Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

We consider a deep learning model for classifying high-dimensional data and seek to
achieve optimal evaluation accuracy and robustness based on multicriteria decision-making …

Understanding the nature and origin of collective excitations in materials is of fundamental
importance for unraveling the underlying physics of a many-body system. Excitation spectra …

Powder X-ray diffraction analysis is a critical component of materials characterization
methodologies. Discerning characteristic Bragg intensity peaks and assigning them to …

Determination of the crystal system and space group is the first step of crystal structure
analysis. Often this turns out to be a bottleneck in the material characterization workflow for …

Determining the structures of previously unseen compounds from experimental
characterizations is a crucial part of materials science. It requires a step of searching for the …

A deep learning (DL)‐based approach for analysis is proposed. Using synthetic XRD data
for a DL approach is inevitable due to the lack of real‐world XRD data. There are two main …

The revolution in materials in the past century was built on a knowledge of the atomic
arrangements and the structure-property relationship. The sine qua non for obtaining …

The process of material discovery and design can be simplified and accelerated if we can
effectively learn from existing data. In this study, we explore the use of machine learning …

This work describes a proof of concept demonstrating that convolutional neural networks
(CNNs) can be used to invert x-ray diffraction (XRD) data, so as to, for instance, retrieve …