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In this work, the polar tetrahedron [PN$_2$O$_2$] was revealed as a new deep-ultraviolet (deep-UV) nonlinear optically active unit. Accordingly, a thermodynamically stable compound (PNO) consisting of the polar [PN$_2$O$_2$] units was…

Optics · Physics 2022-08-29 Congwei Xie , Abudukadi Tudi , Artem R. Oganov

The recognition and classification of the diversity of materials that exist in the environment around us are a key visual competence that computer vision systems focus on in recent years. Understanding the identification of materials in…

Computer Vision and Pattern Recognition · Computer Science 2017-10-20 Anca Sticlaru

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…

Materials Science · Physics 2019-09-11 Jingyu Qu , David Zageceta , Weiwei Zhang , Qiang Zhu

Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery,…

Computational Engineering, Finance, and Science · Computer Science 2026-02-11 Zhenzhong Wang , Haowei Hua , Wanyu Lin , Ming Yang , Kay Chen Tan

Nonlinear optical phenomena play a critical role in understanding microscopic light-matter interactions and have far-reaching applications across various fields, such as biosensing, quantum information, optical switching, and all-optical…

We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our…

Materials Science · Physics 2018-06-28 Konstantin Gubaev , Evgeny V. Podryabinkin , Gus L. W. Hart , Alexander V. Shapeev

The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…

Materials Science · Physics 2020-07-07 Victor Venturi , Holden Parks , Zeeshan Ahmad , Venkatasubramanian Viswanathan

High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in…

Materials Science · Physics 2019-06-17 Hang Zhang , Kedar Hippalgaonkar , Tonio Buonassisi , Ole M. Løvvik , Espen Sagvolden , Ding Ding

We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid…

Materials Science · Physics 2019-04-22 Austin D. Sendek , Ekin D. Cubuk , Evan R. Antoniuk , Gowoon Cheon , Yi Cui , Evan J. Reed

Efficient screening of chemicals is essential for exploring new materials. However, the search space is astronomically large, making calculations with conventional computers infeasible. For example, an $N$-component system of organic…

Computational Physics · Physics 2021-01-13 Kan Hatakeyama-Sato , Takahiro Kashikawa , Koichi Kimura , Kenichi Oyaizu

Epsilon-near-zero (ENZ) materials such as indium tin oxide (ITO), have recently emerged as a new platform to enhance optical nonlinearities. Here we report a theoretical and experimental study on the origin of nonlinearities in ITO thin…

Photonic integrated circuits are indispensible for data transmission within modern datacenters and pervade into multiple application spheres traditionally limited for bulk optics, such as LiDAR and biosensing. Of particular interest are…

Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles calculations in crystal structure prediction (CSP). However,…

Materials Science · Physics 2026-02-04 Yuqi An , Zhenbin Wang

On-chip integration of two-dimensional (2D) materials offers great potential for the realization of novel optoelectronic devices in different photonic platforms. In particular, indium selenide (InSe) is a very promising 2D material due to…

Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin film transistors. In comparison to oxide…

We perform the first high-throughput search and evaluation of materials that can serve as excellent low-mass dark matter detectors. Using properties of close to one thousand materials from the Materials Project database, we project the…

High Energy Physics - Phenomenology · Physics 2025-06-26 Sinéad M. Griffin , Yonit Hochberg , Benjamin V. Lehmann , Rotem Ovadia , Kristin A. Persson , Bethany A. Suter , Ruo Xi Yang , Wayne Zhao

Nonlinear optics is of crucial importance in several fields of science and technology with applications in frequency conversion, entangled-photon generation, self-referencing of frequency combs, crystal characterization, sensing, and…

Optics · Physics 2022-09-19 O. Dogadov , C. Trovatello , B. Yao , G. Soavi , G. Cerullo

The sustainable development of next-generation device technology is paramount in the face of climate change and the looming energy crisis. Tremendous efforts have been made in the discovery and design of nanomaterials that achieve…

The promise of active nanophotonics technology relies on the confinement and control of light at the nanoscale. Confinement via plasmonics, dielectric resonators, and waveguides can be complemented with materials whose optical properties…