Predicting solid state material platforms for quantum technologies

Abstract

Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods. Different approaches were implemented to label data for training the supervised machine learning (ML) algorithms logistic regression, decision trees, random forests and gradient boosting. We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates. In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility, the ML methods highlight features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT.

Formation energy prediction of neutral single-atom impurities in 2D materials using tree-based machine learning

“…Likewise, the presence of impurities can enhance the performance of sensing [19][20][21][22][23][24]. Layered materials are being explored extensively both experimentally [11,[25][26][27][28][29] and computationally [30][31][32][33] to investigate their properties for specific purposes.…”

High-Throughput First-Principles Prediction of Interfacial Adhesion Energies in Metal-on-Metal Contacts

“…Moreover in recent times, alongside the fundamental combination of theory and experiments on specific problems, high-throughput density functional theory (DFT) approaches are used in materials design and discovery. − Increasingly complex DFT-based high-throughput workflows have been developed, screening molecular adsorption energies and sites on intermetallic surfaces in view of electrochemical catalysis applications, looking for novel 2D superconductors, predicting lattice parameters and formation energies of high-entropy alloys, and identifying promising metal–organic frameworks for heterogeneous catalysis. , However, in this quickly developing framework, a systematic study of mechanical and tribological properties of solid–solid heterointerfaces has not been addressed yet. Most probably this is due to the inherent difficulties that this kind of system poses and to the fact that the community of references (the tribology, metallurgy, and mechanical manufacturing communities) most of the time relies on classical macroscopic engineering models.…”

TribChem: A Software for the First-Principles, High-Throughput Study of Solid Interfaces and Their Tribological Properties

“…Integrating the experiments with computational tools and digital data is considered a key strategy to reduce the time and costs for materials discovery and deployment. In this context, first-principles high-throughput calculations, which allow for the density functional theory (DFT) description of many materials in parallel and in an automatized way, represent very powerful tools. − The calculated properties, usually highly accurate, are stored in databases and eventually analyzed with the aid of machine-learning algorithms, allowing for the identification of general trends and predictions. Moreover, raw data are also stored so that the calculation of further properties and rigorous validations are possible.…”