arXiv subjects

James M. Rondinelli

Publications and source records attributed to James M. Rondinelli.

At least 37 records · Page 2Linked to original sources

arXiv2025

Piezomagnetism-driven magnetoelectric coupling in altermagnetic multiferroic K3Cr2F7

Ferroelectric control of altermagnetism in momentum space has been studied widely, while the control of magnetism in real space of altermagnets are still rare. We present a design rule to identify multiferroicity in n=2 Ruddlesden-Popper halides. Our results show that a Jahn-Teller distortion can cooperate with oxygen octahedral rotations to break inversion symmetry, which we demonstrate in K3Cr2F7 and cation-ordered KAg2Cu2Cl7, and leads to a ferrielectric-to-ferroelectric phase transition in K3Cr2F7. Altermagnetic spin order in the ferrielectric phase of K3Cr2F7 transforms into a conventional antiferromagnetic order in the ferroelectric phase, at which strain/pressure engineered sizable changes of weak ferromagnetism can occur. Our study is not only conducive to realize strong magnetoelectric coupling in multiferroics, but also reveals more functionalities in altermagnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

Accelerating Discovery of Metal-Insulator Transition Compounds Using Physics-Informed Machine Learning

Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the complexity of underlying mechanisms, and the challenges of experimental validation. Here, we present a physics-informed machine learning framework that accelerates the discovery of thermally driven MIT materials. Using a trained classifier, we screen a crystal structure database to identify promising candidates for higher fidelity simulations. We focus on Ca$_2$Fe$_3$O$_8$, CaCo$_2$O$_4$, and CaMn$_2$O$_4$, and use density functional theory (DFT) to determine their electronic and magnetic ground states and assess their microscopic MIT mechanisms. We further apply machine learning regression models to estimate their transition temperatures and employ synthesis prediction tools to identify likely precursors and reaction routes. This integrated approach reduces the time and effort required to identify, understand, and synthesize new MIT materials, providing a generalizable pathway for accelerating correlated quantum materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data

The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular interest are emerging materials that display exceptional physical properties, making them promising candidates in energy-efficient microelectronic devices. As the conventional Edisonian approach becomes significantly outpaced by growing societal needs, emerging computational modeling and machine learning (ML) methods are employed for the rational design of materials. However, the complex physical mechanisms, cost of first-principles calculations, and the dispersity and scarcity of data pose challenges to both physics-based and data-driven materials modeling. Moreover, the combinatorial composition-structure design space is high-dimensional and often disjoint, making design optimization nontrivial. In this Account, we review a team effort toward establishing a framework that integrates data-driven and physics-based methods to address these challenges and accelerate materials design. We begin by presenting our integrated materials design framework and its three components in a general context. We then provide an example of applying this materials design framework to metal-insulator transition (MIT) materials, a specific type of emerging materials with practical importance in next-generation memory technologies. We identify multiple new materials which may display this property and propose pathways for their synthesis. Finally, we identify some outstanding challenges in data-driven materials design, such as materials data quality issues and property-performance mismatch. We seek to raise awareness of these overlooked issues hindering materials design, thus stimulating efforts toward developing methods to mitigate the gaps.

cond-mat.mtrl-sci↗

arXiv2024

Suppressed paramagnetism in amorphous Ta$_2$O$_{5-x}$ oxides and its link to superconducting qubit performance

Reduced transmon qubit $T_1$ coherence times have been linked to the amorphous oxide layers formed by thin film capacitors during processing. Because Ta or Ta capped Nb capacitors exhibit overall superior qubit performance to those fabricated with Nb capacitors, it has been hypothesized that the amorphous, non-stoichiometric Ta$_2$O$_{5-x}$ oxide is less lossy than its Nb$_2$O$_{5-x}$ counterpart. The origins of what makes amorphous Ta$_2$O$_{5-x}$ less susceptible to accepted decoherence channels is unknown. Here we establish the microscopic features of amorphous Nb$_2$O$_{5-x}$ and Ta$_2$O$_{5-x}$ using a combination of \textit{ab initio} molecular dynamics and density functional theory calculations. Our simulations establish that oxygen deficiency is less likely to occur in amorphous Ta$_2$O$_{5-x}$ than in Nb$_2$O$_{5-x}$ for $0\le x \le 0.25$ and that at a given level of oxygen deficiency the formation of metal Ta-Ta bonds is enhanced. These bonds, which are accommodated by structural flaws in the amorphous network, capture electrons better than in amorphous Nb$_2$O$_{5-x}$. These thermochemical differences quench or highly suppress magnetic moments in amorphous Ta$_2$O$_{5-x}$ and eliminate a potential source of quasiparticles and magnetic flux noise. Our calculations also show that hyperfine couplings between Nb nuclei and local magnetic moments in Nb$_2$O$_{5-x}$ could form "two-level systems" (TLS) or "two-level fluctuators" (TLF) with energy splittings of 100-1000 MHz or higher. This reveals a new TLS mechanism in amorphous Nb$_2$O$_{5-x}$ oxide layers that is unlikely in Ta$_2$O$_{5-x}$. Our work provides fundamental understanding of the materials chemistry and limitations imposed by native oxides of superconducting qubits, which can be used to guide materials selection and processing.

cond-mat.mtrl-sci↗

arXiv2024

Do Graph Neural Networks Work for High Entropy Alloys?

Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs), however, lack chemical long-range order, limiting the applicability of current graph representations. To overcome this challenge, we propose a representation of HEAs as a collection of local environment (LE) graphs. Based on this representation, we introduce the LESets machine learning model, an accurate, interpretable GNN for HEA property prediction. We demonstrate the accuracy of LESets in modeling the mechanical properties of quaternary HEAs. Through analyses and interpretation, we further extract insights into the modeling and design of HEAs. In a broader sense, LESets extends the potential applicability of GNNs to disordered materials with combinatorial complexity formed by diverse constituents and their flexible configurations.

cs.LG↗

arXiv2024

Effect of chemical short-range order and percolation on passivation in binary alloys

We develop a percolation model for face centered cubic binary alloys with chemical short-range order (SRO) to account for chemical ordering/clustering that occurs in nominally random solid solutions. We employ a lattice generation scheme that directly utilizes the first nearest neighbor Warren-Cowley SRO parameter to generate the lattice. We quantify the effects of SRO on the first nearest neighbor three-dimensional (3D) site percolation threshold using the large cell Monte Carlo renormalization group method and find that the 3D site percolation threshold is a function of the SRO parameter. We analyze the effects of SRO on the distribution of the total number of distinct clusters in the percolated structures and find that short-ranged clustering promotes the formation of a dominant spanning cluster. Furthermore, we find that the scaling exponents of percolation are independent of SRO. We also examine the effects of SRO on the 2D-3D percolation crossover and find that the thickness of the thin film for percolation crossover is a function of the SRO parameter. We combine these results to develop a percolation crossover model to understand the electrochemical passivation behavior in binary alloys. The percolation crossover model provides a theoretical framework to understand the critical composition of passivating elements for protective oxide formation. With this model, we show that SRO can be used as a processing parameter to improve corrosion resistance.

cond-mat.mtrl-sci↗

arXiv2024

Non-relativistic spin splitting in compensated magnets that are not altermagnets

The non-relativistic spin-splitting (NRSS) of electronic bands in "altermagnets" has sparked renewed interest in antiferromagnets (AFMs) that have no net magnetization. However, altermagnets with collinear and compensated magnetism are not the only type of NRSS AFMs. In this study, we identify the symmetry conditions and characteristic signatures of a distinct group of NRSS AFMs that go beyond the description of altermagnets. These compounds exhibit a broken spin-degeneracy among the spin-polarized bands at the $Γ$ point in the absence of spin-orbit coupling (SOC). We use density functional theory calculations to validate these models in ternary magnetic nitrides, specifically MnXN$_2$ (X = Si, Ge, Sn), and their cation ordered variants. By removing the previous NRSS constraint on $Γ$, these compounds may facilitate the generation of spin currents without cancellation arising from the alternating spin polarizations. Our findings expand the scope of NRSS eligible materials.

cond-mat.mtrl-sci↗

arXiv2024

Peierls-like distortion drives anion ordering in rutile TiOF

We use first principles density functional theory calculations to examine the effect of multiple anions on the Peierls distortion in the rutile oxyfluoride TiOF. By using a structure enumeration approach, we obtain the ground state atomic structure for TiOF and identify the driving forces behind the experimentally observed two-dimensional anion ordering along rutile (110) planes. We find that adjacent edge-connected octahedra comprise like atoms with an --O-O/F-F/O-O/F-F-- pattern along the rutile [001] direction. This anion pattern coexists with a Peierls-like distortion leading to the formation of a singlet state between neighboring Ti$^{3+}$ cations. We show that the anion ordering arises from competition between electrostatic interactions, owing to the Ti-F cation-anion pairs, and the tendency of the $d^1$ Ti$^{3+}$ cation to form Ti-Ti dimers, characterized by increased metal-metal bonding. We find that the addition of strong on-site Coulombic interactions to the Ti $d$ manifold suppresses the formation of the singlet state. By increasing the correlation strength, we uncover two first-order phase transitions: first, from a nonmagnetic insulator to a ferromagnetic half-metal, and then second to a ferromagnetic insulator. Last, we show that the electronic configuration of the transition metal cation in rutile oxyfluorides is responsible for the observed anion order, enabling design of ordered heteroanionic materials exhibiting collective phenomena through cation sublattice control.

cond-mat.mtrl-sci↗

arXiv2024

Systematic Improvements in Transmon Qubit Coherence Enabled by Niobium Surface Encapsulation

We present a novel transmon qubit fabrication technique that yields systematic improvements in T$_1$ relaxation times. We fabricate devices using an encapsulation strategy that involves passivating the surface of niobium and thereby preventing the formation of its lossy surface oxide. By maintaining the same superconducting metal and only varying the surface structure, this comparative investigation examining different capping materials, such as tantalum, aluminum, titanium nitride, and gold, and film substrates across different qubit foundries definitively demonstrates the detrimental impact that niobium oxides have on the coherence times of superconducting qubits, compared to native oxides of tantalum, aluminum or titanium nitride. Our surface-encapsulated niobium qubit devices exhibit T$_1$ relaxation times 2 to 5 times longer than baseline niobium qubit devices with native niobium oxides. When capping niobium with tantalum, we obtain median qubit lifetimes above 300 microseconds, with maximum values up to 600 microseconds, that represent the highest lifetimes to date for superconducting qubits prepared on both sapphire and silicon. Our comparative structural and chemical analysis suggests why amorphous niobium oxides may induce higher losses compared to other amorphous oxides. These results are in line with high-accuracy measurements of the niobium oxide loss tangent obtained with ultra-high Q superconducting radiofrequency (SRF) cavities. This new surface encapsulation strategy enables even further reduction of dielectric losses via passivation with ambient-stable materials, while preserving fabrication and scalable manufacturability thanks to the compatibility with silicon processes.

quant-ph↗

arXiv2024

Hard X-ray Generation and Detection of Nanometer-Scale Localized Coherent Acoustic Wave Packets in SrTiO$_3$ and KTaO$_3$

We demonstrate that the absorption of femtosecond x-ray pulses can excite quasi-spherical high-wavevector coherent acoustic phonon wavepackets using an all x-ray pump and probe scattering experiment. The time- and momentum-resolved diffuse scattering signal is consistent with strain pulses induced by the rapid electron cascade dynamics following photoionization at uncorrelated excitation centers. We quantify key parameters of this process, including the localization size of the strain wavepacket and the energy absorption efficiency, which are determined by the photoelectron and Auger electron cascade dynamics, as well as the electron-phonon interaction. In particular, we obtain the localization size of the observed strain wave packet to be 1.5 and 2.5 nm for bulk SrTiO$_3$ and KTaO$_3$ single crystals, even though there are no nanoscale structures or light-intensity patterns that would ordinarily be required to generate acoustic waves of wavelengths much shorter than the penetration depth. Whereas in GaAs and GaP we do not observe a signal above background. The results provide crucial information on x-ray matter interactions, which sheds light on the mechanism of x-ray energy deposition, and the study of high wavevector acoustic phonons and thermal transport at the nanoscale.

cond-mat.mtrl-sci↗

arXiv2023

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling

Recent advances in machine learning (ML) have expedited materials discovery and design. One significant challenge faced in ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here we present MolSets, a specialized ML model for molecular mixtures. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecule level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in virtual screening of the combinatorial chemical space.

cs.LG↗

arXiv2023

Informatics-based learning of oxygen vacancy ordering principles in oxygen-deficient perovskites

Ordered oxygen vacancies (OOVs) in perovskites can exhibit long-range order and may be used to direct materials properties through modifications in electronic structures and broken symmetries. Based on the various vacancy patterns observed in previously known compounds, we explore the ordering principles of OOVs in oxygen-deficient perovskite oxides with $AB\mathrm{O}_{2.5}$ stoichiometry to identify other OOV variants. We performed first-principles calculations to assess the OOV stability on a dataset of 50 OOV structures generated from our bespoke algorithm. The algorithm employs uniform planar vacancy patterns on (111) pseudocubic perovskite layers and the approach proves effective for generating stable OOV patterns with minimal computational loads. We find as expected that the major factors determining the stability of OOV structures include coordination preferences of transition metals and elastic penalties resulting from the assemblies of polyhedra. Cooperative rotational modes of polyhedra within OOV structures reduce elastic instabilities by optimizing the bond valence of $A$- and $B$-cations. This finding explains the observed formation of vacancy channels along low-index crystallographic directions in prototypical OOV phases. The identified ordering principles enable us to devise other stable vacancy patterns with longer periodicity for targeted property design in yet to be synthesized compounds.

cond-mat.mtrl-sci↗

arXiv2023

Thermal Magnetoelectrics in all Inorganic Quasi-Two-Dimensional Halide Perovskites

From lithium-ion batteries to high-temperature superconductors, oxide materials have been widely used in electronic devices. However, demands of future technologies require materials beyond oxides, as anion chemistries distinct from oxygen can expand the palette of mechanisms and phenomena, to achieve superior functionalities. Examples include nitride-based wide bandgap semiconductors and halide perovskite solar cells, with MAPbBr3 being a representation revolutionizing photovoltaics research. Here, we demonstrate magnetoelectric behaviour in quasi-two-dimensional halides (K,Rb)3Mn2Cl7 through simultaneous thermal control of electric and magnetic polarizations by exploiting a polar-to-antipolar displacive transition. Additionally, our calculations indicate a possible polarization switching path including a strong magnetoelectric coupling, indicating halides can be excellent platforms to design future multiferroic and ferroelectric devices. We expect our findings to broaden the exploration of multiferroics to non-oxide materials and open access to novel mechanisms, beyond conventional electric/magnetic control, for coupling ferroic orders.

cond-mat.mtrl-sci↗

arXiv2023

Canted Antiferromagnetism in Polar MnSiN$_2$ with High Néel Temperature

MnSiN$_2$ is a transition metal nitride with Mn and Si ions displaying an ordered distribution on the cation sites of a distorted wurtzite-derived structure. The Mn$^{2+}$ ions reside on a 3D diamond-like covalent network with strong superexchange pathways. We simulate its electronic structure and find that the N anions in MnSiN$_2$ act as $σ$- and $π$-donors, which serve to enhance the N-mediated superexchange, leading to the high Néel ordering temperature of $T_N$ = 443 K. Polycrystalline samples of MnSiN$_2$ were prepared to reexamine the magnetic structure and resolve previously reported discrepancies. An additional magnetic canting transition is observed at $T_\mathrm{cant}$ = 433 K and the precise canted ground state magnetic structure has been resolved using a combination of DFT calculations and powder neutron diffraction. The calculations favor a $G$-type antiferromagnetic spin order with lowering to $Pc^\prime$. Irreducible representation analysis of the magnetic Bragg peaks supports the lowering of the magnetic symmetry. The computed model includes a 10$^\circ$ rotation of the magnetic spins away from the crystallographic $c$-axis consistent with measured powder neutron diffraction data modeling and a small canting of 0.6$^\circ$.

cond-mat.mtrl-sci↗

arXiv2023

Heteroanionic Stabilization of Ni$^{1+}$ with Nonplanar Coordination in Layered Nickelates

We present electronic structure calculations on layered nickelate oxyfluorides derived from the Ruddlesden-Popper arisotype structure in search of unidentified materials that may host nickelate superconductivity. By performing anion exchange of oxygen with fluorine, we create two heteroanionic La$_2$NiO$_3$F polymorphs and stabilize Ni$^{1+}$ in 4-coordinate and 5-coordinate square planar and square pyramidal geometries, respectively. We further predict chemical reactions with high thermodynamic driving forces to guide their synthesis. These oxyfluorides are weakly correlated antiferromagnetic insulators and their nonmagnetic phases exhibit quasi-2D Fermi surfaces dominated by Ni $d_{x^2-y^2}$ states, which strikingly resemble undoped cuprate superconductors. We discuss how the oxyfluoride anion chemistry promotes Ni-O covalency and single-band character that is more similar to the cuprates than homoanionic infinite-layer nickelates. We use our understanding to propose doping strategies and layered LaSrNiO$_2$F$_2$ and La$_3$Ni$_2$O$_4$F$_3$ nickelate oxyfluorides with tunable electronic and magnetic structures for experimentation.

cond-mat.mtrl-sci↗

arXiv2023

ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

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.

cond-mat.mtrl-sci↗

arXiv2023

Quantum sensing of magnetic fields with molecular color centers

Molecular color centers, such as $S=1$ Cr($o$-tolyl)$_{4}$, show promise as an adaptable platform for magnetic quantum sensing. Their intrinsically small size, i.e., 1-2 nm, enables them to sense fields at short distances and in various geometries. This feature, in conjunction with tunable optical read-out of spin information, offers the potential for molecular color centers to be a paradigm shifting materials class beyond diamond-NV centers by accessing a distance scale opaque to NVs. This capability could, for example, address ambiguity in the reported magnetic fields arising from two-dimensional magnets by allowing for a single sensing technique to be used over a wider range of distances. Yet, so far, these abilities have only been hypothesized with theoretical validation absent. We show through simulation that Cr($o$-tolyl)$_{4}$ can spatially resolve proximity-exchange versus direct magnetic field effects from monolayer CrI$_{3}$ by quantifying how these interactions impact the excited states of the molecule. At short distances, proximity exchange dominates through molecule-substrate interactions, but at further distances the molecule behaves as a typical magnetic sensor, with magnetostatic effects dominating changes to the energy of the excited state. Our models effectively demonstrate how a molecular color center could be used to measure the magnetic field of a 2D magnet and the role different distance-dependent interactions contribute to the measured field.

cond-mat.mtrl-sci↗

arXiv2022

Polar Metals Taxonomy for Materials Classification and Discovery

Over the past decade, materials that combine broken inversion symmetry with metallic conductivity have gone from a thought experiment to one of the fastest growing research topics. In 2013, the observation of the first uncontested polar transition in a metal, LiOsO$_3$, inspired a surge of theoretical and experimental work on the subject, uncovering a host of materials which combine properties previously thought to be contraindicated [Nat. Mater. \textbf{12}, 1024 (2013)]. As is often the case in a nascent field, the sudden rise in interest has been accompanied by diverse (and sometimes conflicting) terminology. Although ``ferroelectric-like" metals are well-defined in theory, i.e., materials that undergo a symmetry-lowering transition to a polar phase while exhibiting metallic electron transport, real materials find a myriad of ways to push the boundaries of this definition. Here, we review and explore the burgeoning polar metal frontier from the perspectives of theory, simulation, and experiment while introducing a unified taxonomy. The framework allows one to describe, identify, and classify polar metals; we also use it to discuss some of the fundamental tensions between theory and models of reality inherent in the terms ``ferroelectric" and ``metals.'' In addition, we highlight shortcomings of electrostatic doping simulations in modeling different subclasses of polar metals, noting how the assumptions of this approach depart from experiment. We include a survey of known materials that combine polar symmetry with metallic conductivity, classified according to the mechanisms used to harmonize those two orders and their resulting properties. We conclude by describing opportunities for the discovery of novel polar metals by utilizing our taxonomy.

cond-mat.mtrl-sci↗