Computational Chemistry Days 2021

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Computational Chemistry Days 2021

Aalto University, 15

^th

-16

^th

December 2021

Abstracts for the Oral Presentations

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Ab initio modeling of strongly anisotropic exchange interactions in polymetallic lanthanide–radical single-molecule magnets

A. Mansikkamäki,^a M. Murugesu,^b N. Mavragani,^b D. Errulat,^b

a NMR Research Unit, University of Oulu, P.O. Box 300, 90014 Oulu, Finland

b Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario, K1N 6N5 Canada

Email: a [email protected]

Coordination complexes with multiple lanthanide ions coupled by radical ligands can in certain situations show strong magnetic anisotropy. This can lead to slow relaxation of magnetization and giant magnetic coercivity[1], with possible applications in molecular quantum devices. The lanthanide–radical interaction is strongly anisotropic and extremely complicated. The microscopic mechanism was not derived until 2015[2], and a completely first-principles calculation of it has never been carried out before.

In the present work we introduce a fully ab initio multireference methodology for the evaluation of lanthanide–radical exchange coupling. The ab initio states are mapped to a pseudospin Hamiltonian with a minimal set of parameters that utilizes the full rotational symmetry of the problem under SO(3). The results are used to analyze the microscopic exchange mechanism in detail and to quantitatively reproduce experimentally measured static magnetic properties of polymetallic lanthanide–radical single-molecule magnets[3].

[1] J. D. Rinehart, M. Fang, W. J. Evans, J. R. Long. Strong exchange and magnetic blocking in N23–-radical-bridged lanthanide complexes. Nat. Chem. 2011, 3, 538–542.

[2] N. Iwahara, L. F. Chibotaru, Exchange interaction between J multiplets. Phys. Rev. B.

2015, 91, 174438.

[3] N. Mavragani, D. Errulat, D. A. Gálico, A. A. Kitos, A. Mansikkamäki, M. Murugesu, Radical-Bridged Ln4 Metallocene Complexes with Strong Magnetic Coupling and a Large Coercive Field. Angew. Chem. Int. Ed. 2021, in press.

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Department of Chemistry, Faculty of Science, University of Helsinki, Finland [email protected]

Abstract

An overview of relations between magnetically induced current densities, ring-current strengths and magnetic shielding tensors is presented.[1,2,3] Spatial contributions also called magnetic shielding densities calculated from magnetically induced current densities yield information about the origin of nuclear magnetic shielding constants.[4,5] Spatial contributions to the magnetizability can be calculated in the same way.[6] It is also shown how the strength and profile of magnetically induced ring currents can be obtained from nuclear magnetic shielding tensors by using the integral form of Amper´ere-Maxwell’s law.[7] The methods have been implemented and applied on ring-shaped molecules like benzene and porphyrinoids.

-15 -10 -5 0 5 10 15

-8 -6 -4 -2 0 2 4 6 8

The left figure shows the magnetic shielding density of free-base porphyrin. The right picture shows the ring-current profile of benzene calculated from the magnetically induced current density and from the magnetic shielding tensor (solid line) using Amp´ere-Maxwell’s law (dashed line), respectively.

References

[1] D. Sundholm, M. Dimitrova, R. J. F. Berger, ”Current density and molecular magnetic properties”, Chem. Comm. (2021) DOI:10.1039/D1CC03350F

[2] M. Dimitrova, D. Sundholm, ”Current density, current-density pathways and molecular aromaticity”, Chapter 5 in Aromaticity: Modern Computational Methods and Applications, Ed. I.

Fern´andez L´opez, Elsevier (2021) pp. 155-194. http://arxiv.org/abs/2105.04902

[3] D. Sundholm, H. Fliegl, ”Aromatic Pathways in Porphyrinoids by Magnetically Induced Ring Currents”, Handbook of Porphyrin Science, Vol. 46, Eds K. M. Kadish, K. M. Smith and R.

Guilard, World Scientific (2021) (in press).

[4] R. Kumar Jinger, H. Fliegl, R. Bast, M. Dimitrova, S. Lehtola, D. Sundholm, ”Spatial contributions to NMR chemical shifts”, J. Phys. Chem. A 125 (2021) 1778-1786

[5] H. Fliegl, M. Dimitrova, R. J. F. Berger, D. Sundholm, ”Spatial Contributions to ¹H NMR Chemical Shifts of Free-Base Porphyrinoids”, Chemistry 3 (2021) 1005-1021.

[6] S. Lehtola, M. Dimitrova, H. Fliegl, D. Sundholm, ”Benchmarking magnetizabilities with recent density functionals”, J. Chem. Theory Comput. 17 (2021) 1457-1468.

[7] R. J. F. Berger, M. Dimitrova, R. T. Nasibullin, R. R. Valiev, D. Sundholm, ”Integration of Global Ring Currents Using the Amp´ere-Maxwell Law”, Phys. Chem. Chem. Phys. (2021) (submitted).

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Radical Approach to Lanthanide-Based Single-Molecule Magnets

Jani Olavi Moilanen,^a Juho Toivola,^a Akseli Mansikkamäki,^b Jaclyn L. Brusso,^c and Muralee Murugesu^c

a Department of Chemistry, Nanoscience Centre, University of Jyväskylä, P.O. Box 35, FI- 40014, Finland

b NMR Research Unit, Faculty of Science, University of Oulu, Oulu FIN-90014, Finland

c Department of Chemistry and Biomolecular Sciences, University of Ottawa, ON, K1N 6N5, Canada.

Email: [email protected]

Lanthanide-based single-molecule magnets (Ln-SMM) represent a class of molecular compounds that retain their induced magnetization long enough to show magnetic hysteresis below a certain blocking temperature (Tb), which would be an approximate upper limit for the operating temperature of any SMM-based memory device.[1] One of the most successful strategies to increase Tb in Ln-SMMs has been to maximize the magnetic anisotropy of Ln ion by optimizing the interaction between the single-ion electron density of Ln ion and the crystal (ligand) field, like in the family of lanthanide metallocenes that have the highest reported Tb, up to 80 K, to date. Also, the exchange interaction between the spin of a bridging radical ligand and unpaired electrons of Ln centers can be utilized to enhance the magnetic properties of Ln- SMMs.[2] Although radical-based Ln-SMMs show lower Tb than lanthanide metallocenes, their measured coercive fields (Hc) have been among the largest ever reported to any molecule.

Most of the reported radical-based Ln-SMMs have only utilized strong exchange interaction between the spins of radical ligand and metal centers, and there is only a handful of compounds in which the stacked radicals not only act as bridging radical units between Ln centers, but they also interact with each other through the antiferromagnetic interaction.[3] In this presentation, I will talk about our recent results obtained for the dinuclear [Dy2(μ-(bpytz)2)(THMD)4] complex 1 (bpytz = 3,6-bis(3,5-dimethyl-pyrazolyl)-1,2,4,5-tetrazine; TMHD = 2,2,6,6- tetramethyl-3,5-heptanedionate) in which two radical ligands function as the single bridging π- dimer between two Dy³⁺ centers.[3c] Our findings demonstrate how the nature of the exchange interaction between radical ligands influences the magnetic properties of the investigated system through experimental and computational data. We also show with the help of model systems and quantum chemical calculations how the exchange interaction between the stacked radical ligands can be changed from the antiferromagnetic to ferromagnetic.

[1] (a) Guo, F.-S.; Day, B. M.; Chen, Y.-C.; Tong, M.-L.; Mansikkamäki, A.; Layfield, R.

A. Science 2018, 362, 1400–1403. (b) Gould, C. A.; McClain, K. R.; Yu, J. M.;

Groshens, T. J.; Furche, F.; Harvey, B. G.; Long, J. R. J. Am. Chem. Soc. 2019, 141, 12967–12973.

[2] (a) Mavragani, N.; Errulat, D.; Gálico, D. A.; Kitos, A. A.; Mansikkamäki, A.;

Murugesu, M. Angew. Chem. Int. Ed. 2021, 60, 24206–24213. (b) Demir, S.; Gonzalez, M. I.; Darago, L. E.; Evans, W. J.; Long, J. R. Nat Commun 2017, 8, 2144.

[3] (a) Fatila, E. M.; Rouzières, M.; Jennings, M. C.; Lough, A. J.; Clérac, R.; Preuss, K. E.

J. Am. Chem. Soc. 2013, 135, 9596–9599. (b) Han, T.; Petersen, J. B.; Li, Z.-H.; Zhai, Y.-Q.; Kostopoulos, A.; Ortu, F.; McInnes, E. J. L.; Winpenny, R. E. P.; Zheng, Y.-Z.

Inorg. Chem. 2020, 59, 7371–7375. (c) Lemes, M. A.; Mavragani, N.; Richardson, P.;

Zhang, Y.; Gabidullin, B.; Brusso, J. L.; Moilanen, J. O.; Murugesu, M. Inorg. Chem.

Front. 2020, 7, 2592–2601.

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Chemistry on Quantum Computers

Mikael P. Johansson^a

a CSC – IT Center for Science, Espoo, Finland Email:[email protected]

Quantum computing has the potential to revolutionise computational modelling. By exploiting quantum phenomena like superposition and entanglement, quantum parallelism offers up to exponential speed-up compared to classical binary supercomputers, for some problem classes.

One of the computationally difficult problems that quantum computers can accelerate is solving the electronic structure of molecules and materials. Here, I will give an overview of how quantum computing can be useful for computational chemistry in general, and quantum chemical applications in particular. When can we expect that quantum computers become useful for solving chemical problems? What is needed to get there?

Figure 1. Quantum chip with qubits, made by IQM Quantum Computers in Espoo, Finland.

[1] Y. Cao et al. Quantum Chemistry in the Age of Quantum Computing. Chem. Rev. 119 (2019) 10856.https://doi.org/10.1021/acs.chemrev.8b00803

[2] B. Bauer, S. Bravyi, M. Motta, G. K.-L. Chan. Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. Chem Rev. 120 (2020) 12685.

https://doi.org/10.1021/acs.chemrev.9b00829

[3] J.R. McClean et al. What the foundations of quantum computer science teach us about chemistry. J. Chem. Phys. 155 (2021) 150901.https://doi.org/10.1063/5.0060367 [4] M.P. Johansson, E. Krishnasamy, N. Meyer, C. Piechurski. Quantum Computing – A

European Perspective. PRACE Technical Report (2021).

https://doi.org/10.5281/zenodo.5547407

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Visualisation of the magnetically induced current density in molecules

M. Dimitrova,^a D. Sundholm^a

a Department of Chemistry, University of Helsinki Email: [email protected]

Molecules interact with magnetic fields which leads to a range of response properties, including magnetisability (magnetic susceptibility), nuclear magnetic shielding and magnetically induced current density. In the limit of weak magnetic fields (such as those present on our planet and routinely used in laboratories), one can employ perturbation theory to calculate magnetic properties. We employ the gauge-including magnetically induced current method (GIMIC) developed in our group in order to investigate molecular properties both visually and by integrating their strength [1,2,3]. We have developed sophisticated methods to depict the magnetically induced current density on a plane or as streamlines, which makes it possible to rationalise the complicated features of the current-density vector field in various molecular systems. Recently we implemented numerical integration of magnetisabilities and nuclear magnetic shielding constants from the magnetically induced current density. They can also been visualised to determine the spatial contributions to them on a slice or as 3D contours.

Figure 1. The main pathways of the magnetically induced current density in benzene (in red and blue) and the induced magnetic field (in black).

[1] D. Sundholm, M. Dimitrova, R. J. F. Berger, Current density and molecular magnetic properties. Chem. Comm. 2021. DOI:10.1039/D1CC03350F

[2] M. Dimitrova, D. Sundholm, Current density, current-density pathways and molecular aromaticity, Chapter 5 in Aromaticity: Modern Computational Methods and Applications, Ed. I. Fernandez, Elsevier 2021 pp. 155-194. http://arxiv.org/abs/2105.04902

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Insights into proton pumping in respiratory complex I through molecular dynamics

J. Lasham,â K. Parey,^{b, c, d} D. J. Mills,^c A. Djurabekova,â O. Haapanen,â E. G. Yoga,^b H.

Xie,^c W. Kühlbrandt,^c J. Vonck,^c V. Zickermann,^bV. Sharma^a

a University of Helsinki, Helsinki, Finland

b Goethe University, Frankfurt am Main, Germany

c Max Planck Institute of Biophysics, Frankfurt am Main, Germany

d University of Osnabrück, Osnabrück, Germany Email: [email protected]

Respiratory complex I is part of the electron transport chain, and functions through the reduction of ubiquinone from NADH, via a chain of iron-sulfurs clusters. This redox reaction is coupled to proton-pumping up to 200 Å away, and ultimately leads to the generation of ATP – the energy source in living cells. Recently, a high-resolution cryo-EM structure (2.1 Å) of complex I was resolved (Figure 1), and the near-atomic detail enabled protein-bound water molecules to be resolved, several of which are in potential proton-transfer pathways.[1]

We performed atomistic molecular dynamics simulations on this high-resolution structure in full lipid-solvent environment, resulting in a model system size of over 1.3 million atoms.

The simulations revealed crucial details of how protein conformational changes and changes in protonation states of titratable residues play an important role in the proton pumping of this enzyme. Additionally, simulations were also performed on another structure of complex I under turnover conditions (3.4 Å resolution), which along with biochemical data, allowed us to identify a novel proton transfer route. The combination of high-resolution protein structure data and large-scale simulations has allowed us to suggest new mechanistic insights into the redox-coupled proton pumping mechanism of complex I.

Figure 1. Full structure of complex I from Yarrowia lipolytica. Different protein subunits are shown in different colors. Iron-sulfur clusters are shown as yellow and orange spheres.

Selected water molecules from a simulation snapshot are shown as red spheres.

[1] K. Parey, J. Lasham, D. J. Mills, A. Djurabekova, O. Haapanen, E. G. Yoga, H. Xie, W.

Kühlbrandt, V. Sharma, J. Vonck & V. Zickermann, High-resolution structure and dynamics of mitochondrial complex I – insights into the proton pumping mechanism, Sci. Adv., 2021, 7, eabj3221.

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Effect of pH on complexation and secondary structures of polypeptides

T. Kastinen,â,b,c P. Batys,^d M. Morga,^d J. L. Lutkenhaus,ê and M. Sammalkorpiâ,c,f

a Department of Chemistry and Materials Science, Aalto University, Finland

b Faculty of Engineering and Natural Sciences, Tampere University, Finland

c Academy of Finland Centre of Excellence in Life-Inspired Hybrid Materials (LIBER), Aalto University, Finland

d Jerzy Haber Institute of Catalysis and Surface Chemistry PAS, Poland

e Artie McFerrin Department of Chemical Engineering and Department of Materials Science and Engineering, Texas A&M University, Texas, United States

f Department of Bioproducts and Biosystems, Aalto University, Finland Email:[email protected], [email protected]

Charged polypeptides are biomolecules with attractive properties, such as biocompatibility, biodegradability, and non-toxicity. They are also polyelectrolytes. In solution, their assemblies involve complex, highly ordered secondary structures similar to those in proteins. Polypeptide properties and self-assembly can be controlled by external stimuli, such as the solution pH or salt ions. This has led to charged polypeptides attracting a particular interest in biomedical applications, including drug delivery and functional coatings.

Here, we present studies of the influence of pH on the complexation and secondary structures of oppositely charged polypeptides poly(L-glutamic acid) (PGA) and poly(L-lysine) (PLL) [1].

By using atomistic molecular dynamics simulations and experimental techniques such as circular dichroism (CD), laser Doppler velocimetry (LDV), and dynamic light scattering (DLS), we determine the secondary structure, electrophoretic mobility (the zeta potential), and the hydrodynamic diameter of the examined macroions. We show that pH-controlled ionization degrees of the polypeptides have a clear impact on the intermolecular interactions between PGA and PLL and consequently on their complexation. Furthermore, the ionization degrees of PGA and PLL affect their secondary structures, namely the formation of β-sheets is observed between the fully charged PGA and PLL, while partially charged PGA leads to α-helical structures (Figure 1). These findings provide insight into the impact of pH on charged polypeptide assemblies. Especially the obtained guidelines for secondary structure changes can advance future design of self-assembling polypeptide materials.

Figure 1. Secondary structures predicted by molecular dynamics simulations for the polypeptide systems consisting of the fully charged PLL with either (a) the fully or (b) partially charged PGA. In the polypeptide chains, the β-sheet and α-helix structures are colored red and blue, respectively.

[1] P. Batys, M. Morga, P. Bonarek, M. Sammalkorpi. pH-Induced Changes in Polypeptide Conformation: Force-Field Comparison with Experimental Validation. J. Phys. Chem. B 2020, 124, 2961.

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Small Details Matter: a Subtle Shift in a High-Energy Water Molecule’s Location has a Great Impact in GSK-3β Inhibitor Potency.

T. Pantsar,^a S. Andreev,^b P. Koch,^b,c

a School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland

b Institute of Pharmaceutical Sciences, Department of Medicinal and Pharmaceutical Chemistry, Eberhard Karls University Tübingen, Germany

c Department of Pharmaceutical/Medicinal Chemistry II, Institute of Pharmacy, University of Regensburg, Germany

In small molecule binding, water is not a passive bystander but rather takes an active role in the binding site, which may be decisive for the potency of the inhibitor. In the contribution, we will discuss our latest findings related to our glycogen synthase kinase-3β (GSK-3β) inhibitor discovery project, where we found water to be decisive for inhibitor potency [1]. We utilized WaterMap analysis for our co-crystallized GSK-3β inhibitor complex. For the

derivatives, where a direct WaterMap approach failed, we successfully combined WaterMap with microsecond timescale molecular dynamics simulations. By addressing a high-energy water in the binding site, we improved the IC50 value of our co-crystallized GSK-3β inhibitor by nearly two orders of magnitude. Our results demonstrate that this high-energy water was not displaced by our potent inhibitor ((S)-15, IC50 value of 6 nM); instead, only a subtle shift in the location of this water molecule resulted in a dramatic decrease in the energy of this high-energy hydration site. This energy decrease was well in line with the improved inhibitor potency. Furthermore, (S)-15 demonstrated both a favorable kinome selectivity profile and target engagement in a cellular environment and reduced GSK-3 autophosphorylation in neuronal SH-SY5Y cells. Our findings highlight that even a slight adjustment in the location of a high-energy water can be decisive for ligand binding.

[1] S. Andreev⧫, T. Pantsar⧫, et al.: Addressing a Trapped High-Energy Water: Design and Synthesis of Highly Potent Pyrimidoindole-Based Glycogen Synthase Kinase-3β Inhibitors.

J. Med. Chem. 2021, XXXX, XXX, XXX–XXX.

https://doi.org/10.1021/acs.jmedchem.0c02146

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Mechanism of the Aza-Quasi-Favorskii Reaction

Juha H. Siitonen

Department of Chemistry, Rice University, 6500 Main Street, Houston, Texas 77030, USA.

Molecular rearrangements are powerful synthetic tools that allow relatively simple starting materials to be converted to markedly complex products. However, their strategic use in target molecule synthesis necessitates mechanistic understanding.

Herein we discuss our computational work toward unraveling the mechanism of a newly-found rearrangement reaction: the aza-Quasi-Favorskii reaction. Several mechanistic alternatives could be ruled out by theoretical studies (B97D3/def2TZVP) on the system. In addition, we show an example where theory seemingly contradicts the experiment, yet further experimentation shows theory to be right.

The reaction pathways were efficiently mapped by using a toolchain consisting of conformer search using meta-dynamics with genetic crossing (iMTD-GC) combined with the nudged elastic band (NEB) method.¹The resulting pathways were further analyzed using intrinsic bonding orbitals (IBO) to build a quantitative picture of the mechanism that is chemically meaningful for practicing organic chemists.²

Figure 1. Extracting chemically meaningful and experimentally useful mechanistic information from theoretical models of chemical reactions.

[1] D. R. Pract, F. Bohl, S. Grimme Automated exploration of the low-energy chemical space with fast quantum chemical methods, Phys. Chem. Chem. Phys., 2020, 22, 7169.

[2] G. Knizia, Intrinsic atomic orbitals: An unbiased bridge between quantum theory and chemical concepts, J. Chem. Theory Compt. 2013, 9, 4834.

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1 Department of Chemistry and Materials Science; ²Department of Bioproducts and Biosystems, Aalto University, Finland

[email protected] / [email protected] Abstract:

In apolar solvents, surfactants self-assemble to form diverse morphologies. Trace amounts of small polar molecules, such as water, in the apolar solvent encourage the growth of these assemblies. Additionally, the shape and size of these aggregates is sensitive to factors such as surfactant chemistry, concentration, and temperature. Modelling of these systems at molecular level is subject to challenges, including the low charge screening by the dielectric environment of the apolar solvent, the relatively high solvent viscosity, and limitations in the accessible time scale in molecular level modelling. Here, we examine self-assembly and adsorption of surfactants and colloidal species at solid – liquid interfaces in bio-oils and other apolar solvents via a bottom-up approach in which we examine the systems using fully atomistic classical molecular dynamics (MD), coarse-grained soft potentials based dissipative particle dynamics (DPD), and equilibrium state thermodynamics modelling. This combined multiscale approach allows accessing aggregation and adsorption phenomena at different length and time scales such that one can discern the effect of individual molecular features on adsorption strength and aggregation propensity but also examine the effect of these variables on larger scale adsorption vs aggregation equilibrium. Additionally, the work offers insight into the challenges in modelling self-assembly in apolar environments with rigorous comparison to real chemical systems.

Figure 1. Modelling methods at multiple length scales are employed to access effect of e.g. molecular structure, surfactant-surfactant interaction, and adsorbent chemistry on adsorption/ aggregation in bio oils.

References:

[1] Vuorte, M.; Vierros, S.; Kuitunen, S.; Sammalkorpi, M., Adsorption of impurities in vegetable oil: a molecular modelling study. J Colloid Interface Sci, 2020, 571(1), 55-65. https://doi.org/10.1016/j.jcis.2020.03.012

[2] Vuorte, M. I.; Kuitunen, S.; Sammalkorpi, M., Physisorption of bio oil nitrogen compounds onto montmorillonite.

Phys. Chem. Chem. Phys., 2021, 23, 21840. https://doi.org/10.1039/D1CP01880A

[3] Vuorte, M.; Kuitunen, S.; Van Tassel, P. R.; Sammalkorpi, M. Equilibrium state model for adsorption and aggregation in apolar solvent, under preparation, 2021.

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Grand canonical ensemble methods for electrochemistry

Marko M. Melander^a,b

a Department of Chemistry, University of Jyväskylä, Finland

b Nanoscience Center, University of Jyväskylä, Finland Email: [email protected]

Using the electrode potential and the electrolyte to manipulate reaction thermodynamics and kinetics forms the backbone of all electrochemistry and electrocatalysis. Especially important reactions to understand are proton-coupled electron transfer (PCET) reactions forming the mechanistic basis of, e.g., oxygen, CO2, and N2 reduction and hydrogen evolution reactions.

While clever and cheap schemes for evaluating electrochemical thermodynamics and kinetics have been developed, a rigorous treatment is needed 1) to test the accuracy and 2) to define well-controlled computational models.

In my contribution I will present a rigorous theory of grand canonical ensemble density functional theory (GCE-DFT) [1,2] of electrochemical solid-liquid interfaces to compute thermodynamics as a function of the electrode potential and electrolyte concentration. Besides thermodynamics, I will present a generally valid GCE rate theory (GCE-RT)[3] to address PCET reaction kinetics as a function of the electrode potential. The GCE-RT can account for (non-adiabatic) proton and electron tunneling which may significantly contribute to PCET kinetics and long-range electron transfer, respectively. Besides theory, I will present how the thermodynamics, kinetics, and mechanism of hydrogen evolution and oxygen reduction reactions can be addressed and understood from the atomic scale with GCE-DFT and GCE- RT. [3,4] Also approaches combining model Hamiltonians with GCE-DFT and RT to understand electrochemical reactions are briefly discussed. [5,6]

Figure 1. From theory and implementation to computation and understanding of electrochemistry [1] M.M. Melander et al.J. Chem. Phys. 150, 041706 (2019)

[2] M.M. Melander, et al. submitted (2021), preprint DOI:10.33774/chemrxiv-2021-r621x [3] M.M. Melander, J. Electrochem. Soc. 167 116518 (2020)

[4] K. Sakaushi, et al. Phys. Chem. Chem. Phys., ,22, 19401 (2020)

[5] D.Q. Liu et al. submitted (2021), preprint DOI: 10.26434/chemrxiv.14365592.v2 [6]. M.M. Melander, submitted (2021)

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From Absolute Potentials to a Generalized Computational Standard Hydrogen Electrode for Aqueous and Non-aqueous Solvents

Michael Busch^a*, Elisabet Ahlberg^b, Kari Laasonen^a

a Aalto University, Department of Chemistry and Materials Science, 02150 Esbo, Finland

b University of Gothenburg, Department of Chemistry and Molecular Biology, 41296 Gothenburg, Sweden

The prediction of electron transfer (ET) and proton coupled electron transfer (PCET) potentials in non-aqueous solvents using quantum chemical calculations is central to many areas of electrochemistry ranging from energy storage and conversion to electrosynthesis. ET potentials are typically computed using the absolute potential of the standard hydrogen electrode (SHE) in water.[1,2] This connects the redox potential, independent from the solvent the reaction takes place in, directly to the SHE scale in water. The prediction of PCET potentials on the other hand relies on the computational normal hydrogen electrode which uses the formation of H2 in the gas phase as reference.[3] Owing to the use of a reference in the gas phase, the reaction is always offset to the H2 evolution reaction in the solvent of interest. While this is unproblematic when dealing with reactions in aqueous solutions, it results in a situation whereby the redox potentials of the ET and PCET steps are predicted with respect to different reference systems. In principle interconversion to a common scale could be achieved by using tabulated values. However, the required conversion factors are often unreliable or missing.[4] A more promising path is the use of the absolute potential measured or computed in the solvent of interest. So far, the absolute potential is unfortunately only known in very few non-aqueous solvents and often subject to significant uncertainties.

[2,5]

In this contribution we will describe a procedure to predict the absolute potential of the H2

evolution reaction in any solvent based on the experimental pKa of a freely chosen acid in water and few trivial ab-initio computations.[6] Based on this method a generalized normal hydrogen electrode for aqueous and non-aqueous solvents is constructed. Our procedure not only enables the computation of redox reactions in non-aqueous media but also allows for the efficient and reliable computation of acid dissociation constants in all solvents. The validity of this new generalized computational standard hydrogen electrode is tested by computing the absolute potentials of the H2 evolution reaction in 36 organic solvents and ionic liquids.

Global trends in the relative stability of the dissolved proton are evaluated taking advantage of linear solvation energy relations.

References

1 M. Busch, K. Laasonen and E. Ahlberg, Phys. Chem. Chem. Phys., 2020, 22, 25833.

2 A. Marenich, J. Ho, M. Coote, C. Cramer and D. Truhlar, Phys. Chem. Chem. Phys., 2014, 16, 15068.

3 J. Rossmeisl, Z. Qu, H. Zhu, G. Kroes and J. K. Nørskov, J. Electroanal. Chem., 2007, 607, 83.4 A. Isse and A. Gennaro, J. Phys. Chem. B, 2010, 114, 7894.

5 S. Trasatti, Pure & Appl Chem., 1986, 58, 955.

6 M. Busch, E. Ahlberg, K. Laasonen, Phys. Chem. Chem. Phys., 2021, 23, 11727 (HOT paper).

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A DFT investigation into the pH-dependency of glycerol electro-oxidation on gold

L. Laverdure,â A. M. Verma,^b M. M. Melander,â K. Honkalaâ

a Department of chemistry, University of Jyväskylä, Finland

b Department of chemical engineering, Indian Institute of Science, India Email: [email protected]

Electrocatalytic oxidation of glycerol (EOG) is an attractive approach to convert surplus glycerol to value-added products. Experiments have shown that EOG activity and selectivity depend on the electrocatalyst, but also on the electrode potential, the pH, and the electrolyte.

For broadly employed gold (Au) electrocatalysts, high EOG activity under alkaline conditions with glyceric acid as a primary product has been observed. Under acidic and neutral conditions, however, Au is almost inactive producing only small amounts of dihydroxyacetone. Here, we present an extensive mechanistic study examining the pH- and potential-dependency of Au- catalyzed EOG. Our results show that activity and selectivity are controlled by the presence of surface-bound hydroxyl groups. Under alkaline conditions and close to the experimental onset potential, modest OH coverage is preferred. This indicates that both Au(OH)ads and Au can be active sites. Together, they cooperatively facilitate the thermodynamically and kinetically feasible formation of glyceric acid thus explaining the experimentally observed high activity and selectivity. Under acidic conditions, hydroxide coverage is negligible and the dihydroxyacetone emerges as the favoured product. The corresponding reaction barriers, however, are high which explains the low activity and selectivity towards dihydroxyacetone reported in experiments. Overall, our findings demonstrate that computational studies should explicitly account for pH and coverage effects under alkaline conditions for electrocatalytic oxidation reactions to reliably predict electrocatalytic behaviour.

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Machine learning driven materials simulation with TurboGAP

Miguel A. Caro^a

a Department of Electrical Engineering and Automation, Aalto University, Finland Email: [email protected]

Machine learning potentials (MLPs) have gained tremendous momentum in recent year as cheap and accurate surrogates for first-principles simulation [1]. A particularly accurate flavor of MLP is the Gaussian approximation potential (GAP) [2], especially when coupled with accurate many-body atomic descriptors based on a smooth representation of the atomic densities [3,4]. These potentials allow us to tackle problems previously out of reach. An example is the elucidation of the growth mechanism in tetrahedral amorphous carbon [5].

They also allow us to simulate larger systems than is possible with density functional theory and with significantly better accuracy than classical force fields, making for instance the simulation of complex atomic structures in nanoporous materials feasible for the first time [6]. By adding physics-inspired functionality to the non-parametric GAP fit, we can improve the description of long-range interactions, such as van der Waals [7]. In this presentation, I will give an overview of the GAP methodology and its current status, and will introduce TurboGAP, a fast implementation of GAP developed at Aalto University.

[1] V.L. Deringer, M.A. Caro, and G. Csányi. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. Adv. Mater. 31, 1902765 (2019).

[2] A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Gaussian approximation potentials:

The accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).

[3] A.P. Bartók, R. Kondor, and G. Csányi. On representing chemical environments. Phys.

Rev.B 87, 184115 (2013).

[4] M.A. Caro. Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials. Phys. Rev. B 100, 024112 (2019).

[5] M.A. Caro, V.L. Deringer, J. Koskinen, T. Laurila, and G. Csányi. Growth Mechanism and Origin of High sp³ Content in Tetrahedral Amorphous Carbon. Phys. Rev. Lett. 120, 166101 (2018).

[6] Y. Wang, Z. Fan, P. Qian, T. Ala-Nissila, and M.A. Caro. Atomic Structure and Pore Size Distribution in Nanoporous Carbon. arXiv preprint arXiv:2109.10191.

[7] H. Muhli, X. Chen, A.P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M.A.

Caro. Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60. Phys. Rev. B 104, 054106 (2021).

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Ligand assisted hydrogenation of levulinic acid on Pt(111) from firstprinciples calculations

L.

Gell ,^a K. Honkala^a

a Department of Chemistry, Nanoscience Center, P:O. Box 35 University of Jyväskylä Email: [email protected]

In this study, we investigate the hydrogenation reaction of levulinic acid to 4- hydroxypentanovic acid on a ligand-modified Pt(111) using DFT. Modifying nanoparticle surfaces with ligands can have beneficial effects on the desired reaction such as improved selectivity or lower activation energies[1]. The N³,N³-dimethyl-N²-(quinolin-2-yl)propane- 1,2-diamine (AQ) ligand was selected to modify the surface, since it combines good surface adsorption properties with functional groups that can influence the reaction. The adsorption geometry of the AQ ligand was studied as well as the co-adsorption of a second AQ for the possibility of self-assembly. We find that dissociated hydrogen from the Pt(111) surface can protonate the AQ ligand and discuss the role this plays on the mechanism of the hydrogenation reaction of levulinic acid (LA). By comparing the ligand-modified Pt(111) surface to the bare Pt(111) surface we show that the reaction changes from a step-wise to a concerted mechanism due to the influence of the ligand molecule. This demonstrates the effect ligand-modified surfaces can have in catalyzing reactions and shows that desired reactions can be achieved by tuning the reaction environment.

Figure 1. 2AQ ligands adsorbed on Pt(111) surface. Schematic illustration of the proton uptake of surface hydrogen by one AQ ligand.

[1] A. Bell, Science, 2003, 299, 1688-1691.

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Electrochemical CO

2

conversion over single atom and Cu-based alloy catalysts

P. Pršlja,^a M. A. Caro^a, X. Chen^b

a Aalto University, Dept. of Electrical Engineering and Automation

b Aalto University, Dept. of Applied Physics Email: [email protected]

Clean electrochemical CO2 conversion for the production of value-added chemicals using renewable electricity as input is a convenient strategy for reaching sustainability. To make conversion of CO2 efficient it is necessary to design selective and stable catalysts towards a certain product. Cu-based alloys[1] and single-atom catalysts (SACs)[2] have received much attention in electrochemical energy conversion. The alloy nanoclusters have multiple active sites with different performances, which can reduce selectivity toward a specific product.

However, the composition of the alloy can be tuned so they can become catalytically selective.

On the other hand, SACs in which all metal species are atomically dispersed on a solid support can enable 100% atom utilization and often show outstanding selectivity towards C1 products.

Computational techniques such as Machine Learning (using GAP potential), Density Functional Theory combined with the computational hydrogen electrode method[3,4] can be used to evaluate the activity and selectivity of the product distribution. Among the SACs, the Ni derivative (NiNxC) exhibits the highest efficiency for producing CO, where small Ni nanoparticles can disintegrate by Ni(CO)2-like. Also, Co-molecular catalysts anchored on multiwalled carbon nanotubes show promising activity towards C1 hydrocarbons and alcohols, where the product selectivity was explained with the key differences between the binding energies of CH2O and O. Out of Cu-based alloys, CuNiCo is the promising catalyst, where based on the composition and amount of the metals it can be selective towards alcohols (CH3OH or CH3CH2OH) or hydrocarbons (CH4).

[1] J. Zhao, S. Xue, J. Barber, Y. Zhou, J. Mengaand, X. Ke,An overview of Cu-based heterogeneous electrocatalysts for CO2 reduction. J.Mater.Chem.A, 2020,8,4700.

[2] M. Jia, Q. Fan, S. Liu, J. Qiu, Z. Sun, Single-atom catalysis for electrochemical CO2 reduction. Curr. Opin. Green Sustain. Chem., 2019,16, 1-6.

[3]A.A. Peterson, F. Abild-Pedersen, F. Studt, J. Rossmeisl, J.K. Nørskov,

How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels.

Energy Environ. Sci., 2010, 3, 1311

[4]J.K. Nørskov, J. Rossmeisl, A. Logadottir, L. Lindqvist, J.R. Kitchin, T. Bligaard, H.

Jónsson, Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode. J. Phys.

Chem. B., 2004, 108, 17886

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benchmarking the model

Timo Weckman, Derk Kooi, Paola Gori-Giorgi [email protected]

Density functional theory (DFT) has become an important part of modern chemistry, physics and material science. DFT calculations are routinely used to gain new insight as well as to support of experimental research. One of the main challenges in current DFT is the treatment of the long-range dispersion (van der Waals, vdW) interactions. Long-range dispersion interaction, which is the focus of this work, arise from quantum charge fluctuations in the electron density of molecules and atoms. This interaction is an important aspect of chemistry, biology and physics, determining for example protein folding and the structure of DNA.

Current approaches to include dispersion interaction are based on several different physical ideas.[1]

The most common approach is to use a pair-wise empirical correction on top of a DFT calculation performed with a standard approximate functional. However, this requires determining the required interaction parameters before-hand, either from experimental data or from accurate computations.

Changes in these parameters as the environment and structure of a molecule changes are difficult to incorporate in a model. Some non-empirical methods for calculating the dispersion interaction exist (the vdW-DF method[2] being the most widely used) but they tend to be outperformed by the more empirical models.

We have recently introduced a class of variational wave functions for capturing the weak long-range interactions between two systems separated by large distance[3, 4, 5], dubbed the Fixed Diagonal Matrices (FDM) method. The variational wave functions are constructed by explicitly forbidding the deformation of the diagonal of the full many-body spatial density matrix of each monomer. This way we obtain an explicit expression for the dispersion coefficients in terms of the ground-state pair densities of the isolated monomers. The density constraint can be written as an expansion of one-body functions or "dispersals", leading to a surprisingly simple expressions for the dispersion coefficients, based on quantities relying on ground state properties.

The FDM model produces the exact dispersion coefficients between two hydrogen atoms up to C10, and relative errors below 0.2% in the case of two helium atoms[3]. We have recently provided a benchmark[4] for open- and closed-shell systems using different levels of theory for the monomer calculations. The results are very promising: using correlated pair density significantly improves the performance of the model over Hartree–Fock pair density for both atoms and molecules reducing the mean percentage error from 55.2% (Hartree–Fock) to 6.7% (CCSD). In addition to performance, we also discuss the convergence of our results with respect to the basis set and to the size of the dispersal expansion.

In our paper under finalization[5], we extend our method to account for an exchange energy correction for the Hartree–Fock approximation as well as test the performance of the model when using approximate exchange–correlation holes.

References

[1] Martin Stöhr, Troy Van Voorhis, and Alexandre Tkatchenko. Theory and practice of modeling van der waals interactions in electronic-structure calculations. Chem. Soc. Rev., 48:4118–4154, 2019.

[2] Ylva Andersson, David C Langreth, and Bengt I Lundqvist. van der waals interactions in density-functional theory.

Physical Review Letters, 76:102–105, 1996.

[3] Derk P Kooi and Paola Gori-Giorgi. A variational approach to London dispersion interactions without density distortion. Journal of Physical Chemistry Letters, 10(7):1537–1541, 2019.

[4] Derk P Kooi, Timo Weckman, and Paola Gori-Giorgi. Dispersion without many-body density distortion: Assessment on atoms and small molecules. Journal of chemical theory and computation, 17(4):2283–2293, 2021.

[5] Timo Weckman, Derk P Kooi, and Paola Gori-Giorgi. Fixed diagonal matrices approach to dispersion: the effect of approximate exchange-correlation holes. under finalization, 2021.

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Electrical conductivity of doped carbon nanotube networks

Kevin Conley and Antti Karttunen

Department of Chemistry and Materials Science, Aalto University School of Chemical Engineering, P.O. Box 16100, FI-00076 Aalto, Finland

The strong electrical conductivity and material strength properties of carbon nanotube films are useful in a range of commercial applications. The electrical transport through a carbon nanotube network is influenced by physical factors, such as the number of junctions, tube length, and alignment, and the properties of the individual tubes and the junctions. Here we investigate the electronic structure and anisotropy of electrical conductivity of carbon nanotube networks and how they are influenced by atomic and molecular dopants. The conductivity along the tubes and across the junctions is calculated using Density Functional Theory and semiclassical Boltzmann transport theory. In pristine networks, the electrical conductivity is strong along the tubes and the junctions are the bottleneck. This is not always the case in doped networks. The relative conductivity depends on the dopant type, location, and amount. These results are relevant to the electrical transport within carbon nanotube films.

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A New Electronic Structure Code for GPUs

Teemu Järvinen,^a Dage Sundholm^a

a University of Helsinki

Most electronic structure calculations are done with CPU, while the bulk of the computing power of modern HPC hardware is on GPUs, which is emphasized in Finland by coming LUMI-computer. To get access to this unused computing power we have developed a new type of electronic structure code that is specifically designed for GPUs.

There has also been considerable development in programming languages/techniques, like a new programming language specifically designed for scientific computing [1], advances in automatic differentiation etc. These allow a rapid development of a code that is both fast and extremely flexible and able to calculate novel results.

We will present the working principle of the program and some preliminary novel results the program calculate.

[1] Jeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B. Shah, Julia: A Fresh Approach to Numerical Computing. SIAM Review. 2017, 59: 65–98.