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Atom-like defects in solid-state hosts are promising candidates for the development of quantum information systems, but despite their importance, the host substrate/defect combinations currently under study have almost exclusively been…
Point defects in semiconductors offer a promising platform for advancing quantum technologies due to their localized energy states and controllable spin properties. Prior research has focused on a limited set of defects within materials…
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…
Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To…
The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vastness of chemical and structural space. We present a guided…
Spins in solid-state materials, molecules, and other chemical systems have the potential to impact the fields of quantum sensing, communication, simulation, and computing. In particular, color centers in diamond, such as negatively charged…
Quantum technology has grown out of quantum information theory and now provides a valuable tool that researchers from numerous fields can add to their toolbox of research methods. To date, various systems have been exploited to promote the…
Spin defect centers with long quantum coherence times ($T_2$) are key solid-state platforms for a variety of quantum applications. Recently, cluster correlation expansion (CCE) techniques have emerged as a powerful tool to simulate the…
Wide-bandgap oxides such as ZnO are favorable hosts for spin defect qubits due to their dilute nuclear spin background and potential for ultra-high purity. Yet, a deep-level defect qubit with robust optical and spin properties has not been…
Two-dimensional (2D) semiconductors isoelectronic to phosphorene has been drawing much attention recently due to their promising applications for next-generation (opt)electronics. This family of 2D materials contains more than 400 members,…
Defects are ubiquitous in solids and strongly influence materials' mechanical and functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing…
Swift discovery of spin-crossover materials for their potential application in quantum information devices requires techniques which enable efficient identification of suitably bistable candidates. To this end, we screened the Cambridge…
Defects with associated electron and nuclear spins in solid-state materials have a long history relevant to quantum information science going back to the first spin echo experiments with silicon dopants in the 1950s. Since the turn of the…
Two-dimensional (2D) materials that can host qubits with long spin coherence time (T2) have the distinct advantage of integrating easily with existing microelectronic and photonic platforms, making them attractive for designing novel…
Prediction of critical temperature $(T_c)$ of a superconductor remains a significant challenge in condensed matter physics. While the BCS theory explains superconductivity in conventional superconductors, there is no framework to predict…
Materials discovery is a computationally intensive process that requires exploring vast chemical spaces to identify promising candidates with desirable properties. In this work, we propose using quantum-enhanced machine learning algorithms…
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…
We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced complexity. Training of query-aware similarity-based ridge…
Solid-state point defects are attracting increasing attention in the field of quantum information science, because their localized states can act as a spin-photon interface in devices that store and transfer quantum information, which have…
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between…