medium, AIChE Journal, Color Research & Application, Wiley, Wiley Online Library, Bandcamp Daily, Journal of Automated Methods and Management in Chemistry, Liverpool Echo, Royal Society of Chemistry, Boston Magazine and
R.K.M. Fac Fellow in Energy, Assoc. Prof. at @ChemePitt. Trying to atomistically model everything and break bad habits. (he/his/him) ow.ly/LPdS50DMrhT
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Articles

A Comprehensive Benchmark Database of Per- and Polyfluoroalkyl Substance Properties from Quantum Mechanical Methods Click to copy article link Article link copied!

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By Melissa Marciesky, Scott Simpson, John Keith
| dx.doi.org
1. Introduction Click to copy section linkSection link copied! Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals with fully (perfluorinated) or partially (polyfluorinated) carbon chains. (1) One study identified more than 60 PFAS use categories and over 200 specific applications, including apparel, food production, fire-fighting foam, musical instruments, glass, and leather. (2) Their widespread use and persistence have led to global contamination.
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Front Cover: Vibrational Partition Functions from Bond Order and Populations Relationships (ChemPhysChem 14/2025)

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By Barbaro Zulueta, John Keith
| Wiley Online Library Verified
Graphical Abstract The Front Cover illustrates a new computational method that partitions vibrational thermal energy across individual chemical bonds. This approach offers a novel alternative to traditional statistical mechanics based on vibrational partition functions, enabling localized insight into thermodynamic behavior across chemical space and advancing the interpretation of vibrational contributions to energy. More information can be found in the Research Article by B. Zulueta and J. A.
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A focus on delocalization error poisoning the density-functional many-body expansion

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By Barbaro Zulueta, John Keith
| Royal Society of Chemistry Verified
A focus on delocalization error poisoning the density-functional many-body expansion Broderick and Herbert's article (D. R. Broderick and J. M. Herbert, Chem. Sci., 2024, 15, 19893–19906, https://doi.org/10.1039/D4SC05955G) explores an open concern about using energies from density-functional approximations when developing force fields and machine learning potentials for large-scale simulations.
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