How do phonons relax molecular spins?

Lunghi, Alessandro; Sanvito, Stefano

doi:10.1126/sciadv.aax7163

Cited by 96 publications

(206 citation statements)

References 31 publications

(49 reference statements)

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“…Indeed, the here-calculated phonons energies and eigenvectors enable the evaluation of the relaxation dynamics in this prototype molecular qubit. A first ab initio simulation of direct relaxation processes has just been performed 57 . In that contribution the authors showed that in high external magnetic field the dominant mechanism of spin relaxation comes from the modulation of the g tensor of the Spin Hamiltonian Zeeman term by low-energy acoustic modes.…”

Section: Discussionmentioning

confidence: 99%

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Unveiling phonons in a molecular qubit with four-dimensional inelastic neutron scattering and density functional theory

et al. 2020

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Phonons are the main source of relaxation in molecular nanomagnets, and different mechanisms have been proposed in order to explain the wealth of experimental findings. However, very limited experimental investigations on phonons in these systems have been performed so far, yielding no information about their dispersions. Here we exploit state-ofthe-art single-crystal inelastic neutron scattering to directly measure for the first time phonon dispersions in a prototypical molecular qubit. Both acoustic and optical branches are detected in crystals of [VO(acac) 2 ] along different directions in the reciprocal space. Using energies and polarisation vectors calculated with state-of-the-art Density Functional Theory, we reproduce important qualitative features of [VO(acac) 2 ] phonon modes, such as the presence of low-lying optical branches. Moreover, we evidence phonon anti-crossings involving acoustic and optical branches, yielding significant transfers of the spin-phonon coupling strength between the different modes.

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Section: Discussionmentioning

confidence: 99%

“…where g α;β are the components of the g tensor and Q j;q are those of the normal modes 57 . Results for the AC along Γ-X are reported in Fig.…”

Section: Discussionmentioning

confidence: 99%

Unveiling phonons in a molecular qubit with four-dimensional inelastic neutron scattering and density functional theory

et al. 2020

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“…When both α 1 and α 2 are nonzero, the elastic net model is obtained. The ridge and LASSO regression models have found wide applications in materials ML to solve the small‐data problem, compute phonons using compressive sensing, perform feature selection, and avoid overfitting …”

Section: Model Selection and Trainingmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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“…The understanding of the microscopic processes leading to spin-phonon coupling and demagnetization is of interest for several applications such as heat-assisted magnetic recording [6], ultra-fast demagnetization [7], magnetostriction [8], spintronics [9], molecular magnetism [10,11] and quantum computing based on electronic spins [12,13]. The design of magnetic materials is a prototypical example where multiple features must be optimized at the same time to reach optimal efficiency.…”

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confidence: 99%

“…The curvature of the plots corresponds to the anharmonic terms of the potential energy surface and to the spin-phonon coupling coefficients beyond the first-order. All these features are related to multi-phonons contributions to spin relaxation and their determination is expected to be crucial for the rationalization of spin dynamics in molecular compounds [13]. The calculation of first-order spin-phonon coupling coefficients would require more than 1000 CASSCF simulations [10,13], a value comparable to the cost of training and testing of the ML model.…”

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confidence: 99%

Surfing Multiple Conformation-Property Landscapes via Machine Learning: Designing Single-Ion Magnetic Anisotropy

Lunghi

Sanvito

2020

J. Phys. Chem. C

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The advent of computational statistical disciplines, such as machine learning, is leading to a paradigm shift in the way we conceive the design of new compounds. Today computational science does not only provide a sound understanding of experiments, but also can directly design the best compound for specific applications. This approach, known as reverse engineering, requires the construction of models able to efficiently predict continuous structure-property maps. Here we show that reverse engineering can be used to tune the magnetic properties of a single-ion molecular magnet in an automated intelligent fashion. We design a machine learning model to predict both the energy and magnetic properties as function of the chemical structure. Then, a particleswarm optimization algorithm is used to explore the conformational landscapes in the search for new molecular structures leading to an enhanced magnetic anisotropy. We find that a 5% change in one of the coordination angles leads to a ∼50% increase in the anisotropy. Our approach paves the way for a machine-learning-driven exploration of the chemical space of general classes of magnetic materials. Most importantly, it can be applied to any structure-property relation and offers an effective way to automatically generate new materials with target properties starting from the knowledge of previously synthesized ones.

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How do phonons relax molecular spins?

Cited by 96 publications

References 31 publications

Unveiling phonons in a molecular qubit with four-dimensional inelastic neutron scattering and density functional theory

Unveiling phonons in a molecular qubit with four-dimensional inelastic neutron scattering and density functional theory

A Critical Review of Machine Learning of Energy Materials

Surfing Multiple Conformation-Property Landscapes via Machine Learning: Designing Single-Ion Magnetic Anisotropy

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