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Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently hindered by…
Chiral crystals show promise for spintronic technologies on account of their high spin selectivity, which has led to significant recent interest in quantitative characterization and first-principles prediction of their spin-optoelectronics…
In this paper, we introduce a density-based topology optimization framework to design porous electrodes for maximum energy storage. We simulate the full cell with a model that incorporates electronic potential, ionic potential, and…
Electrical double layers play a key role in a variety of electrochemical systems. The mean free path of secondary electrons in aqueous solutions is on the order of a nanometer, making them suitable for probing of ultrathin electrical double…
2D quantum confined hybrid materials are of great interest from a solid state physics standpoint because of the rich multibody phenomena hosted, their tunability and easy synthesis allowing to create material libraries. In addition, from a…
The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain…
The study of graphene, since its discovery around 2004, is possibly the largest and fastest growing field of research in material science, because of its exotic mechanical, thermal, electronic, optical and chemical properties. The studies…
We introduce the Computational 2D Materials Database (C2DB), which organises a variety of structural, thermodynamic, elastic, electronic, magnetic, and optical properties of around 1500 two-dimensional materials distributed over more than…
Electron scattering on liquid samples has been enabled recently by the development of ultrathin liquid sheet technologies. The data treatment of liquid-phase electron scattering has been mostly reliant on methodologies developed for gas…
Throughout the physical sciences, entropy stands out as a pivotal but enigmatic concept that, in materials design, often takes a backseat to energy. Here, we demonstrate how to precisely engineer entropy to achieve desired colloidal…
Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized…
Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these…
Electrides are unique materials because of the existence of interstitial anionic electrons (IAEs). Due to these loosely bound IAEs and their strong interaction with the framework of cations, electrides can host superconductivity with rather…
We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains…
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…
Traditional trial-and-error methods are obstacles for large-scale searching of new optoelectronic materials. Here, we introduce a method combining high-throughput ab initio calculations and machine-learning approaches to predict…
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy…
Recent advances in the synthesis of polar molecular materials have produced practical alternatives to ferroelectric ceramics, opening up exciting new avenues for their incorporation into modern electronic devices. However, in order to…
The traditional display of elements in the periodic table is convenient for the study of chemistry and physics. However, the atomic number alone is insufficient for training statistical machine learning models to describe and extract…
While Cadmium Telluride (CdTe) excels in terms of photon radiation absorption properties and outperforms silicon (Si) in this respect, the crystal growth, characterization and processing into a radiation detector is much more complicated.…