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Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We…
In this study, we present a novel approach along with the needed computational strategies for efficient and scalable feature engineering of the crystal structure in compounds of different chemical compositions. This approach utilizes a…
At the few-atom-thick limit, transition metal dichalcogenides (TMDs) exhibit a host of attractive electronic optical, and structural properties. The possibility to pattern these properties has a great impact on applied and fundamental…
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…
The phase transitions among polymorphic two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted increasing attention for their potential in enabling distinct functionalities in the same material for making integrated…
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal…
Transition metal-intercalated transition metal dichalcogenides (TMDs) are promising platforms for next-generation spintronic devices based on their wide range of electronic and magnetic phases, which can be tuned by varying the host lattice…
Two-dimensional (2D) layered transition metal dichalcogenides (TMDCs) are promising memristive materials for neuromorphic computing systems as they could solve the problem of the excessively high energy consumption of conventional von…
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…
Substitutional transition metal (TM) point defects have recently been controllably introduced in two-dimensional (2D) transition metal dichalcogenides. We identify quantum defect candidates through a first-principles materials discovery…
Machine learning (ML) is becoming increasingly popular for predicting material properties to accelerate materials discovery. Because material properties are strongly affected by its crystal structure, a key issue is converting the crystal…
Transition metal dichalcogenides (TMDs) are regarded as a possible materials platform for quantum information science and related device applications. In TMD monolayers, the dephasing time and inhomogeneity are crucial parameters for any…
Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
TMDs have recently been spotlighted due to their original features. Firstly, they have layered structures with Van der Waals interactions. Secondly, they have different phases, affecting a degree of anisotropy. Finally, they have excellent…
MoTe$_2$ is an exfoliable transition metal dichalcogenide (TMD) which crystallizes in three symmetries, the semiconducting trigonal-prismatic $2H-$phase, the semimetallic $1T^{\prime}$ monoclinic phase, and the semimetallic orthorhombic…
Transition metal dichalcogenides (TMD) are currently among the most interesting two-dimensional (2D) materials due to their outstanding properties. MoTe2 involves attractive polymorphic TMD crystals which can exist in three different 2D…
We train artificial neural networks to distinguish the geometric phases of a set of bands in 1D photonic crystals. We find that the trained network yields remarkably accurate predictions of the topological phases for 1D photonic crystals,…
Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding…
Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for…