Related papers: Data Mining for Terahertz Generation Crystals
Using optical technology for current injection and electromagnetic emission simplifies the comparison between materials. Here, we inject current into monolayer graphene and bulk gallium arsenide (GaAs) using two-color quantum interference…
Light-matter interactions are key in providing fundamental information about materials. Even in the linear-response regime, the spectroscopic response of a material encodes in it many properties of the ground state as well as of its…
Generative design marks a significant data-driven advancement in the exploration of novel inorganic materials, which entails learning the symmetry equivalent to the crystal structure prediction (CSP) task and subsequent learning of their…
We propose a high resolution spatial diagnostic method via inserting a millimeter-gap grating into the collimated terahertz beam to monitor the minute variation of the terahertz beam in strong-field terahertz sources, which is difficult to…
The development of new high dielectric materials is essential for advancement in modern electronics. Oxides are generally regarded as the most promising class of high dielectric materials for industrial applications as they possess both…
Finding new superconductors with a high critical temperature ($T_c$) has been a challenging task due to computational and experimental costs. We present a diffusion model inspired by the computer vision community to generate new…
Methodology adapted from data science sparked the field of materials informatics, and materials databases are at the heart of it. Applying artificial intelligence to these databases will allow the prediction of properties of complex organic…
Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, and this set is screened for candidate materials for specific applications. In contrast, many…
High energy single- to few-cycle terahertz pulses enable the exploration of the frontier of science, such as electron acceleration, strong-field physics, and spectroscopy. One important method of generating such terahertz pulses is to use…
For a very long time, computational approaches to the design of new materials have relied on an iterative process of finding a candidate material and modeling its properties. AI has played a crucial role in this regard, helping to…
Crystal structure prediction (CSP) has proven to be a highly effective route for discovering new materials. Substantial advancements have been made in CSP of inorganic and molecular crystals, while hybrid materials, including metal-organic…
We propose a novel representation of crystalline materials named orbital-field matrix (OFM) based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Our…
Crystal structure determination from powder diffraction patterns is a complex challenge in materials science, often requiring extensive expertise and computational resources. This study introduces DiffractGPT, a generative pre-trained…
We characterize the terahertz (THz) generation of N-benzyl-2-methyl-4-nitroaniline (BNA), with crystals ranging in thickness from 123-700 {\mu}m. We compare excitation using 800-nm and 1250 to 1500-nm wavelengths. Pumping BNA with 800-nm…
Atomic structure analysis of crystalline materials is a paramount endeavor in both chemical and material sciences. This sophisticated technique necessitates not only a solid foundation in crystallography but also a profound comprehension of…
Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous…
We propose and experimentally prove efficient high-resolution electro-optic sampling measurement of broadband terahertz waveforms in a LiNbO$_3$ crystal in the configuration with the probe laser beam propagating along the optical axis of…
Amorphous materials are integral part of todays technology, they commonly are performant and versatile in integration. Consequently, future applications increasingly aim to harvest the potential of the amorphous state. Establishing its…
Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials…
The combination of deep learning algorithm and materials science has made significant progress in predicting novel materials and understanding various behaviours of materials. Here, we introduced a new model called as the Crystal…