BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Stefano Sanvito

Stefano Sanvito contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-scicond-mat.mes-hallquant-phphysics.comp-phArtificial Intelligencecond-mat.str-elcond-mat.supr-conphysics.atm-clusphysics.chem-ph

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
14works
0followers
9topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

14 published item(s)

preprint2026arXiv

Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications

Autoresearch offers a flexible paradigm for automating scientific tasks, in which an AI agent proposes, implements, evaluates, and refines candidate solutions against a quantitative objective. Here, we use composition-based materials-property prediction to test whether such agents can perform a task beyond model selection and hyperparameter optimization: the design of input descriptors. We introduce Automat, an autoresearch framework where a coding agent based on a large language model generates composition-only descriptors for chemical compounds and evaluates them using a random forest workflow. The agent is restricted to information derivable from chemical formulas and iteratively proposes, implements, and tests chemically motivated descriptor strategies. We apply Automat, with OpenAI Codex using GPT-5.5 as the coding agent, to the prediction of experimental band gaps in inorganic materials and Curie temperatures in ferromagnetic compounds. In both tasks, Automat improves over fractional-composition, Magpie, and combined fractional-composition/Magpie baselines, while producing descriptor families that are chemically interpretable. These results provide a demonstration that autoresearch agents can generate competitive, task-specific materials descriptors without manual feature engineering during the run. They also reveal current limitations, including descriptor redundancy, sensitivity to greedy feature expansion, and the need for explicit complexity control, descriptor pruning, and more sophisticated search strategies.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A spectral-neighbour representation for vector fields: machine-learning potentials including spin

We introduce a translational and rotational invariant local representation for vector fields, which can be employed in the construction of machine-learning energy models of solids and molecules. This allows us to describe, on the same footing, the energy fluctuations due to the atomic motion, the longitudinal and transverse excitations of the vector field, and their mutual interplay. The formalism can then be applied to physical systems where the total energy is determined by a vector density, as in the case of magnetism. Our representation is constructed over the power spectrum of the combined angular momentum describing the local atomic positions and the vector field, and can be used in conjunction with different machine-learning schemes and data taken from accurate ab initio electronic structure theories. We demonstrate the descriptive power of our representation for a range of classical spin Hamiltonian and machine-learning algorithms. In particular, we construct energy models based on both linear Ridge regression, as in conventional spectral neighbour analysis potentials, and gaussian approximation. These are both built to represent a Heisenberg-type Hamiltonian including a longitudinal energy term and spin-lattice coupling.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Data-Driven Time Propagation of Quantum Systems with Neural Networks

We investigate the potential of supervised machine learning to propagate a quantum system in time. While Markovian dynamics can be learned easily, given a sufficient amount of data, non-Markovian systems are non-trivial and their description requires the memory knowledge of past states. Here we analyse the feature of such memory by taking a simple 1D Heisenberg model as many-body Hamiltonian, and construct a non-Markovian description by representing the system over the single-particle reduced density matrix. The number of past states required for this representation to reproduce the time-dependent dynamics is found to grow exponentially with the number of spins and with the density of the system spectrum. Most importantly, we demonstrate that neural networks can work as time propagators at any time in the future and that they can be concatenated in time forming an autoregression. Such neural-network autoregression can be used to generate long-time and arbitrary dense time trajectories. Finally, we investigate the time resolution needed to represent the system memory. We find two regimes: for fine memory samplings the memory needed remains constant, while longer memories are required for coarse samplings, although the total number of time steps remains constant. The boundary between these two regimes is set by the period corresponding to the highest frequency in the system spectrum, demonstrating that neural network can overcome the limitation set by the Shannon-Nyquist sampling theorem.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Fe- and Co-based magnetic tunnel junctions with AlN and ZnO spacers

AlN and ZnO, two wide band-gap semiconductors extensively used in the display industry, crystallise in the wurtzite structure, which can favour the formation of epitaxial interfaces to close-packed common ferromagnets. Here we explore these semiconductors as material for insulating barriers in magnetic tunnel junctions. In particular, the {\it ab initio} quantum transport code {\it Smeagol} is used to model the $X$[111]/$Y$[0001]/$X$[111] ($X=$ Co and Fe, $Y=$ AlN and ZnO) family of junctions. Both semiconductors display a valance-band top with $p$-orbital character, while the conduction band bottom exhibits $s$-type symmetry. The smallest complex-band decay coefficient in the forbidden energy-gap along the [0001] direction is associated with the $Δ_1$ symmetry, and connects across the band gap at the $Γ$ point in 2D Brillouin zones. This feature enables spin filtering and may result in a large tunnelling magnetoresistance. In general, we find that Co-based junctions present limited spin filtering and little magnetoresistance at low bias, since both spin sub-bands cross the Fermi level with $Δ_1$ symmetry. This contrasts the situation of Fe, where only the minority $Δ_1$ band is available. However, even in the case of Fe the magnitude of the magnetoresistance at low bias remains relatively small, mostly due to conduction away from the $Γ$ point and through complex bands with symmetry different than $Δ_1$. The only exception is for the Fe/AlN/Fe junction, where we predict a magnetoresitance of around 1,000\% at low bias.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Inversion of the chemical environment representations

Machine-learning generative methods for material design are constructed by representing a given chemical structure, either a solid or a molecule, over appropriate atomic features, generally called structural descriptors. These must be fully descriptive of the system, must facilitate the training process and must be invertible, so that one can extract the atomic configurations corresponding to the output of the model. In general, this last requirement is not automatically satisfied by the most efficient structural descriptors, namely the representation is not directly invertible. Such drawback severely limits our freedom of choice in selecting the most appropriate descriptors for the problem, and thus our flexibility to construct generative models. In this work, we present a general optimization method capable of inverting any local many-body descriptor of the chemical environment, back to a cartesian representation. The algorithm is then implemented together with the bispectrum representation of the local structure and demonstrated for a number of molecules. The scheme presented here, thus, represents a general approach to the inversion of structural descriptors, enabling the construction of efficient structural generative models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Spin transfer torque in Mn$_3$Ga-based ferrimagnetic tunnel junctions from first principles

We report on first-principles calculations of spin-transfer torque (STT) in epitaxial magnetic tunnel junctions (MTJs) based on ferrimagnetic tetragonal Mn$_3$Ga electrodes, both as analyzer in an Fe/MgO stack, and also in an analogous stack with a second Mn$_3$Ga electrode (instead of Fe) as polarizer. Solving the ballistic transport problem (NEGF + DFT) for the nonequilibrium spin density in a scattering region extended to over 7.6 nm into the Mn$_3$Ga electrode, we find long-range spatial oscillations of the STT decaying on a length scale of a few tens of angstroms, both in the linear response regime and for finite bias. The oscillatory behavior of the STT in Mn$_3$Ga is robust against variations in the stack geometry and the applied bias voltage, which may affect the phase and the amplitude of the spatial oscillation, but the wave number is only responsive to variations in the longitudinal lattice constant of Mn$_3$Ga (for fixed in-plane geometry) without being commensurate with the lattice. Our interpretation of the long-range STT oscillations is based on the bulk electronic structure of Mn$_3$Ga, taking also into account the spin-filtering properties of the MgO barrier. Comparison to a fully Mn$_3$Ga-based stack shows similar STT oscillations, but a significant enhancement of both the TMR effect at the Fermi level and the STT at the interface, due to resonant tunneling for the mirror-symmetric junction with thinner barrier (three monoatomic layers). From the calculated energy dependence of the spin-polarized transmissions at 0 V, we anticipate asymmetric or symmetric TMR as a function of the applied bias voltage for the Fe-based and the all-Mn$_3$Ga stacks, respectively, which also both exhibit a sign change below 1 V. In the latter (symmetric) case we expect a TMR peak at zero, which is larger for the thinner barriers because of a spin-polarized resonant tunneling contribution.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Transition between large and small electron polaron at neutral ferroelectric domain walls in BiFeO$_3$

Ferroelectric domain walls are planes within an insulating material that can accumulate and conduct charge carriers, hence the interaction of the domain walls with the charge carriers can be important for photovoltaic and other electronic applications. By means of first principles calculations we predict a transition from a large two-dimensional electron polaron to a small polaron at the domain walls at a critical electron density, with polaron signatures in optical absorption and photoluminescence. We find that large and small polarons at the domain walls create different absorption peaks within the band gap that are not present in the case of pristine domain walls. These are an extended Drude peak in the case of large electron or hole polarons and a narrow mid-gap peak in the case of the small electron polaron.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

On the conservation of the angular momentum in ultrafast spin dynamics

The total angular momentum of a close system is a conserved quantity, which should remain constant in time for any excitation experiment once the pumping signal has extinguished. Such conservation, however, is never satisfied in practice in any real-time first principles description of the demagnetization process. Furthermore, there is a growing experimental evidence that the same takes place in experiments. The missing angular momentum is usually associated to lattice vibrations, which are not measured experimentally and are never considered in real-time simulations. Here we critically analyse the issue and conclude that current state-of-the-art simulations violate angular momentum conservation already at the electronic level of description. This shortcoming originates from an oversimplified description of the spin-orbit coupling, which includes atomic contributions but neglects completely that of itinerant electrons. We corroborate our findings with time-dependent simulations using model tight-binding Hamiltonians, and show that indeed such conservation can be re-introduced by an appropriate choice of spin-orbit coupling. The consequences of our findings on recent experiments are also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Edge superconductivity in Multilayer WTe2 Josephson junction

WTe2, as a type-II Weyl semimetal, has 2D Fermi arcs on the (001) surface in the bulk and 1D helical edge states in its monolayer. These features have recently attracted wide attention in condensed matter physics. However, in the intermediate regime between the bulk and monolayer, the edge states have not been resolved owing to its closed band gap which makes the bulk states dominant. Here, we report the signatures of the edge superconductivity by superconducting quantum interference measurements in multilayer WTe2 Josephson junctions and we directly map the localized supercurrent. In thick WTe2 (~60 nm), the supercurrent is uniformly distributed by bulk states with symmetric Josephson effect ($\left|I_c^+(B)\right|=\left|I_c^-(B)\right|$). In thin WTe2 (10 nm), however, the supercurrent becomes confined to the edge and its width reaches up to 1.4 um and exhibits non-symmetric behavior $\left|I_c^+(B)\right|\neq \left|I_c^-(B)\right|$. The ability to tune the edge domination by changing thickness and the edge superconductivity establishes WTe2 as a promising topological system with exotic quantum phases and a rich physics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multiple Spin-Phonon Relaxation Pathways in a Kramer Single-Ion Magnet

We present a first-principles investigation of spin-phonon relaxation in a molecular crystal of Co(II) single-ion magnets. Our study combines electronic structure calculations with machine-learning force fields and unravels the nature of both the Orbach and the Raman relaxation channels in terms of atomistic processes. We find that although both mechanisms are mediated by the excited spin states, the low temperature spin dynamics is dominated by phonons in the THz energy range, which partially suppress the benefit of having a large magnetic anisotropy. This study also determines the importance of intra-molecular motions for both the relaxation mechanisms and paves the way to the rational design of a new generation of single-ion magnets with tailored spin-phonon coupling.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Role of longitudinal fluctuations in L$1_0$ FePt

L$1_0$ FePt is a technologically important material for a range of novel data storage applications. In the ordered FePt structure the normally non-magnetic Pt ion acquires a magnetic moment, which depends on the local field originating from the neighboring Fe atoms. In this work a model of FePt is constructed, where the induced Pt moment is simulated by using combined longitudinal and rotational spin dynamics. The model is parameterized to include a linear variation of the moment with the exchange field, so that at the Pt site the magnetic moment depends on the Fe ordering. The Curie temperature of FePt is calculated and agrees well with similar models that incorporate the Pt dynamics through an effective Fe-only Hamiltonian. By computing the dynamic correlation function the anisotropy field and the Gilbert damping are extracted over a range of temperatures. The anisotropy exhibits a power-law dependence with temperature with exponent $n\approx2.1$. This agrees well with what observed experimentally and it is obtained without including a two-ion anisotropy term as in other approaches. Our work shows that incorporating longitudinal fluctuations into spin dynamics calculations is crucial for understanding the properties of materials with induced moments.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The limit of spin lifetime in solid-state electronic spins

The development of spin qubits for quantum technologies requires their protection from the main source of finite-temperature decoherence: atomic vibrations. Here we eliminate one of the main barriers to the progress in this field by providing a complete first-principles picture of spin relaxation that includes up to two-phonon processes. Our method is based on machine learning and electronic structure theory and makes the prediction of spin lifetime in realistic systems feasible. We study a prototypical vanadium-based molecular qubit and reveal that the spin lifetime at high temperature is limited by Raman processes due to a small number of THz intra-molecular vibrations. These findings effectively change the conventional understanding of spin relaxation in this class of materials and open new avenues for the rational design of long-living spin systems.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Electronic spin-spin decoherence contribution in molecular qubits by quantum unitary spin dynamics

The realisation of quantum computers based on molecular electronic spins requires the design of qubits with very long coherence times, T2. Dephasing can proceed over several different microscopic pathways, active at the same time and in different regimes. This makes the rationalisation of the dephasing process not straightforward. Here we present a computational methodology able to address spin decoherence processes for a general ensemble of spins. The method consists in the propagation of the unitary quantum spin dynamics on a reduced Hilbert space. Then we study the dependence of spin dephasing over the magnetic dilution for a crystal of Vanadyl-based molecular qubits. Our results show the importance of long-range electronic spin-spin interactions and their effect on the shape of the spin-echo signal.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

XDFT: an efficient first-principles method for neutral excitations in molecules

State-of-the-art methods for calculating neutral excitation energies are typically demanding and limited to single electron-hole pairs and their composite plasmons. Here we introduce excitonic density-functional theory (XDFT) a computationally light, generally applicable, first-principles technique for calculating neutral excitations based on generalized constrained DFT. In order to simulate an M-particle excited state of an N-electron system, XDFT automatically optimizes a constraining potential to confine N-M electrons within the ground-state Kohn-Sham valence subspace. We demonstrate the efficacy of XDFT by calculating the lowest single-particle singlet and triplet excitation energies of the well-known Thiel molecular test set, with results which are in excellent agreement with time-dependent DFT. Furthermore, going beyond the capability of adiabatic time-dependent DFT, we show that XDFT can successfully capture double excitations. Overall our method makes optical gaps, excition bindings and oscillator strengths readily accessible at a computational cost comparable to that of standard DFT. As such, XDFT appears as an ideal candidate to work within high-throughput discovery frameworks and within linear-scaling methods for large systems.

0 citations0 reviewsNo code linkedOpen access