arXiv subjects

Francesco Ricci

Publications and source records attributed to Francesco Ricci.

At least 37 records · Page 2Linked to original sources

arXiv2021

Giant thermoelectric figure of merit in multivalley high-complexity-factor LaSO

We report a giant thermoelectric figure of merit $ZT$ (up to 6 at 1100 K) in $n$-doped lanthanum oxysulphate LaSO. Thermoelectric coefficients are computed from ab initio bands within Bloch-Boltzmann theory in an energy-, chemical potential- and temperature-dependent relaxation time approximation. The lattice thermal conductivity is estimated from a model employing the ab initio phonon and Grüneisen-parameter spectrum. The main source of the large $ZT$ is the significant power factor which correlates with a large band complexity factor. We also suggest a possible $n$-type dopant for the material based on ab initio calculations.

cond-mat.mtrl-sci↗

arXiv2021

OPTIMADE, an API for exchanging materials data

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

cond-mat.mtrl-sci↗

arXiv2021

Graph Learning based Recommender Systems: A Review

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. Differently from other RS approaches, including content-based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs are a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area. Finally, we share some new research directions in this vibrant area.

cs.IR↗

arXiv2021

Selective control of reconfigurable chiral plasmonic metamolecules

Selective configuration control of plasmonic nanostructures using either top-down or bottom-up approaches has remained challenging in the field of active plasmonics. We demonstrate the realization of DNA-assembled reconfigurable plasmonic metamolecules, which can respond to a wide range of pH changes in a programmable manner. This programmability allows for selective reconfiguration of different plasmonic metamolecule species coexisting in solution through simple pH tuning. This approach enables discrimination of chiral plasmonic quasi-enantiomers and arbitrary tuning of chiroptical effects with unprecedented degrees of freedom. Our work outlines a new blueprint for implementation of advanced active plasmonic systems, in which individual structural species can be programmed to perform multiple tasks and functions in response to independent external stimuli.

physics.optics↗

arXiv2020

A three-order-parameter bistable magnetoelectric multiferroic metal

Using first-principles calculations we predict that the layered-perovskite metal Bi$_5$Mn$_5$O$_{17}$ is a ferromagnet, ferroelectric, and ferrotoroid which may realize the long sought-after goal of a room-temperature ferromagnetic single-phase multiferroic with large, strongly coupled, primary-order polarization and magnetization. Bi$_5$Mn$_5$O$_{17}$ has two nearly energy-degenerate ground states with mutually orthogonal vector order parameters (polarization, magnetization, ferrotoroidicity), which can be rotated globally by switching between ground states. Giant cross-coupling magnetoelectric and magnetotoroidic effects, as well as optical non-reciprocity, are thus expected. Importantly, Bi$_5$Mn$_5$O$_{17}$ should be thermodynamically stable in O-rich growth conditions, and hence experimentally accessible.

cond-mat.mtrl-sci↗

arXiv2020

Structure motif centric learning framework for inorganic crystalline systems

Incorporation of physical principles in a network-based machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for materials science and condensed matter physics. In this work, as inspired by the Pauling rule, we propose that structure motifs (polyhedral formed by cations and surrounding anions) in inorganic crystals can serve as a central input to a machine learning framework for crystalline inorganic materials. Taking metal oxides as examples, we demonstrated that, an unsupervised learning algorithm Motif2Vec is able to convert the presence of structure motifs and their connections in a large set of crystalline compounds into unique vector representations. The connections among complex materials can be largely determined by the presence of different structure motifs and their clustering information are identified by our Motif2Vec algorithm. To demonstrate the novel use of structure motif information, we show that a motif-centric learning framework can be effectively created by combining motif information with the recently developed atom-based graph neural networks to form an atom-motif dual graph network (AMDNet). Taking advantage of node and edge information on both atomic and motif level, the AMDNet is more accurate than an atom graph network in predicting electronic structure related material properties of metal oxides such as band gaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex material properties by incorporating beyond-atom physical principles.

cond-mat.mtrl-sci↗

arXiv2020

Graph Learning Approaches to Recommender Systems: A Review

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics and popularity for Recommender Systems (RS). Differently from conventional RS, including content based filtering and collaborative filtering, GLRS are built on simple or complex graphs where various objects, e.g., users, items, and attributes, are explicitly or implicitly connected. With the rapid development of graph learning, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building advanced RS. In this paper, we provide a systematic review of GLRS, on how they obtain the knowledge from graphs to improve the accuracy, reliability and explainability for recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges in this new research area. Then, we survey the most recent and important developments in the area. Finally, we share some new research directions in this vibrant area.

cs.IR↗

arXiv2019

Searching for Materials with High Refractive Index and Wide Band Gap: A First-Principles High-Throughput Study

Materials combining both a high refractive index and a wide band gap are of great interest for optoelectronic and sensor applications. However, these two properties are typically described by an inverse correlation with high refractive index appearing in small gap materials and vice-versa. Here, we conduct a first-principles high-throughput study on more than 4000 semiconductors (with a special focus on oxides). Our data confirm the general inverse trend between refractive index and band gap but interesting outliers are also identified. The data are then analyzed through a simple model involving two main descriptors: the average optical gap and the effective frequency. The former can be determined directly from the electronic structure of the compounds, but the latter cannot. This calls for further analysis in order to obtain a predictive model. Nonetheless, it turns out that the negative effect of a large band gap on the refractive index can counterbalanced in two ways: (i) by limiting the difference between the direct band gap and the average optical gap which can be realized by a narrow distribution in energy of the optical transitions and (ii) by increasing the effective frequency which can be achieved through either a high number of transitions from the top of the valence band to the bottom of the conduction or a high average probability for these transitions. Focusing on oxides, we use our data to investigate how the chemistry influences this inverse relationship and rationalize why certain classes of materials would perform better. Our findings can be used to search for new compounds in many optical applications both in the linear and non-linear regime (waveguides, optical modulators, laser, frequency converter, etc.).

cond-mat.mtrl-sci↗

arXiv2019

Optimal Feedback Cooling of a Charged Levitated Nanoparticle with Adaptive Control

We use an optimal control protocol to cool one mode of the center of mass motion of an optically levitated nanoparticle. The feedback technique relies on exerting a Coulomb force on a charged particle with a pair of electrodes and follows the control law of a linear quadratic regulator, whose gains are optimized by a machine learning algorithm in under 5 s. With a simpler and more robust setup than optical feedback schemes, we achieve a minimum center of mass temperature of 5 mK at $3\times 10^{-7}$ mbar and transients 10 to 600 times faster than cold damping. This cooling technique can be easily extended to 3D cooling and is particularly relevant for studies demanding high repetition rates and force sensing experiments with levitated objects.

physics.ins-det↗

arXiv2019

Accurate mass measurement of a levitated nanomechanical resonator for precision force sensing

Nanomechanical resonators are widely operated as force and mass sensors with sensitivities in the zepto-Newton and yocto-gram regime, respectively. Their accuracy, however, is usually undermined by high uncertainties in the effective mass of the system, whose estimation is a non-trivial task. This critical issue can be addressed in levitodynamics, where the nanoresonator typically consists of a single silica nanoparticle of well-defined mass. Yet, current methods assess the mass of the levitated nanoparticles with uncertainties up to a few tens of percent, therefore preventing to achieve unprecedented sensing performances. Here, we present a novel measurement protocol that uses the electrical field from a surrounding plate capacitor to directly drive a charged optically levitated particle in moderate vacuum. The developed technique estimates the mass within a statistical error below 1% and a systematic error of 2%, and paves the way toward more reliable sensing and metrology applications of levitodynamics systems.

physics.ins-det↗

arXiv2018

Computationally-driven, high throughput identification of CaTe and Li$_\textrm{3}$Sb as promising candidates for high mobility $p$-type transparent conducting materials

High-performance $p$-type transparent conducting materials (TCMs) must exhibit a rare combination of properties including high mobility, transparency and $p$-type dopability. The development of high-mobility/conductivity $p$-type TCMs is necessary for many applications such as solar cells, or transparent electronic devices. Oxides have been traditionally considered as the most promising chemical space to dig out novel $p$-type TCMs. However, non-oxides might perform better than traditional $p$-type TCMs (oxides) in terms of mobility. We report on a high-throughput (HT) computational search for non-oxide $p$-type TCMs from a large dataset of more than 30,000 compounds which identified CaTe and Li$_\textrm{3}$Sb as very good candidates for high-mobility $p$-type TCMs. From our calculations, both compounds are expected to be $p$-type dopable: intrinsically for Li$_\textrm{3}$Sb while CaTe would require extrinsic doping. Using electron-phonon computations, we estimate hole mobilities at room-temperature to be about 20 and 70 cm$^2$/Vs for CaTe and Li$_\textrm{3}$Sb, respectively. The computed hole mobility for Li$_\textrm{3}$Sb is quite exceptional and comparable with the electron mobility in the best $n$-type TCMs.

cond-mat.mtrl-sci↗

arXiv2018

Calibration and temperature measurement of levitated optomechanical sensors

Optically levitated nanoparticles offer enormous potential for precision sensing. However, as for any other metrology device, the absolute measurement performance of a levitated-particle sensor is limited by the accuracy of the calibration relating the measured signal to an absolute displacement of the particle. Here, we suggest and demonstrate calibration protocols for levitated-nanoparticle sensors. Our calibration procedures include the treatment of anharmonicities in the trapping potential, as well as a protocol using a harmonic driving force, which is applicable if the sensor is coupled to a heat bath of unknown temperature. Finally, using the calibration, we determine the center-of-mass temperature of an optically levitated particle in thermal equilibrium from its motion, and discuss the optimal measurement time required to determine said temperature.

physics.optics↗

arXiv2018

High-Throughput Identification of Electrides from all Known Inorganic Materials

In this paper, we present the results of a large-scale, high-throughput computational search for electrides among all known inorganic materials. Analyzing a database of density functional theory results on more than 60,000 compounds, we identify 69 new electride candidates. We report on all these candidates and discuss the structural and chemical factors leading to electride formation. Among these candidates, our work identifies the first partially-filled 3d transition metal containing electrides Ba3CrN3 and Sr3CrN3; an unexpected finding that contravenes conventional chemistry.

cond-mat.mtrl-sci↗

arXiv2017

Two-structure thermodynamics for the TIP4P/2005 model of water covering supercooled and deeply stretched regions

One of the most promising frameworks for understanding the anomalies of cold and supercooled water postulates the existence of two competing, interconvertible local structures. If the non-ideality in the Gibbs energy of mixing overcomes the ideal entropy of mixing of these two structures, a liquid-liquid phase transition, terminated at a liquid-liquid critical point, is predicted. Various versions of the "two-structure equation of state" (TSEOS) based on this concept have shown remarkable agreement with both experimental data for metastable, deeply supercooled water and simulations of molecular water models. However, existing TSEOSs were not designed to describe the negative pressure region and do not account for the stability limit of the liquid state with respect to the vapor. While experimental data on supercooled water at negative pressures may shed additional light on the source of the anomalies of water, such data are very limited. To fill this gap, we have analyzed simulation results for TIP4P/2005, one of the most accurate classical water models available. We have used recently published simulation data, and performed additional simulations, over a broad range of positive and negative pressures, from ambient temperature to deeply supercooled conditions. We show that, by explicitly incorporating the liquid-vapor spinodal into a TSEOS, we are able to match the simulation data for TIP4P/2005 with remarkable accuracy. In particular, this equation of state quantitatively reproduces the lines of extrema in density, isothermal compressibility, and isobaric heat capacity. Contrary to an explanation of the thermodynamic anomalies of water based on a "retracing spinodal", the liquid-vapor spinodal in the present TSEOS continues monotonically to lower pressures upon cooling, influencing but not giving rise to density extrema and other thermodynamic anomalies.

cond-mat.stat-mech↗

arXiv2017

Optically levitated nanoparticle as a model system for stochastic bistable dynamics

Nano-mechanical resonators have gained an increasing importance in nanotechnology owing to their contributions to both fundamental and applied science. Yet, their small dimensions and mass raises some challenges as their dynamics gets dominated by nonlinearities that degrade their performance, for instance in sensing applications. Here, we report on the precise control of the nonlinear and stochastic bistable dynamics of a levitated nanoparticle in high vacuum. We demonstrate how it can lead to efficient signal amplification schemes, including stochastic resonance. This work paves the way for the use of levitated nanoparticles as a model system for stochastic bistable dynamics, with applications to a wide variety of fields.

physics.optics↗

arXiv2016

A Symbolic Method for the Analysis of a Nonlinear Two-Mass-Skate Model

Multibody systems usually give rise to complex nonlinear dynamics, and the bicycle is not an exception. Even a simple model as the Two-Mass-Skate presents a long expression of the kinetic energy, making difficult to write explicitly the equations of motion. Instead of linearising or approximating the model, we will overcome this issue by using a functional expression of the kinetic energy. With introduction of appropriate nonlinear functions, the equations of motion are written in a form that can be easily handled despite their complexity. A stability analysis of the dynamics is then conducted.

physics.class-ph↗

arXiv2015

Prediction of a native ferroelectric metal

The possibility that metals may support ferroelectricity is an open issue. Anderson and Blount showed that certain martensitic transitions involve inversion symmetry breaking and the formal existence of a polar axis, so 'metallic ferroelectric' behavior has been claimed for metals undergoing a centrosymmetric (CS) to non-CS structural transformation (Cd2ReO7, LiOsO3) or natively non-CS (SrCaRu2O), or for ferroelectric insulators whose polar distortion survives moderate metallicity induced by doping or proximity. However, none of these systems, nor any other to our knowledge, embodies a truly ferroelectric metal with native switchable polarization and native metallicity coexisting in a single phase. Here we report the first-ever theoretical prediction of such a material. By first-principles calculations, we show that the layered perovskite Bi5Ti5O17 has a non-zero density of states at the Fermi level and metal-like conductivity, as well as a spontaneous polarization in zero field. Further, we predict that the polarization of Bi5Ti5O17 is switchable both in principle, as the material complies with the sufficient symmetry requirements, and in practice, as Bi5Ti5O17 can sustain a sizable potential drop along the polar direction, as needed to revert its polarization by application of an electric bias.

cond-mat.mtrl-sci↗

arXiv2015

Properties of (Ga$_{1-x}$In$_x$)$_2$O$_3$ over the whole $x$ range

Using density-functional ab initio theoretical techniques, we study (Ga$_{1-x}$In$_x$)$_2$O$_3$ in both its equilibrium structures (monoclinic $β$ and bixbyite) and over the whole range of composition. We establish that the alloy exhibits a large and temperature-independent miscibility gap. On the low-$x$ side, the favored phase is isostructural with $β$-Ga$_2$O$_3$; on the high-$x$ side, it is isostructural with bixbyite In$_2$O$_3$. The miscibility gap opens between approximately 15\% and 55\% In content for the bixbyite alloy grown epitaxially on In$_2$O$_3$, and 15\% and 85\% In content for the free-standing bixbyite alloy. The gap, volume and band offsets to the parent compound also exhibit anomalies as function of $x$. Specifically, the offsets in epitaxial conditions are predominantly type-B staggered, but have opposite signs in the two end-of-range phases.

cond-mat.mtrl-sci↗