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Two-dimensional (2D) materials have attracted a great deal of interest in recent years. This family of materials allows for the realization of versatile electronic devices and holds promise for next-generation (opto)electronics. Their…

Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior…

Since the advent of graphene, two-dimensional (2D) materials become very attractive and there is growing interest to explore new 2D beyond graphene. Here, through density functional theory (DFT) calculations, we predict 2D wide-band-gap…

Generative models such as AlphaFold and MatterGen can directly generate novel material structures with desired properties, accelerating the new materials discovery and revolutionizing the material design paradigm from traditional…

Starting from graphene, 2D layered materials family has been recently set up more than 100 different materials with variety of different class of materials such as semiconductors, metals, semimetals, superconductors. Among these materials,…

Mechanical metamaterials enable precise control over structural properties, but their design method remains challenging due to their complex structure. Although additive manufacturing has expanded geometric freedom, navigating this vast and…

Metal superhydrides, known for their high hydrogen content and polyhedral hydrogen cages, are promising candidates for high-temperature superconductivity. Recent research has emphasized "chemical pre-compression," enabling hydrogen…

Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab-initio methods like density functional theory…

The application of machine learning in materials presents a unique challenge of dealing with scarce and varied materials data - both experimental and theoretical. Nevertheless, several state-of-the-art machine learning models for materials…

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…

Deep learning based generative models such as deepfake have been able to generate amazing images and videos. However, these models may need significant transformation when applied to generate crystal materials structures in which the…

Two-dimensional (2D) layered materials, demonstrating significantly different properties from their bulk counterparts, offer a materials platform with potential applications from energy to information processing devices. Although some…

We search for novel two-dimensional materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered…

The end of conventional Dennard scaling and the widening gap between memory bandwidth and arithmetic throughput have made the von Neumann partition a structural bottleneck rather than a transient one. Two-dimensional (2D) materials, with…

Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to…

In the last decade, the materials community has been exploring new 2D materials (graphene, metallene, TMDs, TMCs, MXene, among others) that have unique physical and chemical properties. Recently, a new family of 2D materials, the so-called…

As the continuing down-scaling of field-effect transistors (FETs) in more-than-Moore integrated circuits, finding new functional two-dimensional (2D) materials with a higher dielectric constant (high-k) serve as gate dielectrics is…

The exponentially growing number of interconnected devices in the Internet of Things poses an increasing amount of challenges to the field of cyber security and encryption. For authenticated use and communication, each device must securely…

Low-dimensional materials have attractive properties that drive intense efforts for novel materials discovery. However, experiments are tedious for systematic discovery, and present computational methods are often tuned to two-dimensional…

We develop new transfer learning algorithms to accelerate prediction of material properties from ab initio simulations based on density functional theory (DFT). Transfer learning has been successfully utilized for data-efficient modeling in…