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Infrared refractive indices of organic materials are typically resolved through IR ellipsometry. This technique takes advantage of optical interference effects to solve the optical constants. These are the same effects that complicate the analysis of coherent spectroscopy experiments on thin films. Vibrational sum frequency generation is an interface-specific coherent spectroscopy that requires spectral modeling to account for optical interference effects to uncover interfacial molecular responses. Here, we explore the possibility of leveraging incident beam geometries and sample thicknesses to simultaneously obtain the molecular responses and refractive indices. Globally fitting a higher number of spectra with a single set of refractive indices increases the fidelity of the fitted parameters. Finally, we test our method on samples with a range of thicknesses and compare the results to those obtained by IR ellipsometry.

 

Module for ab initio structure evolution (MAISE) is an open-source package for materials modeling and prediction. The code’s main feature is an automated generation of neural network (NN) interatomic potentials for use in global structure searches. The systematic construction of Behler–Parrinello-type NN models approximating ab initio energy and forces relies on two approaches introduced in our recent studies. An evolutionary sampling scheme for generating reference structures improves the NNs’ mapping of regions visited in unconstrained searches, while a stratified training approach enables the creation of standardized NN models for multiple elements. A more flexible NN architecture proposed here expands the applicability of the stratified scheme for an arbitrary number of elements. The full workflow in the NN development is managed with a customizable ‘MAISE-NET’ wrapper written in Python. The global structure optimization capability in MAISE is based on an evolutionary algorithm applicable for nanoparticles, films, and bulk crystals. A multitribe extension of the algorithm allows for an efficient simultaneous optimization of nanoparticles in a given size range. Implemented structure analysis functions include fingerprinting with radial distribution functions and finding space groups with the SPGLIB tool. This work overviews MAISE’s available features, constructed models, and confirmed predictions.