Systems-chart approach to the design of spin relaxation times in molecular qubits
The paper presents systems design charts for molecular qubits, mapping chemical variables to spin relaxation times for optimized qubit design.
Engineering topics
Publications and source records attributed to Rondinelli, James M..
The paper presents systems design charts for molecular qubits, mapping chemical variables to spin relaxation times for optimized qubit design.
Patterning spins to generate collective behavior is at the core of condensed matter physics. Physicists develop techniques, including the fabrication of magnetic nanostructures and precision layering of materials specifically to engender frustrated lattices. As chemists, we can access such exotic materials through targeted chemical synthesis and create new lattice types by chemical design. Here, in this study, we introduce a new approach to induce magnetic frustration on a modified honeycomb lattice through a competition of alternating antiferromagnetic (AFM) and ferromagnetic (FM) nearest-neighbor interactions. By subtly modulating these two types of interactions through facile synthetic modifications, we created two systems: (1) a topological spin glass and (2) a frustrated spin-canted magnet with low-temperature exchange bias. To design this unconventional magnetic lattice, we used a metal–organic framework (MOF) platform, Ni 3 (pymca) 3 X 3 (NipymcaX where pymca = pyrimidine-2-carboxylato and X = Cl, Br). We isolated two MOFs, NipymcaCl and NipymcaBr, featuring canted Ni 2+ -based moments. Despite this similarity, differences in the single-ion anisotropies of the Ni 2+ spins result in distinct magnetic properties for each material. NipymcaCl is a topological spin glass, while NipymcaBr is a rare frustrated magnet with low-temperature exchange bias. Density functional theory calculations and Monte Carlo simulations on the NipymcaX lattice support the presence of magnetic frustration as a result of alternating AFM and FM interactions. Our calculations enabled us to determine the ground-state spin configuration and the distribution of spin–spin correlations relative to paradigmatic kagomé and triangular lattices. This modified honeycomb lattice is similar to the electronic Kekulé-O phase in graphene and provides a highly tunable platform to realize unconventional spin physics.
Enabling coherent spin-control in integer spin complexes with molecular color center design.
Band topology of anomalous quantum Hall insulators can be precisely addressed by computing the Chern numbers of constituent nondegenerate bands, describing the presence of quantized, Abelian Berry flux through the two-dimensional Brillouin zone. Can Berry flux be captured for the SU (2) Berry connection of two -fold degenerate bands in spinful materials preserving space -inversion ($\mathscr{P}$) and time -reversal ($\mathscr{T}$) symmetries without detailed knowledge of underlying basis? We address this question by investigating the correspondence between a non -Abelian generalization of Stokes' theorem and the manifestly gauge -invariant eigenvalues of Wilson loops computed along in -plane contours which preserve the underlying crystalline symmetry. The importance of this correspondence is elucidated by performing natural number resolved classification of ab initio band structures of three-dimensional, Dirac materials. Further, our work underscores how identification of quantized Berry flux, both Abelian and non -Abelian, offers a unified framework for addressing first -order and higher -order topology of insulators and semimetals.
Thin-film ferroelectrics have been pursued for capacitive and nonvolatile memory devices. They rely on polarizations that are oriented in an out-of-plane direction to facilitate integration and addressability with complementary metal-oxide semiconductor architectures. The internal depolarization field, however, formed by surface charges can suppress the out-of-plane polarization in ultrathin ferroelectric films that could otherwise exhibit lower coercive fields and operate with lower power. Here, we unveil stabilization of a polar longitudinal optical (LO) mode in the n = 2 Ruddlesden–Popper family that produces out-of-plane ferroelectricity, persists under open-circuit boundary conditions, and is distinct from hyperferroelectricity. Our first-principles calculations show the stabilization of the LO mode is ubiquitous in chalcogenides and halides and relies on anharmonic trilinear mode coupling. We further show that the out-of-plane ferroelectricity can be predicted with a crystallographic tolerance factor, and we use these insights to design a room-temperature multiferroic with strong magnetoelectric coupling suitable for magneto-electric spin-orbit transistors.
Transition metal subchalcogenides involve electron-rich metals and can facilitate an in-depth understanding of the relationships among quantum properties such as superconductivity, charge density wave, and topological band structures. However, effective experimental routes toward synthesizing transition metal subchalcogenides are still lacking, hindering the development of new quantum materials. Herein, we propose a eutectic polytelluride flux strategy as an excellent solution to address phase discovery and crystal growth in transition metal subtelluride systems. We report new phases easily and selectively synthesized using a eutectic “K 3 Te 4 ” polytelluride flux upon adjusting the ratio of Nb metal to flux in the starting materials (K/Nb/Te = 3:x:4). Using a high Nb content in the solvent (x = 2 and 1), crystals of KNb 3 Te 3 O 0.38 and K 0.9 Nb 3 Te 4 are obtained. Both subtellurides exhibit diverse Nb clusters, including face-sharing and edge-sharing Nb 6 octahedral columns and zig-zag Nb chains. Reducing the Nb content to x = 0.33 leads to the formation of a layered compound, K 1.06 NbTe 2 . This compound comprises a NbTe 6 trigonal prism with K intercalated between the layers. Single crystals of known binary Nb tellurides can also be grown using another eutectic flux “KTe 3 . 2 ”, and the obtained NbTe 2 exhibits a new polymorphism with extra trimerization along the b-axis in the Nb–Nb bonded double zig-zag cluster. Finally, precise control over the structural dimensionality and oxidation state, combined with the facile crystal growth process, makes our synthetic strategy an efficient route to explore quantum materials in transition metal subchalcogenides.
Abstract A bimetallic hydroxychalcogenide, BaZn 2 Se 2 (OH) 2 , was synthesized through hydrothermal pouch methods. The single crystal X‐ray diffraction and electron diffraction indicates that the phase crystallizes in the orthorhombic space group Pnma and is composed of anionic layers [ZnSe 3/3 (OH) 1/1 ] − that are separated and charged balanced by Ba 2+ cations. The [ZnSe 3/3 (OH) 1/1 ] – layer comprises two unique Zn sites, which form interpenetrating zigzag chains with an in‐plane dipole moment and adopts a brownmillerite‐type structural motif. The adjacent layers contain tetrahedrally coordinated Zn chains of opposite handedness related by an inversion center, which cancel the microscopic dipoles to minimize the macroscopic electric polarization. The adoption of a brownmillerite structural motif in BaZn 2 Se 2 (OH) 2 can be rationalized by the distinct charge difference between Se 2− and OH − anions, which creates a sufficient dipole moment in the ZnSe 3 (OH) tetrahedra to allow the occurrence of twisted chains. FTIR spectroscopy confirms the existence of OH − anions and DFT calculations indicate that BaZn 2 Se 2 (OH) 2 is a semiconductor with a direct band gap. This work expands the chemistry of the brownmillerite family from traditional homoanionic oxides to multianion hydroxychalcogenides, offering a new opportunity to explore tunable structural complexity for better design of functional materials.
Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.
Persistent spin textures (PSTs) in momentum space have the potential to enable spintronic devices which are currently limited by spin lifetimes in nonmagnetic spin-orbit coupled materials. We perform a first-principles study on the proustite mineral family, Ag 3 BQ 3 (B = As, Sb; Q = S, Se), and show these chalcogenides exhibit nonsymmetry-protected PSTs, which we refer to as symmetry-assisted PSTs. Chemical substitution can be used to tune the PST quality and properties, e.g., spin lifetime, and we find that a Rashba anisotropy criterion correlates with the PST area and spin lifetime for two of the three proustites examined. Last, we show that a first-order effective SOC Hamiltonian, often used in two-dimensional systems, is insufficient to describe the PST state in all proustites, suggesting that higher order models are necessary to fully describe PSTs in bulk three-dimensional materials.
Persistent spin textures (PSTs) in solid-state materials arise from a unidirectional spin-orbit field in momentum space and offer a route to deliver long carrier spin lifetimes sought for future quantum microelectronic devices. Nonetheless, few three-dimensional materials are known to host PSTs owing to crystal symmetry and chemical requirements. There are even fewer examples demonstrated experimentally. Here we report that high-quality persistent spin textures can be obtained in the polar point groups containing an odd number of mirror operations. We use representation theory analysis and electronic structure calculations to formulate general discovery principles to identify PSTs hidden in known complex ternary layered and perovskite structures with large electric polarizations. We then show some of these materials exhibit PSTs without requiring any special crystalline symmetries. This finding removes the limitation imposed by mirror-symmetry protected PSTs that has limited compound discovery. Our general design approach enables the pursuit of persistent spin helices in materials exhibiting the C 3 v crystal class adopted by many quantum materials exhibiting large Rashba coefficients.
Abstract Engineering design often involves qualitative and quantitative design variables, which requires systematic methods for the exploration of these mixed-variable design spaces. Expensive simulation techniques, such as those required to evaluate optimization objectives in materials design applications, constitute the main portion of the cost of the design process and underline the need for efficient search strategies—Bayesian optimization (BO) being one of the most widely adopted. Although recent developments in mixed-variable Bayesian optimization have shown promise, the effects of dimensionality of qualitative variables have not been well studied. High-dimensional qualitative variables, i.e., with many levels, impose a large design cost as they typically require a larger dataset to quantify the effect of each level on the optimization objective. We address this challenge by leveraging domain knowledge about underlying physical descriptors, which embody the physics of the underlying physical phenomena, to infer the effect of unobserved levels that have not been sampled yet. We show that physical descriptors can be intuitively embedded into the latent variable Gaussian process approach—a mixed-variable GP modeling technique—and used to selectively explore levels of qualitative variables in the Bayesian optimization framework. This physics-informed approach is particularly useful when one or more qualitative variables are high dimensional (many-level) and the modeling dataset is small, containing observations for only a subset of levels. Through a combination of mathematical test functions and materials design applications, our method is shown to be robust to certain types of incomplete domain knowledge and significantly reduces the design cost for problems with high-dimensional qualitative variables.