On-Surface Oligomerization of Self-Terminating Molecular Chains for the Design of Spintronic Devices

Sierda, Emil; Abadía, Mikel; Brede, Jens; Elsebach, Micha; Bugenhagen, Bernhard; Prosenc, Marc H.; Bazarnik, Maciej; Wiesendanger, R.

doi:10.1021/acsnano.7b04194

Cited by 21 publications

(20 citation statements)

References 33 publications

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“…Interactions between unpaired electrons localized on metal centers are an important feature of the active sites of many metalloenzymes 1 , artificial catalysts 2 , and molecular magnets with possible applications such as data storage, quantum computing or magnetic materials [3][4][5][6][7][8][9][10][11] . Such interactions are usually not mediated directly by magnetic interactions due to the distance between the spin centers, but rather via a combination of Coulomb interaction and exchange interactions resulting from the Pauli exclusion principle.…”

Section: Introductionmentioning

confidence: 99%

Exchange Spin Coupling from Gaussian Process Regression

et al. 2020

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Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates ("error bars") along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science. File list (4) download file view on ChemRxiv supp.pdf (2.07 MiB) download file view on ChemRxiv ms.pdf (5.17 MiB) download file view on ChemRxiv Cu2-dataset.zip (22.06 MiB) download file view on ChemRxiv py_regression.zip (17.79 MiB)

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Section: Introductionmentioning

confidence: 99%

Exchange Spin Coupling from Gaussian Process Regression

et al. 2020

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“…An emerging possibility to address these challenges is to develop hybrid materials in which surfaces are functionalized with organic molecules. − Chemical approaches using self-assembled monolayers or molecular films to manipulate the chemical, electronic, − and magnetic , properties of thin films and nanostructures have been successfully applied to tune material surfaces and interfaces. Moreover, fascinating studies over the past two decades have demonstrated that chiral molecular assemblies can act as electron spin filters, − a phenomenon known as the chiral-induced spin selectivity (CISS) effect (Figure ).…”

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confidence: 99%

Spin Selectivity in Photoinduced Charge-Transfer Mediated by Chiral Molecules

et al. 2019

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Optical control and readout of electron spin and spin currents in thin films and nanostructures have remained attractive yet challenging goals for emerging technologies designed for applications in information processing and storage. Recent advances in room-temperature spin polarization using nanometric chiral molecular assemblies suggest that chemically modified surfaces or interfaces can be used for optical spin conversion by exploiting photoinduced charge separation and injection from well-coupled organic chromophores or quantum dots. Using light to drive photoexcited charge-transfer processes mediated by molecules with central or helical chirality enables indirect measurements of spin polarization attributed to the chiral-induced spin selectivity effect and of the efficiency of spin-dependent electron transfer relative to competitive relaxation pathways. Herein, we highlight recent approaches used to detect and to analyze spin selectivity in photoinduced charge transfer including spin-transfer torque for local magnetization, nanoscale charge separation and polarization, and soft ferromagnetic substrate magnetization- and chirality-dependent photoluminescence. Building on these methods through systematic investigation of molecular and environmental parameters that influence spin filtering should elucidate means to manipulate electron spins and photoexcited states for room-temperature optoelectronic and photospintronic applications.

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Section: Introductionmentioning

confidence: 99%

Exchange Spin Coupling from Gaussian Process Regression

Bahlke¹,

Mogos²,

Proppe³

et al. 2020

Preprint

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Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

show abstract

On-Surface Oligomerization of Self-Terminating Molecular Chains for the Design of Spintronic Devices

Cited by 21 publications

References 33 publications

Exchange Spin Coupling from Gaussian Process Regression

Exchange Spin Coupling from Gaussian Process Regression

Spin Selectivity in Photoinduced Charge-Transfer Mediated by Chiral Molecules

Exchange Spin Coupling from Gaussian Process Regression

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