scholarly journals Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis

2018 ◽  
Vol 14 (9) ◽  
pp. 4772-4779 ◽  
Author(s):  
Matthew Welborn ◽  
Lixue Cheng ◽  
Thomas F. Miller
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Molecular Orbital ◽  
Orbital Basis

scholarly journals Analytical gradients for molecular-orbital-based machine learning

2021 ◽  
Vol 154 (12) ◽  
pp. 124120
Author(s):  
Sebastian J. R. Lee ◽  
Tamara Husch ◽  
Feizhi Ding ◽  
Thomas F. Miller
Keyword(s):  
Machine Learning ◽  
Molecular Orbital ◽  
Analytical Gradients

The effect of ionic lattices on the electronic structures of polyatomic ions. I. The triiodide ion

1966 ◽  
Vol 19 (9) ◽  
pp. 1567 ◽  
Author(s):  
RD Brown ◽  
EK Nunn
Keyword(s):  
Electronic Structure ◽  
Molecular Orbital ◽  
Electron Energy ◽  
Electronic Structures ◽  
Valence Electron ◽  
Bond Orders ◽  
Bond Lengths ◽  
Molecular Orbital Study ◽  
Crystalline Environment ◽  
Asymmetric Stretch

A VESCF molecular-orbital study of the electronic structure of the triiodide anion in its crystalline environment in caesium triiodide and in tetraphenylarsonium triiodide reveals the effect of the lattices upon the electronic structures. The calculated total valence-electron energy as a function of the position of the central iodine nucleus provides an understanding of the observed geometries of the anion in the two crystals. The energy plot also implies that the asymmetric stretch of the triiodide is strongly anharmonic in the crystal. A satisfactory correlation exists between observed iodine : iodine bond lengths and computed bond orders.

A semi-empirical molecular orbital scheme to study electron transfer in iron–sulphur proteins

2005 ◽  
Vol 33 (1) ◽  
pp. 20-21 ◽  
Author(s):  
M. Sundararajan ◽  
J.P. McNamara ◽  
M. Mohr ◽  
I.H. Hillier ◽  
H. Wang
Keyword(s):  
Electron Transfer ◽  
Electronic Structure ◽  
Molecular Orbital ◽  
Parametric Method ◽  
Orbital Method ◽  
Molecular Orbital Method ◽  
Semi Empirical

We describe the use of the semi-empirical molecular orbital method PM3 (parametric method 3) to study the electronic structure of iron–sulphur proteins. We first develop appropriate parameters to describe models of the redox site of rubredoxins, followed by some preliminary calculations of multinuclear iron systems of relevance to hydrogenases.

scholarly journals A Study of Interfacial Electronic Structure at the CuPc/CsPbI2Br Interface

Crystals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 547
Author(s):  
Zengguang Tang ◽  
Liujiang Zhang ◽  
Zhenhuang Su ◽  
Zhen Wang ◽  
Li Chen ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Molecular Orbital ◽  
Photoelectron Spectroscopy ◽  
High Performance ◽  
Energy Gap ◽  
Hole Transport ◽  
Hole Injection ◽  
Band Bending ◽  
Valance Band Maximum ◽  
Interfacial Electronic Structure

In this article, CsPbI2Br perovskite thin films were spin-coated on FTO, on which CuPc was deposited by thermal evaporation. The electronic structure at the CsPbI2Br/CuPc interface was examined during the CuPc deposition by in situ X-ray photoelectron spectroscopy (XPS) and ultraviolet photoelectron spectroscopy (UPS) measurements. No downward band bending was resolved at the CsPbI2Br side, whereas there is ~0.23 eV upward band bending as well as a dipole of ~0.08 eV identified at the molecular side. Although the hole injection barrier as indicated by the energy gap from CsPbI2Br valance band maximum (VBM) to CuPc highest occupied molecular orbital (HOMO) was estimated to be ~0.26 eV, favoring hole extraction from CsPbI2Br to CuPc, the electron blocking barrier of ~0.04 eV as indicated by the offset between CsPbI2Br conduction band minimum (CBM) and CuPc lowest unoccupied molecular orbital (LUMO) is too small to efficiently block electron transfer. Therefore, the present experimental study implies that CuPc may not be a promising hole transport material for high-performance solar cells using CsPbI2Br as active layer.

scholarly journals Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

2020 ◽  
Author(s):  
Dakota Folmsbee ◽  
Geoffrey Hutchison
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Density Functional ◽  
Large Scale ◽  
Single Point ◽  
Semiempirical Method ◽  
Coupled Cluster ◽  
Scale Evaluation ◽  
Machine Learning Methods ◽  
Electronic Structure Methods

We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

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