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Evolutionary algorithm is combined with full-potential ab-initio calculations to investigate conformational space of (MoS$_2$)$_n$ and (MoSe$_2$)$_n$ (n=1-10) nanoclusters and to identify the lowest energy structural isomers of these…
Large scale Density Functional Theory (DFT) based electronic structure calculations are highly time consuming and scale poorly with system size. While semi-empirical approximations to DFT result in a reduction in computational time versus…
Bimetallic nanoalloys such as nanoparticles and nanowires are attracting significant attention due to their vast potential applications such as in catalysis and nanoelectronics. Notably, Pd-Pt nanoparticles/nanowires are being widely…
We present a comprehensive approach to the modeling, performance analysis, and design of clustered molecular nanonetworks in which nano-machines of different clusters release an appropriate number of molecules to transmit their sensed…
Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic properties. In the past couple of decades, density functional theory…
The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine…
Achieving robust room-temperature ferromagnetism in purely organic 2D crystals remains a fundamental challenge, primarily due to antiferromagnetic (AFM) coupling mediated by {\pi}-electron superexchange. Here, we present a mix-topology…
We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and…
Extensive first-principle calculations on embedded clusters containing few O, Y, Ti, and Cr atoms as well as vacancies are performed to obtain interaction parameters to be applied in Metropolis Monte Carlo simulations, within the framework…
We demonstrate a machine learning-based approach which predicts the properties of crystal structures following relaxation based on the unrelaxed structure. Use of crystal graph singular values reduces the number of features required to…
Molecules can form myriad crystalline polymorphs, each with distinct properties affecting their performance across diverse applications, from pharmaceuticals to functional materials and more. Predicting the thermodynamically most stable…
Single NV centers in diamond coupled by hyperfine interaction to neighboring 13C nuclear spins are now widely used in the emerging quantum technologies as elements of quantum memory adjusted to NV center electron spin qubit. For nuclear…
Providing highly simplified models of strongly correlated electronic systems that challenge {\it ab initio} calculations can serve as a valuable testing ground to improve these methods. In this study, we present a comprehensive study of the…
We introduce DeepDFT, a deep learning model for predicting the electronic charge density around atoms, the fundamental variable in electronic structure simulations from which all ground state properties can be calculated. The model is…
Multipole moments are the first order responses of the energy to spatial derivatives of the electric field strength. The quality of density functional theory (DFT) prediction of molecular multipole moments thus characterizes errors in…
The cluster multipole (CMP) expansion for magnetic structures provides a scheme to systematically generate candidate magnetic structures specifically including noncollinear magnetic configurations adapted to the crystal symmetry of a given…
Due to its favorable computational efficiency time-dependent (TD) density functional theory (DFT) enables the prediction of electronic spectra in a high-throughput manner across chemical space. Its predictions, however, can be quite…
Clustering is a core task in machine learning with wide-ranging applications in data mining and pattern recognition. However, its unsupervised nature makes it inherently challenging. Many existing clustering algorithms suffer from critical…
We show that a deep-learning neural network potential (DP) based on density functional theory (DFT) calculations can well describe Cu-Zr materials, an example of a binary alloy system that can coexist in several ordered intermetallics and…
The electronic structure calculation for the nanoclusters of (AsSiTeB/SiAsBTe) quaternary semiconductor alloy belonging to the (III-V Group elements) is performed. The two clusters one in the linear form and the other in the bent form have…