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Multifunctional two-dimensional (2D) multiferroics are promising materials for designing low-dimensional multipurpose devices. The key to multifunctionality in these materials is breaking the space-inversion and the time-reversal symmetry,…

We introduce a machine learning prediction workflow to study the impact of defects on the Raman response of 2D materials. By combining the use of machine-learned interatomic potentials, the Raman-active $\Gamma$-weighted density of states…

We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen Cooper Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states. Next, we…

Magnesium hydride has great potential as a solid hydrogen (H) storage material because of its high H storage capacity of 7.6 wt%. However, its slow hydrogenation and dehydrogenation kinetics and the high temperature of 300 $^\circ$C…

Recent experimental reports on the realizations of two-dimensional (2D) networks of the C60-based fullerenes with anisotropic and nanoporous lattices represent a significant advance, and create exciting prospects for the development of a…

The study of atomically thin two-dimensional materials is a young and rapidly growing field. In the past years, a great advance in the study of the remarkable electrical and optical properties of 2D materials fabricated by exfoliation of…

Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work,…

Bimetallic nanoparticles (BNPs) exhibit diverse morphologies such as core-shell, Janus, onion-like, quasi-Janus, and homogeneous structures. Although extensive effort has been directed towards understanding the equilibrium configurations of…

Lateral superlattices in 2D materials are emerging as a powerful platform for exploring novel quantum phenomena, which can be realized through the proximity coupling in forming moir\'e pattern with another layer. This approach, however, is…

Edges are essential for the mechanical, chemical, electronic, and magnetic properties of two-dimensional (2D) materials. Research has shown that features assigned to edges are not strictly localized but often penetrate the bulk to some…

This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and…

Fundamental research on two-dimensional (2D) magnetic systems based on van der Waals materials has been gaining traction rapidly since their recent discovery. With the increase of recent knowledge, it has become clear that such materials…

Understanding the growth mechanisms of two-dimensional (2D) van der Waals (vdW) heterostructures is of great importance in exploring their functionalities and device applications. A custom-built system integrating physical vapor deposition…

Machine learning interatomic potentials (MLIPs) have revolutionized computational materials science by bridging the gap between quantum mechanical accuracy and classical simulation efficiency, enabling unprecedented exploration of materials…

We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here…

Accurately predicting when and how materials fail is critical to designing safe, reliable structures, mechanical systems, and engineered components that operate under stress. Yet, fracture behavior remains difficult to model across the…

Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning…

Accurate building energy models are crucial for analyzing sector-coupled energy systems, where buildings interact with electrified heating, energy storage, and advanced control across various scenarios. High-fidelity (HiFi) white-box models…

The high mechanical strength and excellent flexibility of 2D materials such as graphene are some of their most important properties [1]. Good flexibility is key for exploiting 2D materials in many emerging technologies, such as wearable…

The availability of accurate computational tools for modeling and simulation is vital to accelerate the discovery of materials capable of storing hydrogen (H2) under given parameters of pressure swing and temperature. Previously, we…