Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups

Fine, Jonathan; Rasjashekar, Anand; Jethava, Krupal P.; Chopra, Gaurav

doi:10.26434/chemrxiv.8081924.v2

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…In particular, dimensionality reduction (DR) techniques such as principal component analysis (PCA) are commonly used in Raman spectral analysis in order to obtain a compressed (latent) representation that facilitates analysis or interpretation, which can be useful to detect outliers or otherwise discriminate between complex spectra [134,135]. More recently, data-driven workflows have been proposed automate, accelerate, and improve Raman spectral analysis through denoising and reconstruction of low signal-to-noise ratio Raman signatures [136] and prediction or classification of a broad range of associated properties [137][138][139][140][141][142]. Machine learning methods have also been used to predict the surface-enhanced Raman spectroscopy signals of different molecular conformations [143].…”

Section: Unsupervised Representation Learning Of Vibrational Spectramentioning

confidence: 99%

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

Andrejevic

2022

Springer Theses

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Neutron and photon scattering and spectroscopy represent two fundamental categories of characterization techniques used to interrogate materials' structural and dynamical properties at atomic to mesoscopic length scales. As advances at scientific user facilities enable the collection of ever larger data volumes in higher-dimensional parameter spaces, the design, analysis, and interpretation of such experiments becomes both increasingly valuable and complex. At the same time, interest in novel functional and quantum materials for next-generation technologies, including dissipationless electronics, energy harvesting, and quantum computing, demands an understanding of unconventional or emergent properties beyond the scope of many approximate models. Machine learning methods are designed to leverage large, highdimensional datasets in order to detect underlying patterns and make informed predictions on related tasks. Thus, integration of these data-driven methods with neutron and photon spectroscopies has the potential to improve experimental design, accelerate and enhance data analysis, and uncover insights beyond traditional models.This thesis work demonstrates the proposed integration of machine learning to augment experimental design and analysis in the context of four different scattering and spectroscopic techniques. First, we use Euclidean neural networks to predict materials' vibrational properties directly from the atomic masses and positions of their constituent atoms, highlighting the importance of using effective materials data representations to strengthen model performance and interpretability. We then consider how machine learning methods can be applied to develop effective, low-dimensional representations of materials' spectral signatures, which we exemplify through unsupervised representation learning of Raman spectra. Such learned representations are proposed as efficient prediction targets for supervised learning from relevant structural or chemical attributes, or as convenient parameter spaces for optimization of physical models. We illustrate the latter by training a variational autoencoder to retrieve the sample parameters of proximity-coupled heterostructures from their polarized neutron reflectometry profiles with high resolution. Finally, we study the capacity of machine learning models to extract "hidden" insights from spectral data by develop-

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Section: Unsupervised Representation Learning Of Vibrational Spectramentioning

confidence: 99%

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

Andrejevic

2022

Springer Theses

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“…One example is the use of ML to interpret different types of spectroscopic measurements to determine structural or electronic properties of molecules and materials. Fine et al 241 have recently presented a ML approach to extract data on functional groups from infrared and mass spectroscopy data, while Kiyohara et al 242 have successfully applied a ML scheme to obtain chemical, elemental, and geometric information from the X-ray spectra of materials. Another application where ML shows promise is the automated interpretation of nuclear magnetic resonance spectra with respect to atomic structure, which typically relies heavily on experience.…”

Section: ML Helps To Connect Theory and Experimentsmentioning

confidence: 99%

Perspective on integrating machine learning into computational chemistry and materials science

Westermayr,

Gastegger,

Schütt

et al. 2021

Preprint

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Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties -be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how various aspects of atomistic computational modelling are being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a deeper and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.

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“…This has been presented for instance in refs. [18][19][20][21]. As a last example of the potentiality of machine learning trainings in the context of molecular dynamics simulations, Vacher and coll.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic graph theory, reinforcement learning and game theory in MD simulations: from 3D-structures to topological 2D-MolGraphs and backwards

Bougueroua

Bricage

Aboulfath

et al. 2022

Preprint

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This paper reviews graph theory-based methods that were recently developed in our group for post-processing molecular dynamics trajectories. We show that the use of algorithmic graph theory not only provides a direct and fast methodology to identify conformers sampled over time but also allows to follow the interconversions between the conformers through graphs of transitions in time. Examples of gas phase molecules and inhomogeneous aqueous solid interfaces are presented to demonstrate the power of topological 2D graphs and their versatility for post-processing molecular dynamics trajectories. An even more complex challenge is to predict 3D structures from topological 2D graphs. Our first attempts to tackle such a challenge are presented with the development of game theory and reinforcement learning methods for predicting the 3D structure of a gas-phase peptide.

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Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups

Cited by 3 publications

References 34 publications

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

Perspective on integrating machine learning into computational chemistry and materials science

Algorithmic graph theory, reinforcement learning and game theory in MD simulations: from 3D-structures to topological 2D-MolGraphs and backwards

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