scholarly journals The role of the quadrupolar interaction in the tunneling dynamics of lanthanide molecular magnets

2019 ◽  
Vol 125 (14) ◽  
pp. 142903 ◽  
Author(s):  
Gheorghe Taran ◽  
Edgar Bonet ◽  
Wolfgang Wernsdorfer
Keyword(s):  
Molecular Magnets ◽  
Quadrupolar Interaction ◽  
Tunneling Dynamics

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

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.

π–d INTERACTION BASED MOLECULAR CONDUCTING MAGNETS: HOW TO INCREASE THE EFFECTS OF THE π–d INTERACTION

COSMOS ◽  
2008 ◽  
Vol 04 (02) ◽  
pp. 131-140 ◽  
Author(s):  
AKIRA MIYAZAKI ◽  
TOSHIAKI ENOKI
Keyword(s):  
Crystal Structure ◽  
Magnetic Properties ◽  
Transition Temperature ◽  
Phase Boundary ◽  
Ground State ◽  
Magnetic Order ◽  
Molecular Magnets ◽  
Metallic State ◽  
One Dimensional

The crystal structures and electronic and magnetic properties of conducting molecular magnets developed by our group are reviewed from the viewpoints of our two current strategies for increasing the efficiency of the π–d interaction. (EDTDM)2 FeBr 4 is composed of quasi-one-dimensional donor sheets sandwiched between magnetic anion sheets. The ground state of the donor layer changes from the insulator state to the metallic state by the application of pressure. When it is near to the insulator–metal phase boundary pressure, the magnetic order of the anion spins considerably affects the transport properties of the donor layer. The crystal structure of ( EDO – TTFBr 2)2 FeX 4 ( X = Cl , Br ) is characterized by strong intermolecular halogen–halogen contacts between the organic donor and FeX 4 anion molecules. The presence of the magnetic order of the Fe 3+ spins and relatively high magnetic order transition temperature proves the role of the halogen–halogen contacts as exchange interaction paths.

π- d Interaction-Based Molecular Magnets: Role of Sulfur-to-Selenium Substitution

2005 ◽  
Vol 180 (3-4) ◽  
pp. 883-890
Author(s):  
Akira Miyazaki ◽  
Kazuki Okabe ◽  
Kengo Enomoto ◽  
Toshiaki Enoki
Keyword(s):  
Molecular Magnets ◽  
Selenium Substitution

Exchange Spin Coupling fromGaussian Progress Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Dipole Moments ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

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.

scholarly journals Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

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.

MAGNONS AND DIPOLAR INTERACTIONS BETWEEN NEW HIGH SPIN MAGNETS

2011 ◽  
Vol 25 (17) ◽  
pp. 1427-1439
Author(s):  
M. A. FALLOUL ◽  
M. EL HAFIDI
Keyword(s):  
High Spin ◽  
Molecular Magnets ◽  
Dipolar Interactions ◽  
Spin Hamiltonian ◽  
Effective Spin ◽  
Molecular Nanomagnets ◽  
Cubic Lattice ◽  
Dipole Interactions ◽  
Microscopic Interactions

In this work, we elucidate the role of various microscopic interactions in high spin molecular nanomagnets. Starting from an effective spin Hamiltonian, we explain the origin and the importance of each coupling. We give special attention to exchange and dipole–dipole interactions between molecular magnets in a cubic lattice. The dispersion relation and magnetization behaviors are analyzed in the quantum magnons formalism taking into account the experimental reality and using common parameters values.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

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.

On the Low Stability of Molecular Magnets Based on Transition Metal Hexacyanochromates (III)

2010 ◽  
Vol 224 (06) ◽  
pp. 807-826
Author(s):  
E. Reguera ◽  
J. Rodriguez-Hernandez ◽  
C. Tellez ◽  
M. Centeno
Keyword(s):  
Transition Metal ◽  
Research Area ◽  
Molecular Magnets ◽  
Metal Salts ◽  
Crystal Water ◽  
Range Magnetic Order ◽  
Prussian Blue Analogues ◽  
Long Range Magnetic Order

AbstractIn the research area of molecular magnets for Prussian blue analogues interesting and unusual effects have been observed, particularly for mixed transition metal salts of the hexacyanochromate (III) anion, TA3-xTBx[Cr(CN)6]2·yH2O. For single metal salts, T3[Cr(CN)6]2·yH2O, with T = Mn(2+), Fe(2+), Co(2+), three paramagnetic ions where long range magnetic order is observed, the materials show low stability. The structural change can be envisaged as a flipping of the CN ligand, from T-N≡C-Cr-C≡N-T to Cr-N≡C-T-C≡N-Cr. The material containing these metals (Mn, Fe, Co) could be partially stabilized by the incorporation of a second metal that does not form stable hexacyano complexes (Ni, Cu, Zn, Cd). In this contribution such possibility is explored. The role of the porous framework in the material low stability is also discussed. For analog compact solids, TCs[Cr(CN)6], a relatively high stability on aging was observed. The study of the mixed compositions is preceded by a structural characterization of the simple series where the effect of the crystal water removal is also considered.

Sign in / Sign up

Export Citation Format

Share Document