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Abstract Kohn–Sham density functional theory (DFT)‐based searches for hypothetical catalysts are too computationally demanding for wide searches through diverse materials space. Here, the accuracy of computational alchemy schemes on carbides, nitrides, and oxides is assessed. With a single set of reference DFT calculations, computational alchemy approximates adsorbate binding energies (BEs) on a large number of hypothetical catalysts surfaces with negligible computational cost. Analogous to previous studies on metal alloys, computational alchemy predicts adsorbate BEs on rocksalt TiC(111), TiN(100), and TiO(100) materials, which have no bandgap, in close agreement with DFT results (with mean unsigned errors up to 0.33 eV). In contrast, it is found that semiconducting systems such as rutile TiO₂(110), rutile SnO₂(110), and rocksalt ZnO(100) can present more significant challenges. This work identifies these challenges being linked to the density of states at the Fermi level and by adding Pt dopants in the surface layer of TiO₂, it is shown that computational alchemy can become more reliable with non‐transition metal systems. This remedy provides insight that promotes computational alchemy for broad searches for catalyst active sites through materials space beyond transition metal alloys. 

Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms, but the employed global descriptor limits transferability to ensembles of molecules. Many-body expansions (MBEs) should provide a rigorous procedure for size-transferable GDML by training models on fundamental n-body interactions. We developed many-body GDML (mbGDML) force fields for water, acetonitrile, and methanol by training 1-, 2-, and 3-body models on only 1000 MP2/def2-TZVP calculations each. Our mbGDML force field includes intramolecular flexibility and intermolecular interactions, providing that the reference data adequately describe these effects. Energy and force predictions of clusters containing up to 20 molecules are within 0.38 kcal/mol per monomer and 0.06 kcal/(mol Å) per atom of reference supersystem calculations. This deviation partially arises from the restriction of the mbGDML model to 3-body interactions. GAP and SchNet in this MBE framework achieved similar accuracies but occasionally had abnormally high errors up to 17 kcal/mol. NequIP trained on total energies and forces of trimers experienced much larger energy errors (at least 15 kcal/mol) as the number of monomers increased—demonstrating the effectiveness of size transferability with MBEs. Given these approximations, our automated mbGDML training schemes also resulted in fair agreement with reference radial distribution functions (RDFs) of bulk solvents. These results highlight mbGDML as valuable for modeling explicitly solvated systems with quantum-mechanical accuracy. 

Bonding energies play an essential role in describing the relative stability of molecules in chemical space. Therefore, methods employed to search chemical space need to capture the bonding behavior for a wide range of molecules, including radicals. In this work, we investigate the ability of quantum alchemy to capture the bonding behavior of hypothetical chemical compounds, specifically diatomic molecules involving hydrogen with various electronic structures. We evaluate equilibrium bond lengths, ionization energies, and electron affinities of these fundamental systems. We compare and contrast how well manual quantum alchemy calculations, i.e., quantum mechanics calculations in which the nuclear charge is altered, and quantum alchemy approximations using a Taylor series expansion can predict these molecular properties. Our results suggest that while manual quantum alchemy calculations outperform Taylor series approximations, truncations of Taylor series approximations after the second order provide the most accurate Taylor series predictions. Furthermore, these results suggest that trends in quantum alchemy predictions are generally dependent on the predicted property (i.e., equilibrium bond length, ionization energy, or electron affinity). Taken together, this work provides insight into how quantum alchemy predictions using a Taylor series expansion may be applied to future studies of non-singlet systems as well as the challenges that remain open for predicting the bonding behavior of such systems. 

A Diversity Index (DI) was developed to quantify eight minority categories (“Women”, “Non Male/Female”, “Afro-American”, “Hispanic”, “Asian/other ethnicity” “LGBQT”, “Disabilities”, and “First Generation”) in contrast to the standard “White American male”. This index is compared with a Minority Index (MI) based only on the ratio of “Non White American male” to the total of group members, which exhibits poor representation for diversity when teams are heavily conformed by minority representatives. In addition, the Diversity Index includes a tuning parameter to adjust for the impact of multiple diversities on the same individual. The Diversity Index has been calculated for four junior courses on Reactive Process Engineering and four senior capstone courses on Process Control and Process Design during the last three years (2019-21). Each course included at least two semester-long projects for 4-6 member teams. The Diversity Index was used to assess the performance of 69 self-selected teams, performing 37 technical projects and 101 outreach projects total. Assessments included relations with grades, peer-grading, team experience, and scope of activities. The analysis provides a quantitative approach to the impact of diversity on team performance. Reliability on some data is still difficult to validate. This study has relied mainly on the instructor interactions with students. In order to protect the students’ personal information, the proposed Diversity Index outputs a quantitative value without exposing the diversity source, and thus promoting more honest, secure and respectful participation. A new step is in progress to offer a “diversity rewarded” option to motivate students to select team members providing for larger inclusion and diversity.