[HTML][HTML] Machine-Learning Strategies for the Accurate and Efficient Analysis of X-ray Spectroscopy
T Penfold, L Watson, C Middleton… - Machine Learning …, 2024 - iopscience.iop.org
Computational spectroscopy has emerged as a critical tool for researchers looking to
achieve both qualitative and quantitative interpretations of experimental spectra. Over the …
achieve both qualitative and quantitative interpretations of experimental spectra. Over the …
Atomic Precision Processing of Two-Dimensional Materials for Next-Generation Microelectronics
The growth of the information era economy is driving the pursuit of advanced materials for
microelectronics, spurred by exploration into “Beyond CMOS” and “More than Moore” …
microelectronics, spurred by exploration into “Beyond CMOS” and “More than Moore” …
In Situ and Operando Spectroscopic Techniques for Electrochemical Energy Storage and Conversion Applications
Understanding the mechanisms of action of fundamental redox processes is of great interest
for the development of more active catalysts and materials for energy storage and …
for the development of more active catalysts and materials for energy storage and …
Computational optimal transport for molecular spectra: The fully continuous case
Computational optimal transport is used to analyze the difference between pairs of
continuous molecular spectra. It is demonstrated that transport distances which are derived …
continuous molecular spectra. It is demonstrated that transport distances which are derived …
Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure …
An important yet challenging aspect of atomistic materials modeling is reconciling
experimental and computational results. Conventional approaches involve generating …
experimental and computational results. Conventional approaches involve generating …
Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides
Precise determination of atomic structural information in functional materials holds
transformative potential and broad implications for emerging technologies. Spectroscopic …
transformative potential and broad implications for emerging technologies. Spectroscopic …
Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS
The fact that the photoabsorption spectrum of a material contains information about the
atomic structure, commonly understood in terms of multiple scattering theory, is the basis of …
atomic structure, commonly understood in terms of multiple scattering theory, is the basis of …
Kalman filter enhanced active learning sampling for inelastic neutron scattering: The case of CrSBr
Spin waves, or magnons, are fundamental excitations in magnetic materials that provide
insights into their dynamic properties and interactions. Magnons are the building blocks of …
insights into their dynamic properties and interactions. Magnons are the building blocks of …
Experiment-driven atomistic materials modeling: A case study combining XPS and ML potentials to infer the structure of oxygen-rich amorphous carbon
An important yet challenging aspect of atomistic materials modeling is reconciling
experimental and computational results. Conventional approaches involve generating …
experimental and computational results. Conventional approaches involve generating …
Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy
Atomistic structures of materials offer valuable insights into their functionality. Determining
these structures remains a fundamental challenge in materials science, especially for …
these structures remains a fundamental challenge in materials science, especially for …