Matthew R. Carbone
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View article: Strain release through hydrogen bond–mediated layer twisting
Strain release through hydrogen bond–mediated layer twisting Open
Strain engineering, enabling the precise control over structure and functional properties, is a key strategy for the design of advanced materials. However, the mechanisms governing strain evolution and release at the nanoscale remain large…
View article: Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models
Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models Open
In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for x-ray absorption spectroscopy (XAS) analysis. Compared to traditional XAS analysis methods, AI/ML approaches off…
View article: Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning
Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning Open
Identifying model Hamiltonians is a vital step toward creating predictive models of materials. Here, we combine Bayesian optimization with the EDRIXS numerical package to infer Hamiltonian parameters from resonant inelastic X-ray scatterin…
View article: OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra
OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra Open
X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computation…
View article: Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential
Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential Open
ZnCl2 solutions are promising electrolytes for aqueous zinc-ion batteries. Here, we report a joint computational and experimental study of the structural and dynamic properties of aqueous ZnCl2 electrolytes with concentrations ranging from…
View article: Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System
Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System Open
The expansion of artificial intelligence (AI) applications has driven substantial investment in computational infrastructure, especially by cloud computing providers. Quantifying the energy footprint of this infrastructure requires models …
View article: Empirical Measurements of AI Training Power Demand on a GPU-Accelerated Node
Empirical Measurements of AI Training Power Demand on a GPU-Accelerated Node Open
The expansion of artificial intelligence (AI) applications has driven substantial investment in computational infrastructure, especially by cloud computing providers. Quantifying the energy footprint of this infrastructure requires models …
View article: CuXASNet: Rapid and Accurate Prediction of Copper L-edge X-Ray Absorption Spectra Using Machine Learning
CuXASNet: Rapid and Accurate Prediction of Copper L-edge X-Ray Absorption Spectra Using Machine Learning Open
In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu L-edge X-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a…
View article: Elucidating the Discharge Behavior of Aqueous Zinc Sulfur Batteries in the Presence of Molybdenum(IV) Chalcogenide Catalyst: The Criticality of Interfacial Electrochemistry
Elucidating the Discharge Behavior of Aqueous Zinc Sulfur Batteries in the Presence of Molybdenum(IV) Chalcogenide Catalyst: The Criticality of Interfacial Electrochemistry Open
The aqueous zinc-sulfur battery holds promise for significant capacity and energy density with low cost and safe operation based on environmentally benign materials. However, it suffers from the sluggish kinetics of the conversion reaction…
View article: Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models
Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models Open
Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable c…
View article: OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra
OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra Open
X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computation…
View article: In situ Synchrotron X‐ray Metrology Boosted by Automated Data Analysis for Real‐time Monitoring of Cathode Calcination
In situ Synchrotron X‐ray Metrology Boosted by Automated Data Analysis for Real‐time Monitoring of Cathode Calcination Open
Synchrotron X‐ray‐based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive…
View article: Machine learning for the advancement of membrane science and technology: A critical review
Machine learning for the advancement of membrane science and technology: A critical review Open
View article: Machine learning-guided discovery of polymer membranes for CO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si41.svg" display="inline" id="d1e198"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> separation with genetic algorithm
Machine learning-guided discovery of polymer membranes for CO separation with genetic algorithm Open
View article: Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning
Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning Open
Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about distributions and locations of atoms, their coordination numbers and oxidation states, and the bonding characteristics [1].…
View article: Atomic insights into the oxidative degradation mechanisms of sulfide solid electrolytes
Atomic insights into the oxidative degradation mechanisms of sulfide solid electrolytes Open
View article: Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy
Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy Open
Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presen…
View article: Machine learning-based discovery of molecular descriptors that control polymer gas permeation
Machine learning-based discovery of molecular descriptors that control polymer gas permeation Open
View article: Neural network ensembles and FEFF spectra for multi-modal small molecule chemical motif prediction
Neural network ensembles and FEFF spectra for multi-modal small molecule chemical motif prediction Open
Data 22-12-05-data: original molecular XANES data created from Ghose et al. 23-04-26-ml-data: machine learning-ready data which is prepared in the format required by Crescendo. 23-05-03-hp: hyper-parameter tuning results from 23-04-26-ml-d…
View article: Neural network ensembles and FEFF spectra for multi-modal small molecule chemical motif prediction
Neural network ensembles and FEFF spectra for multi-modal small molecule chemical motif prediction Open
Data 22-12-05-data: original molecular XANES data created from Ghose et al. 23-04-26-ml-data: machine learning-ready data which is prepared in the format required by Crescendo. 23-05-03-hp: hyper-parameter tuning results from 23-04-26-ml-d…
View article: Physically interpretable approximations of many-body spectral functions
Physically interpretable approximations of many-body spectral functions Open
The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the vector fitting (VFIT) algorithm to fit a variety of spectral functions cal…
View article: Machine Learning-Guided Discovery of Polymer Membranes for Co2 Separation with Genetic Algorithm
Machine Learning-Guided Discovery of Polymer Membranes for Co2 Separation with Genetic Algorithm Open
View article: Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models
Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models Open
The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine le…
View article: Flexible formulation of value for experiment interpretation and design
Flexible formulation of value for experiment interpretation and design Open
View article: Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models
Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models Open
The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine le…
View article: The Generalized Green’s function Cluster Expansion: APython package for simulating polarons
The Generalized Green’s function Cluster Expansion: APython package for simulating polarons Open
We present an efficient implementation of the Generalized Green’s function Cluster Expansion (GGCE), which is a new method for computing the ground-state properties and dynamics of polarons (single electrons coupled to lattice vibrations) …
View article: Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes
Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes Open
Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in the performance of solid-state batteries. In this study, by…
View article: Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon
Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon Open
Improved understanding of structural and chemical properties through local experimental probes, such as X-ray absorption near-edge structure (XANES) spectroscopy, is crucial for the understanding and design of functional materials. In rece…
View article: Lightshow: a Python package for generatingcomputational x-ray absorption spectroscopy input files
Lightshow: a Python package for generatingcomputational x-ray absorption spectroscopy input files Open
First-principles computational spectroscopy is a critical tool for interpreting experiment, per- forming structure refinement, and developing new physical understanding. Systematically setting up input files for different simulation codes …
View article: Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design
Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design Open
The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in vast sample spaces, although it requires framing experimenta…