Vienna Conference April 2023

Time	Speaker	Title	Abstract
Wednesday, April 3rd
10:25-10:30	Florian Libisch	Welcome words
10:30-11:15	Maytal Caspary Toroker	Charge transport through catalytic materials	View
11:15-12:00	Titus van Erp	Utilizing path sampling simulations for studying REDOX reactions	View
12:00-13:40	Lunch
13:40-14:25	Konstantin Neyman	Approaching complexity of nanomaterials for catalysis by DFT modelling (invited lecture)	View
14:25-14:50	Assa Aravindh	Combined structure prediction and density functional theory investigations on efficient catalytic materials.	View
14:50-15:15	Bartek Szyja	On the Design of Metal-Support Interface in Methanol Electrocatalytic Oxidation	View
15:15-15:45	Coffee
15:45-16:30	Jose Gomes	Modelling of materials across multiple scales	View
16:30-16:55	Ugur Bozkaya	Molecular Fragmentation Approaches for Excited States	View
16:55-17:20	Prince Gollapalli	Evaluation of Mn doped perovskite-type oxides as electrolyte materials for efficient hydrogen conversion	View
17:20-19:00	Postersession (and core-group meeting in Sem BA 10 B)
Thursday April 4th
9:00-9:45	A. Daniel Boese	Hydrogen Bonding: From Dimers to Molecular Crystals	View
9:45-10:30	Andreas Grüneis	Coupled-cluster theory for condensed phase systems
10:30-11:00	Coffee
11:00-11:25	Felix Hummel	Temperature and Fractional Electron Numbers in Coupled-Cluster Methods	View
11:25-12:10	Gareth Parkinson	An atomic-scale view of single-atom catalysis	View, PDF
12:10-12:35	Anja Bieberle-Hütter	Microkinetic modeling of the oxygen evolution reaction: extended model and sensitivity analysis	View
12:35-13:50	Lunch
13:50-14:35	Simone Piccinin	On the role of surface hole accumulation in the oxygen evolution reaction	View
14:35-15:00	Peter Vancso	Higher-order moiré patterns in MoTe2/graphene heterostructure	View
15:00-15:25	Luliana Marin	Enhanced OpenMM Algorithms for CUDA and OpenCL Incorporating Novel Stochastic Dynamics and the Berendsen Thermostat	View
15:25-15:55	Coffee
15:55-16:40	Aleix Comas-Vives	Deciphering Heterogeneous Catalysts: Theoretical Insights	View
16:40-17:05	Dorota Rutkowska-Zbik	Pt Chini clusters for H2 generation	View
17:05-17:10	Veera Krasnenko	A database for Metal-Organic Frameworks (MOFS)
17:15-18:00	MC meeting
Friday April 5th
9:00-9:45	Ana Munoz Garcia	Exploring electrochemistry in energy storage materials and interfaces with ab initio methods	View
9:45-10:30	Georg Madsen	Machine-learned force fields for metal-liquid interfaces	View
10:30-10:50	Coffee
10:50-11:35	Karsten Reuter	Exploring catalytic reaction networks with machine learning
11:35-12:20	Leticia Gonzalez	Excited state modelling in photosensitizers for photocatalysis	View
12:20-13:20	Lunch
13:20-13:30	Group picture at the terrace
13:30-14:15	Georg Kresse	Machine learning and beyond DFT methods: quantitative materials modeling at your fingertips	View
14:15-15:00	Dominik Eder	Metalorganic Frameworks as Next-Generation Photocatalysts

Presenter	Title	Abstract
Klemen Bohinc	Charge properties of TiO2 nanotubes	View
Alejandro Gallo	ATRIP: A Massively Parallel CCSD(T) implementation for the Exascale Computing Age on CPUs and GPUs	View
Richard Jacobi	Structural properties of a DNA-based light-harvesting system	View
Nebojsa Potkonjak	Synergetic effect of co-deposits in electrocatalytic hydrogen production	View
Ivan Radović	Stopping force acting on a charged particle moving over a drift-current biased supported graphene	View, PDF
Tobias Schäfer	Surface Science using highly-accurate quantum chemistry methods	View
Radhey Shyam Yadav	The origins of Formic acid electrooxidation on selected surfaces of Pt, Pd, and their alloys with Sn.	View
Rafał Stottko	Can Machine Learning Predict the Reaction Paths in Catalytic CO2 Reduction on Small Cu/Ni Clusters?	View
Márton Szendrő	Ultra-flat twisted superlattices in 2D heterostructures	View

Utilizing path sampling simulations for studying REDOX reactions

Redox reactions play a pivotal role in various chemical and biological processes, serving as fundamental mechanisms for electron transfer. In this talk, I explore an application of path sampling simulations to elucidate the intricate dynamics of electron transfer between ruthenium ions within the medium of liquid water. Our study unveils the subtle interplay between correlated electron and proton transfer reactions. By utilizing the RETIS algorithm, enhanced by the newly developed highly parallelizable version called ∞RETIS, we showcase the capability to compute rates of complex reaction mechanisms at a dramatically faster pace.

Multiscale modeling of the oxygen evolution reaction on hematite: Challenges and chances

The modeling and simulation efforts on electrochemical interfaces has increased strongly in the past years related to increased focus and awareness of the energy transition, artificial intelligence and data mining, and the need for better understanding of experimental interfaces and data. In this presentation, I want to discuss the challenges and chances of multiscale modeling of electrochemical interfaces. I will focus on the oxygen evolution reaction on hematite. I will present our multiscale, microkinetic modeling approach by means of this example. [1-3] Throughout the talk, I will explain the specific challenges and chances. In the end, I will summarize all challenges and chances, and complement them with additional aspects from recent review papers. [4,5] With this, I want to give the audience a good overview of where we stand and what still needs to be done in order to be able to identify the reaction mechanisms at electrochemical interfaces with the help of modeling. I will further motivate how our approach can be used for other reactions than the oxygen evolution reaction on hematite surface. [6,7]

1. K. George, M. van Berkel, X. Zhang, R. Sinha, A. Bieberle-Hütter, J. Phys. Chem. C 2019, 123 (15), 9981.

2. K. George, T. Khachatrjan, M. van Berkel, V. Sinha, A. Bieberle-Hütter, ACS Cataysis 2020, 10, 14649.

3. B.F.H. van den Boorn, M. van Berkel, A. Bieberle-Hütter, Adv. Theory Simul. (2022) 2200615 (1-12).

4. Q. Zhang and A. Bieberle-Hütter, ChemSusChem 9 (2016) 1223 – 1242.

5. B. Samanta, Á. Morales-García, F. Illas, N. Goga, J.A. Anta, S. Calero, A. Bieberle-Hütter, F. Libisch, A. B. Muñoz-García, M. Pavone, M.C. Toroker, Chem. Soc. Rev., 2022, 51, 3794.

6. A. Bieberle, L.J. Gauckler, Solid State Ionics 146 (2002) 23-41.

7. M. Vogler, A. Bieberle-Hütter, L.J. Gauckler, J. Warnatz, W.G. Bessler, J. Electrochem. Soc. 156 (5) (2009) B663-B672.

On the role of surface hole accumulation in the oxygen evolution reaction

"The oxygen evolution reaction (OER) plays a crucial role in (photo)electrochemical devices that use renewable energy to produce synthetic fuels. OER is a 4-hole process leading to the oxidation of two water molecules and the release of molecular oxygen and is considered the bottleneck of water splitting. While the mechanism of this reaction is still debated, recent measurements on semiconducting oxides have shown that the dependence of the rate of OER on the surface hole density is a power law, suggesting a multihole mechanism via surface hole accumulation. This is reminiscent of the mechanism for OER promoted by the oxygen evolving complex in Photosystem II and in stark contrast with metallic oxides like IrO2, where the dependence is exponential. In this talk I will discuss our recent findings [1] where, using transient photocurrent measurements, density functional theory simulations and microkinetic modelling, we try to shed light on the origin of this behavior in hematite, alpha-Fe2O3. We find an approximate third order behavior of the OER rate on the surface hole density. We propose a model where the reaction proceeds through a sequence of 1-electron oxidations of surface hydroxy groups leading to surface hole accumulation at oxygen sites. The key O-O bond formation proceeds through a chemical step, the dissociative chemisorption of a hydroxide ion at an oxyl active site in the presence of two nearby surface holes, leading to the direct formation of the superoxo intermediate via a three-hole step. We find that, at variance with the IrO2 case [2], the activation energy of this step is weakly dependent on the surface hole coverage, leading to the observed power law.
[1] G. Righi, J. Plescher, F.-P. Schmidt, R. Kramer Campen, S. Fabris, A. Knop-Gericke, R. Schlögl, D. Teschner, T. E. Jones, S. Piccinin, On the origin of multihole oxygen evolution in hematite photoanodes, Nature Catalysis 2022, 5, 888–899
[2] H. N. Nong, L. J. Falling, A. Bergmann, M. Klingenhof, H. P. Tran, C. Spöri, R. Mom, J. Timoshenko, G. Zichittella, A. Knop-Gericke, S. Piccinin, J. Pérez-Ramírez, B. Roldan Cuenya, R. Schlögl, P. Strasser, D. Teschner, T. E. Jones, Key role of chemistry versus bias in electrocatalytic oxygen evolution, Nature 2020, 587, 408–413 "

Machine learning and beyond DFT methods: quantitative materials modeling at your fingertips

I will present two closely intertwined key developments that we have pursued in recent years: machine-learned force fields (MLFF) – with an emphasis on Δ-learning – and the development of a quantum Monte-Carlo method (auxiliary-field QMC), both closely tied to VASP. The talk will begin with an overview of density functional theory and its shortcomings, a brief introduction to the random-phase approximation and the MLFF approach implemented in VASP, and then highlight applications that rely on Δ-learning.
Δ-learning is an efficient approach to construct a machine learned force field for density functionals or correlated wavefunction methods that are expensive to evaluate. We have now used this approach for many materials, including zirconia [1], SrTiO3 [3] and CO adsorbed on Rh(111) [2], as well as liquid water. For the first three applications we have learned the difference between a semi-local functional and the random phase approximation. Typically, we find that the number of computations for the expensive method can be reduced by one to almost two orders of magnitude using Δ-learning. The final machine-learned force field has the same speed and convenience as any other force field and can be used to make finite temperature predictions or exhaustive searches of the configuration space as for any other MLFF. For water, we have learned the difference between expensive hybrid functionals and a semi-local functional. Remarkably as few as 20 hybrid functional calculations are sufficient to determine an MLFF that has comparable accuracy to conventional DFT-based MLFFs. This means that the difference between different approximations can be learned with very little training data.
It also means that correlated wavefunction methods, often thought to be too expensive for routine computations, become a serious option for materials modeling. This has led us to develop and implement the auxiliary-field QMC [4], a method that is fully compatible with other quantum-chemistry methods but shows much more favorable scaling. First applications for small molecules and solids are presented [5-6].
References
1. Liu, Verdi, Karsai, and Kresse, Phys. Rev. B 105, L060102 (2022)
2. Liu, Wang, Avargues, Verdi, Singraber, Karsai, Chen, and Kresse, Phys. Rev. Lett. 130, 078001 (2023)
3. Verdi, Ranalli, Franchini, and Kresse, Phys. Rev. Materials 7, L030801 (2023)
4. Zhang, Krakauer, PRL 90, 136401 (2003)
5. Surkurma, Schlipf, Hummer, Taheridehkordi, Kresse, JCTC (2023)
6. Taheridehkordi, Surkurma, Schlipf, Hummer, Kresse, J. Chem. Phys. 159, 044109 (2023)"

Exploring electrochemistry in energy storage materials and interfaces with ab initio methods

Rechargeable batteries are at the core of the current energy transition toward sustainable technologies including electric transport and internet of things. Beyond the remarkable social impact of Li-ion batteries (LIBs), recognized by the 2019 Nobel prize in Chemistry, several new chemistries and architectures have emerged to address higher energy density, increased scalability, stability, safety, and cost-effectiveness. The identification of novel materials and an in-depth comprehension of their electrochemical behavior are crucial steps in the whole production process prior to the ultimate application in real devices. Electrochemical reactions within batteries are complex phenomena, often including undesired and even detrimental processes that determine the overall system performance. In this context, computer-based investigations can be extremely beneficial for spanning the atomic-to-macro length scales in order to understand the intertwined physico-chemical processes at play and to offer guidelines towards the desired improvement in stability and performance. This contribution will discuss recent findings by our group regarding the computational understanding and design of materials and interfaces for novel batteries. First, we will discuss Na-ion batteries (NIBs), recognized among the most valid alternatives to LIBs for the sustainability of Na and operational similarities. Unfortunately, the Li-to-Na replacement in many cathodes leads to extremely puzzling electrochemistry, involving unexpected phase transformations upon cycling. In this contribution, we will focus on the structural and electronic feature variations at different states of charge leading to the P2P2’ and P2OP4 transitions on the NaxMnO2 prototype cathode [1]. Moreover, we will discuss the rich anionic chemistry of Nax(Mn,Ni)O2, which pushes the cathode capacity beyond the limit of the transition metal redox potential and how strategic doping can hinder the detrimental release of molecular oxygen associated with the process [2,3]. The second part of the talk will be devoted to two challenges related to Li-based batteries that require computational approaches beyond workhorse DFT: (1) the evolution of 1O2 at the Li2O2 surface during oxidation, culprit of parasitic side reactions, degradation and poor rechargeability of Li-air batteries, where we apply an embedded cluster approach, based on CASPT2 and effective point charges [4] and (2) the formation of a protective artificial solid electrolyte interface on a Li metal anode able to block the formation of dendrites, leading to short circuits and thermal runaway. Due to the very different electronic nature of the organic electrolyte and the metal surface, we employ Density Functional Embedding Theory (DFET) to combine the best feasible computational approaches to treat molecular species with localized charge (i.e., the organic electrolyte derivatives upon the reductive decomposition process) and the Li metal surface [5]. [1] A. Langella, A. Massaro, A.B. Muñoz-García, M. Pavone. Chem. Mater. (2024) 36, 2370 [2] A. Massaro, A.B. Muñoz-García, P.P. Prosini, C. Gerbaldi, M. Pavone. ACS Energy Lett. (2021) 6, 2470 [3] A. Massaro, A. Langella, C. Gerbaldi, G. A. Elia, A.B. Muñoz-García, M. Pavone. ACS Appl. Energy Mater. (2022), 5, 10721 [4] F. Fasulo, A. Massaro, A.B. Muñoz-García, M. Pavone. J. Chem. Theory Comput. (2023), 19, 5210 [5] F. Fasulo, A.B. Muñoz-García, A. Massaro, O. Crescenzi, C. Huang, M. Pavone . J. Mater. Chem. A (2023), 11, 5660

Charge transport through catalytic materials

Functional materials are important for applications in the fields of catalysis and renewable energy. Specific functionalities include charge transport through electronic material components as well as catalytic reactivity on material surfaces. In the talk, I will advocate that there is a relation between charge transport efficiency and reactivity, and therefore developing novel algorithms that calculate both are important for better understanding of intrinsic material limitations. We cover our latest results in developing and using charge transport calculation methods and demonstrate them on catalytic materials. Our home code is developed on a user-friendly GUI and enables to use widely available Density Functional Theory results as input. The methodology is demonstrated on several generic materials such as zinc blende semiconductors as well as on Fe2O3, a well-studied water oxidation catalyst.
Reference:

L. Lakhanlal and M. Caspary Toroker, “Filling the gaps on the relation between electronic conductivity and catalysis of electrocatalysts for water splitting using computational modelling”, Current Opinion in Electrochemistry 101342 (2023).
B. Samanta, A. Morales-Garcia, F. Illas, N. Goga, J. A. Anta, S. Calero, A. Bieberle-Hutter, F. Libisch, A. B. Munoz-Garcia, M. Pavone, Maytal Caspary Toroker, “Challenges of modeling nanostructured materials for photocatalytic water splitting”, Chem. Soc. Rev. 51, 3794 (2022).
N. Snir and M. Caspary Toroker, “An efficient method to calculate kinetic energy barriers for surface catalysis”, Adv. Theo. Sim. 2300182 (2023).
N. Snir, N. Yatom, and M. Caspary Toroker, “Progress in understanding hematite electrochemistry through computational modeling”, Computational Materials Science 160, 411 (2019).
Y. Ou, L. P. Twight, B. Samanta, L. Liu, S. Bismas, J. L. Fehrs, N. A. Sagui, J. Villalobos, J. Morales-Santelices, D. Antipin, M. Risch, M. Caspary Toroker, S. W. Boettcher, “Cooperative Fe sites on transition metal (oxy) hydroxides drive oxygen evolution activity in base”, Nature Communications 14, 7688 (2023).
N. Snir and M. Caspary Toroker, “Simulations for charge transfer and photocurrent calculations using hematite for green hydrogen production”, Energy Advances 10.1039/D3YA00366C, (2024).

Conference April 3rd-5th 2024

Catalysts for water splitting and energy storage

Links

Invited Speakers

Program

Posters

Venue

Survey

Conference dinner

Contact Information

Links

Invited Speakers

Program

Posters

Venue

Survey

Conference dinner

Contact Information

Utilizing path sampling simulations for studying REDOX reactions

Approaching complexity of nanomaterials for catalysis by DFT modelling

Molecular Fragmentation Approaches for Excited States

Combined structure prediction and density functional theory investigations on efficient catalytic materials.

Multiscale modeling of the oxygen evolution reaction on hematite: Challenges and chances

On the Design of Metal-Support Interface in Methanol Electrocatalytic Oxidation

Stopping force acting on a charged particle moving over a drift-current biased supported graphene

Synergetic effect of co-deposits in electrocatalytic hydrogen production

Higher-order moiré patterns in MoTe2/graphene heterostructure

Ultra-flat twisted superlattices in 2D heterostructures

Can Machine Learning Predict the Reaction Paths in Catalytic CO2 Reduction on Small Cu/Ni Clusters?

Understanding the efficiency of different hydrogenation methods for TiO2

Enhanced OpenMM Algorithms for CUDA and OpenCL Incorporating Novel Stochastic Dynamics and the Berendsen Thermostat

Deciphering Heterogeneous Catalysts: Theoretical Insights

Pt Chini clusters for H2 generation

On the role of surface hole accumulation in the oxygen evolution reaction

Structural properties of a DNA-based light-harvesting system

Machine-learned force fields for metal-liquid interfaces

Machine learning and beyond DFT methods: quantitative materials modeling at your fingertips

Modelling of materials across multiple scales

Evaluation of Mn doped perovskite-type oxides as electrolyte materials for efficient hydrogen conversion

Excited state modelling in photosensitizers for photocatalysis

The origins of Formic acid electrooxidation on selected surfaces of Pt, Pd, and their alloys with Sn.

ATRIP: A Massively Parallel CCSD(T) implementation for the Exascale Computing Age on CPUs and GPUs

Exploring electrochemistry in energy storage materials and interfaces with ab initio methods

Surface Science using highly-accurate quantum chemistry methods

Hydrogen Bonding: From Dimers to Molecular Crystals

Temperature and Fractional Electron Numbers in Coupled-Cluster Methods

An atomic-scale view of single-atom catalysis

Charge transport through catalytic materials

Charge properties of TiO2 nanotubes