Engineering topics
Small, David W.
Publications and source records attributed to Small, David W..
Characterization of a Molecule Partially Confined at the Pore Mouth of a Zeotype
Abstract We investigate the interaction between a molecule and a pore mouth—a critical step in adsorption processes—by characterizing the conformation of a macrocyclic calix[4]arene‐Ti IV complex, which is grafted on the external surface of a zeotype (*‐SVY). X‐ray absorption and 13 C{ 1 H} CPMAS NMR spectroscopies independently detect a unique conformation of this complex when it is grafted at crystallographically equivalent locations that lie at the interface of 7 Å hemispherical microporous cavities and the external surface. Electronic structure calculations support the presence of this unique conformation, and suggest that it is brought about by a specific orientation of the macrocycle that maximizes non‐covalent interactions between calix[4]arene upper‐rim tert ‐butyl substituents and the microporous‐cavity walls. Our comparative study provides a rare “snapshot” of a molecule partially confined at a pore mouth, an essential intermediate for adsorption into micropores, and demonstrates how surrounding environment controls this confinement in a sensitive fashion.
Characterization of a Molecule Partially Confined at the Pore Mouth of a Zeotype
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Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations
Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present, they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new machine learning approach called "BpopNN" for obtaining efficient approximations to DFT PESs. Conceptually, the methodology is based on approaching the true DFT energy as a function of electron populations on atoms; in practice, this is realized with available density functionals and constrained DFT (CDFT). Additionally, the new approach creates approximations to this function with neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on LinHn clusters.