Computational optimal transport for molecular spectra: The fully continuous case

Seifert, Nathan A.; Prozument, Kirill; Davis, Michael J.

doi:10.1063/5.0166469

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…While CDF appears to alleviate the need for precise energy alignment, the success of model trained on simulated spectra toward inference from experimental measurements still depends heavily on the accuracy of the simulated spectral shape. Although this study focused on a specific technique (XANES) and a specific edge for a family of battery materials, the use of CDF may be generally advantageous for spectroscopic studies, as shown in recent work on quantitative metrics for comparing molecular spectra. − …”

Section: Discussionmentioning

confidence: 99%

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

Chen,

Hwang

et al. 2024

Chem. Mater.

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X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. In this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community.

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Section: Discussionmentioning

confidence: 99%

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

Chen,

Hwang

et al. 2024

Chem. Mater.

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An Automated Analysis of Homocoupling Defects Using MALDI-MS and Open-Source Computer Software

Bochenek,

Ciach,

Smeets

et al. 2024

J. Am. Soc. Mass Spectrom.

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Conjugated organic polymers have substantial potential for multiple applications but their properties are strongly influenced by structural defects such as homocoupling of monomer units and unexpected end-groups. Detecting and/or quantifying these defects requires complex experimental techniques, which hinder the optimization of synthesis protocols and fundamental studies on the influence of structural defects. Mass spectrometry offers a simple way to detect these defects but a manual analysis of many complex spectra is tedious and provides only approximate results. In this work, we develop a computational methodology for analyzing complex mass spectra of organic copolymers. Our method annotates spectra similarly to a human expert and provides quantitative information about the proportions of signal assigned to each ion. Our method is based on the open-source Masserstein algorithm, which we modify to handle large libraries of reference spectra required for annotating complex mass spectra of polymers. We develop a statistical methodology to analyze the quantitative annotations and compare the statistical distributions of structural defects in polymer chains between samples. We apply this methodology to analyze commercial and lab-made samples of a benchmark polymer and show that the samples differ both in the amount and in the types of structural defects.

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Twins in rotational spectroscopy: Does a rotational spectrum uniquely identify a molecule?

Schwarting,

Seifert,

Davis

et al. 2024

The Journal of Chemical Physics

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Rotational spectroscopy is the most accurate method for determining structures of molecules in the gas phase. It is often assumed that a rotational spectrum is a unique “fingerprint” of a molecule. The availability of large molecular databases and the development of artificial intelligence methods for spectroscopy make the testing of this assumption timely. In this paper, we pose the determination of molecular structures from rotational spectra as an inverse problem. Within this framework, we adopt a funnel-based approach to search for molecular twins, which are two or more molecules, which have similar rotational spectra but distinctly different molecular structures. We demonstrate that there are twins within standard levels of computational accuracy by generating rotational constants for many molecules from several large molecular databases, indicating that the inverse problem is ill-posed. However, some twins can be distinguished by increasing the accuracy of the theoretical methods or by performing additional experiments.

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Computational optimal transport for molecular spectra: The fully continuous case

Cited by 3 publications

References 43 publications

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

An Automated Analysis of Homocoupling Defects Using MALDI-MS and Open-Source Computer Software

Twins in rotational spectroscopy: Does a rotational spectrum uniquely identify a molecule?

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