Special issue on machine learning and quantum mechanics

Rupp, Matthias

doi:10.1002/qua.24955

Cited by 16 publications

(13 citation statements)

References 12 publications

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“…The far field of machine learning algorithm has already been expanded to application areas like theoretical chemistry and organic synthesis to train models on data sets derived from experimental data and common rules in computational chemistry [149][150][151]. It has been shown that these models can compete with calculations from chemical laws and increment methods [151][152][153].…”

Section: Nir Light Sensitized Copper Catalyzed Azide-alkyne Click Reamentioning

confidence: 99%

Photophysics and photochemistry of NIR absorbers derived from cyanines: key to new technologies based on chemistry 4.0

Strehmel¹,

Schmitz²,

Kütahya³

et al. 2020

Beilstein J. Org. Chem.

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Cyanines derived from heptamethines were mainly discussed regarding their functionalization to broaden the solubility in different surroundings exhibiting either hydrophilic or hydrophobic properties and to tailor made the ΔG et photopysical properties with respect to absorption and fluorescence. Electrochemical properties were additionally considered for some selected examples. The cyanines chosen comprised as end groups either indolenine, benzo[e]- or benzo[cd]indolium pattern, which facilitated to shift the absorption between 750–1000 nm. This enabled their use in applications with light sources emitting in the near-infrared (NIR) region selected from high power LEDs or lasers with line-shaped focus. The absorbers considered were discussed regarding their function as sensitizer for applications related to Chemistry 4.0 standards. These were mainly photopolymer coatings, which can be found for applications in the graphic industry or to protect selected substrates. The huge release of heat on demand upon turning ON or OFF the NIR light source enables them for photothermal treatment in processes requesting heat to initiate either chemical (activated reactions) or physical (melting, evaporation) events.

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Section: Nir Light Sensitized Copper Catalyzed Azide-alkyne Click Reamentioning

confidence: 99%

Photophysics and photochemistry of NIR absorbers derived from cyanines: key to new technologies based on chemistry 4.0

Strehmel¹,

Schmitz²,

Kütahya³

et al. 2020

Beilstein J. Org. Chem.

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“…On the neutral double vacancy, which is common in MgO preparation, the recovery of the gas phase Ag 8 magic number cluster was predicted. 82 This cluster exhibits large HOMO–LUMO gaps and stability with respect to nearby sizes. By contrast, a DFT-BH investigation by the same authors on Ag n ( n ≤ 11) on the F s vacancy of the same oxide shows the complete loss of the magic number.…”

Section: Global Optimizationmentioning

confidence: 99%

“…The two separate EAs are performed iteratively until the AM1 parameters give an energy ordering that is consistent with the accumulated ab initio database. Although such adaptive, on-the-fly re-parametrizations tailored for a specific problem at hand hold, in our opinion, great potential for the future (with availability of fast computers and robust fitting approaches 82 ) (see Section 6 on machine learning approaches), in the early 2000s this approach was deemed prohibitively expensive 70 and was not pursued further. Rather, a fixed set of improved AM1 parameters, termed the GAM1 method, was obtained 24 from the Si 7 H 14 training set, and considered transferable to other Si n H m stoichiometries.…”

Section: Global Optimizationmentioning

confidence: 99%

Towardsoperandocomputational modeling in heterogeneous catalysis

et al. 2018

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“…Recently, in chemistry and materials science, machinelearning has become a popular tool for analyzing properties of molecules and materials, and finding specific functions from large data sets [32,33]. But it has also been applied to the problem of finding density functionals, constructed by interpolation from accurate examples.…”

Section: Machine Learning Of the Ks Kinetic Energy Functionalmentioning

confidence: 99%

Pure density functional for strong correlation and the thermodynamic limit from machine learning

Li¹,

Baker²,

White³

et al. 2016

Phys. Rev. B

109

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We use density-matrix renormalization group, applied to a one-dimensional model of continuum Hamiltonians, to accurately solve chains of hydrogen atoms of various separations and numbers of atoms. We train and test a machine-learned approximation to F [n], the universal part of the electronic density functional, to within quantum chemical accuracy. Our calculation (a) bypasses the standard Kohn-Sham approach, avoiding the need to find orbitals, (b) includes the strong correlation of highly-stretched bonds without any specific difficulty (unlike all standard DFT approximations) and (c) is so accurate that it can be used to find the energy in the thermodynamic limit to quantum chemical accuracy.

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Special issue on machine learning and quantum mechanics

Cited by 16 publications

References 12 publications

Photophysics and photochemistry of NIR absorbers derived from cyanines: key to new technologies based on chemistry 4.0

Photophysics and photochemistry of NIR absorbers derived from cyanines: key to new technologies based on chemistry 4.0

Towardsoperandocomputational modeling in heterogeneous catalysis

Pure density functional for strong correlation and the thermodynamic limit from machine learning

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