Maxwell C. Venetos
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Maxwell C. Venetos has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Alexander Rizzolo Epstein
Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, USA
1 shared publication - Evan Walter Clark Spotte-Smith
Carnegie Mellon University
1 shared publication - Kristin Aslaug Persson
University of California Berkeley
1 shared publication - Oxana Andriuc
University of California, Berkeley
1 shared publication
Latest Publications
- Open Access
- 10 February 2023
- Published by American Chemical Society (ACS) in ChemRxiv
Assessing the Accuracy of Density Functional Approximations for Predicting Hydrolysis Reaction Kinetics
Hydrolysis reactions are ubiquitous in biological, environmental, and industrial chemistry. Density functional theory (DFT) is commonly employed to study the kinetics and reaction mechanisms of hydrolysis processes. Here, we present a new dataset, Barrier Heights for HydrOlysis - 36 (BH2O-36), to enable the design of density functional approximations (DFA) and the rational selection of DFAs for applications in aqueous chemistry. BH2O-36 consists of 36 diverse organic and inorganic forward and reverse hydrolysis reactions with reference energy barriers calculated at the CCSD(T)/CBS level. Using BH2O-36, we evaluate 63 DFAs. In terms of mean absolute error (MAE) and mean relative absolute error (MRAE), wB97M-V is the best-performing DFA tested, while MN12-L-D3(BJ) is the best-performing pure (non-hybrid) DFA. Broadly, we find that range-separated hybrid DFAs are necessary to approach chemical accuracy (0.043 eV). Although the best-performing DFAs include a dispersion correction to account for long-range interactions, we find that dispersion corrections do not generally improve MAE or MRAE for this dataset.
- 10 February 2023
- Published by American Chemical Society (ACS) in ChemRxiv
Open AccessAssessing the Accuracy of Density Functional Approximations for Predicting Hydrolysis Reaction Kinetics
Hydrolysis reactions are ubiquitous in biological, environmental, and industrial chemistry. Density functional theory (DFT) is commonly employed to study the kinetics and reaction mechanisms of hydrolysis processes. Here, we present a new dataset, Barrier Heights for HydrOlysis - 36 (BH2O-36), to enable the design of density functional approximations (DFA) and the rational selection of DFAs for applications in aqueous chemistry. BH2O-36 consists of 36 diverse organic and inorganic forward and reverse hydrolysis reactions with reference energy barriers calculated at the CCSD(T)/CBS level. Using BH2O-36, we evaluate 63 DFAs. In terms of mean absolute error (MAE) and mean relative absolute error (MRAE), wB97M-V is the best-performing DFA tested, while MN12-L-D3(BJ) is the best-performing pure (non-hybrid) DFA. Broadly, we find that range-separated hybrid DFAs are necessary to approach chemical accuracy (0.043 eV). Although the best-performing DFAs include a dispersion correction to account for long-range interactions, we find that dispersion corrections do not generally improve MAE or MRAE for this dataset.