Related papers: Data Mining for Terahertz Generation Crystals
The generation of tunable narrowband pulses is increasingly being pursued in terahertz science, for example to study the nonlinear response of individual modes of solids and molecules. Here, we extend the chirp-and-delay method to achieve…
Parametric nonlinear optical processes are instrumental in optical quantum technology for generating entangled light. However, the range of materials conventionally used for producing entangled photons is limited. Metal-organic frameworks…
Since de discovery of graphene, the family of 2-dimensional materials has attracted much recent attention. In this work, the nonlinear optical properties of few-layer MoS2 two-dimensional crystals are studied using femtosecond laser pulses.…
Organic nonlinear optical (NLO) crystals have emerged as efficient room-temperature emitters of broadband THz radiation with high electric field strengths. Although initially confined to high-pulse-energy excitation in the millijoule range…
Experimentally obtained X-ray diffraction (XRD) patterns can be difficult to solve, precluding the full characterization of materials, pharmaceuticals, and geological compounds. Herein, we propose a method based upon a multi-objective…
Accurate crystal structure determination is critical across all scientific disciplines involving crystalline materials. However, solving and refining inorganic crystal structures from powder X-ray diffraction (PXRD) data is traditionally a…
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from…
The efficient generation of terahertz (THz) waves in two-dimensional (2D) MXene layers driven by near-infrared femtosecond laser pulses is demonstrated through predictive simulations. Employing a novel hydrodynamic model that…
Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in…
Recent advances in Foundation Models for Materials Science are poised to revolutionize the discovery, manufacture, and design of novel materials with tailored properties and responses. Although great strides have been made, successes have…
Data-driven machine learning methods have the potential to dramatically accelerate the rate of materials design over conventional human-guided approaches. These methods would help identify or, in the case of generative models, even create…
The discovery of new crystalline materials is essential to scientific and technological progress. However, traditional trial-and-error approaches are inefficient due to the vast search space. Recent advancements in machine learning have…
We present a combined experimental and computational methodology for the discovery of new materials. Density functional theory (DFT) formation energy calculations allow us to predict the stability of various hypothetical structures. We…
Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrangements of atoms in a periodic lattice only a vanishingly…
Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have…
In addition to the forward inference of materials properties using machine learning, generative deep learning techniques applied on materials science allow the inverse design of materials, i.e., assessing the…
A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a result, the physical and chemical properties of such materials…
Terahertz spin waves could be generated on-demand via all-optical manipulation of magnetization by femtosecond laser pulse. Here, we present an energy balance model, which explains the energy transfer rates from laser pulse to electron bath…
Accurately predicting the physical and chemical properties of materials remains one of the most challenging tasks in material design, and one effective strategy is to construct a reliable data set and use it for training a machine learning…
Generative artificial intelligence offers a promising avenue for materials discovery, yet its advantages over traditional methods remain unclear. In this work, we introduce and benchmark two baseline approaches - random enumeration of…