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Balcells Badia, David
(2023).
Exploring the Metal-Organic Chemical Space with Evolutionary and Machine Learning Approaches
.
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Balcells Badia, David
(2023).
Machine Learning for Metal-Organic Chemistry.
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Balcells Badia, David
(2023).
Deep Graph Learning Approaches to Chemistry.
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Balcells Badia, David
(2023).
Machine Learning the Chemistry of Transition Metals.
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Balcells Badia, David
(2023).
Machine Learning the Structure and Reactivity of Transition Metal complexes.
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Kneiding, Hannes; Nova Flores, Ainara & Balcells Badia, David
(2023).
Evolutionary Multiobjective Optimization for Transition Metal Complexes.
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Kneiding, Hannes; Nova Flores, Ainara & Balcells Badia, David
(2023).
Evolutionary Multiobjective Optimization for Transition Metal Complexes.
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Kneiding, Hannes & Balcells Badia, David
(2023).
Machine Learning Quantum Properties of Transition Metal Complexes with Natural Quantum Graphs.
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Balcells Badia, David
(2022).
Machine Learning Organometallic Chemistry.
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Balcells Badia, David
(2022).
Machine Learning Approaches to Transition Metal Chemistry.
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Kneiding, Hannes & Balcells Badia, David
(2022).
Graph Representation Learning for Transition Metal Complexes.
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Kneiding, Hannes & Balcells Badia, David
(2022).
Machine Learning Quantum Properties of Transition Metal Complexes from Graph Representations.
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Kneiding, Hannes & Balcells Badia, David
(2022).
Machine Learning Quantum Properties of Transition Metal Complexes using Graph Neural Networks.
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Balcells, David
(2020).
Machine Learning Reactivity at the Fundamental Steps of Transition Metal Catalysts.
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Balcells, David
(2020).
Machine Learning Reactivity at the Fundamental Steps of Transition Metal Catalysis.
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Héron, Julie Denise Josette & Balcells, David
(2019).
Catalyst Activation Mechanisms in the Cu2AAC reaction.
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Héron, Julie Denise Josette & Balcells, David
(2019).
Computational Study of the Catalyst Activation of the Cu2AAC reaction.
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Balcells, David; Clot, Eric; Macgregor, Stuart A.; Maseras, Feliu & Perrin, Lionel
(2019).
A career in catalysis: Odile Eisenstein.
ACS Catalysis.
ISSN 2155-5435.
9(11),
s. 10375–10388.
doi:
10.1021/acscatal.9b02498.
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Artús Suàrez, Lluís; Balcells, David & Nova, Ainara
(2019).
Rational Cocatalyst Design for Amide
Hydrogenolysis Based on DFT Calculations.
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Balcells, David
(2018).
Minimizing Off-Cycle Species in the Design of New Catalysts for Cross-Coupling Reactions
.
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Balcells, David
(2018).
Minimizing Off-Cycle Species in Palladium- and Nickel-Catalyzed Cross-Coupling Reactions
.
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Artus Suarez, Lluis; Nova, Ainara; Balcells, David & Tilset, Mats
(2018).
Computational study on the iron-catalyzed hydrogenation of amides to methanol and amines.
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Artus Suarez, Lluis; Nova, Ainara; Balcells, David & Tilset, Mats
(2018).
Hydrogenation of amides to methanol and amines with an
iron-based catalyst. A computational study.
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Nova, Ainara; Balcells, David & Melteig, Elina
(2018).
Nytt «designer-molekyl» kan spare industrien for store beløp.
[Internett].
https://titan.uio.no/node/2909.
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Balcells, David
(2018).
Rational Design of Pd and Ni Catalysts for Cross-Coupling
Reactions by Minimizing Off-Cycle Species.
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Balcells, David
(2017).
Off-Cycle Strategies in Catalyst Optimization for Cross-Coupling Reactions.
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Balcells, David
(2017).
Challenging TD-DFT in Catalyst Design Aimed at Water Splitting and Carbon Dioxide Reduction.
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Artus Suarez, Lluis; Nova, Ainara & Balcells, David
(2017).
Earth-abundant metal catalyst for amide hydrogenation to methanol.
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Wåhlander, Jakob Karl Kristoffer; Balcells, David; Gundersen, Lise-Lotte & Amedjkouh, Mohamed
(2017).
A New Phosphonamide-framework as Catalyst in Diels-Alder Reactions.
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Balcells, David
(2016).
Challenging TD-DFT in Catalyst Design Aimed at Water Oxidation and Carbon Dioxide Reduction.
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Balcells, David
(2016).
Off-Cycle Chemistry Optimization in Catalyst Design.
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Thoresen, Eirik Mydske; Amedjkouh, Mohamed; Tilset, Mats; Lillerud, Karl Petter; Øien-Ødegaard, Sigurd & Balcells, David
(2016).
Novel Ruthenium Functionalized Linkers for Photosensitization of Metal-Organic Frameworks.
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Balcells, David
(2015).
A joint experimental-theoretical approach to the optimization of the Suzuki-Miyaura reaction.
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Burschowsky, Daniel; Krengel, Ute; Uggerud, Einar & Balcells, David
(2015).
Quantum chemical modeling of chorismate mutase catalysis.
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Wåhlander, Jakob Karl Kristoffer; Balcells, David; Gundersen, Lise-Lotte & Amedjkouh, Mohamed
(2015).
Computational Study of Phosphonamides for use in Diels-Alder Reactions.
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Balcells, David
(2014).
DFT Studies on the Dark Side of Catalysis: Active Species Generation and Catalyst Degradation.
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Balcells, David
(2014).
DFT Studies on the Dark Side of Catalysis: Active Species Generation and Catalyst Degradation
.
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Balcells, David
(2014).
DFT Studies on the Dark Side of Catalysis: Active Species Generation and Catalyst Degradation
.
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Balcells, David
(2014).
DFT for Modeling, Development and New Concepts.
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Balcells, David
(2014).
Computational homogeneous catalysis: Structure, reactivity and new concepts
.
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Balcells, David
(2014).
DFT Adventures on Structure, Reactivity, Development and New Concepts
.
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Balcells, David
(2014).
DFT Studies on the Suzuki-Miyaura Reaction.
Taking a Walk on the Dark Side of Catalysis
.
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Møllendal, Harald; Balcells, David; Eisenstein, Odile & Suissa, Michal Rachel
(2014).
Conformational analysis and theoretical calculations of morphine and morphinum, in the gas phase and in water: A DFT and MP2 study.
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Wåhlander, Jakob Karl Kristoffer; Amedjkouh, Mohamed; Balcells, David & Gundersen, Lise-Lotte
(2014).
Synthesis directed towards asmarine fragments and development of Lewis acid Diels-Alder catalysts.
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Reimann, Sarah; Fromager, Emmanuel; Gori-Giorgi, Paola & Balcells, David
(2018).
Treatment of Magnetic Fields in Density-Functional Theory .
University of Oslo.
ISSN 1501-7710.
2018(2027).
Vis sammendrag
Magnetic properties are an important application area in quantum chemistry. However, for the most widely used method in electronic structure calculations, density-functional theory, it is still not understood how to rigorously include the magnetic field into the exchange--correlation functional. This work contributes to and further develops magnetic-field density-functional theory (BDFT), which is an alternative to, but less well-known, than current density-functional theory (CDFT).
A major focus is to investigate the importance to introduce a magnetic-field dependence into density functional approximations (DFAs) to improve the computation of magnetic properties. The main conclusion is that the full benefit of a field dependence can only be realized if simultaneously the self-consistent density of present DFAs is improved.
The investigations and results of this work provide valuable information both for further theoretical developments of BDFT and for the proper inclusion of magnetic field effects into DFAs, which will allow more accurate results for density-functional calculations involving magnetic fields.