Gerhard Ertl Lecture & Award

Gerhard Ertl Lecture & Award

The Ertl Lecture Award was established in 2008 by the three Berlin universities (Humboldt University, Technical University and Free University) and the Fritz Haber Institute of the Max Planck Society and is awarded once a year. It commemorates former FHI Director Gerhard Ertl's Nobel Prize in Chemistry, which he received in 2007. The prize honours outstanding personalities and researchers in the field of catalysis where Ertl carried out exceptional research for many decades. The prize, sponsored by BASF, includes a one-week research stay at the participating Berlin institutions and a keynote lecture. The winner is typically announced in Spring, the lecture takes place around the December 10th, the anniversary of Ertl's Nobel Prize reception.

Host: TH Department

Towards Predicting the Charge of Electrochemical Interfaces

The charge of an electrochemical interface helps to determine its chemical reactivity and macroscopic electrostatic properties. However, predicting the charge at an electrochemical interface is challenging because it generally requires both the electronic structure of the interface, and the thermodynamically averaged spatial distribution of the electrolyte. Due to this difficulty, interfacial charge prediction remains an open problem in computational electrochemistry. [more]

Accurate Description of Correlated Physics of Bulk Materials using Diagrammatic Methods and Quantum Embedding

This talk will give an overview of continuing work in the Chan group to describe correlated electron materials with high-level quantum chemistry methods. [more]

Theoretical Perspectives on Proton-Coupled Electron Transfer

Proton-coupled electron transfer (PCET) reactions play a vital role in a wide range of chemical and biological processes. This talk will focus on the theory of PCET as well as illustrative applications to catalysis and energy conversion processes. [more]

Blending Old Concepts with Data-driven Approaches to Discover and Classify Homogeneous Catalysts

Sabatier’s principle,[1] developed in the first decades of the 20th century, states that an ideal catalyst should bind a substrate neither too strongly nor too weakly. Today, this simple idea provides the fundamental underpinning for “volcano plots”,[2,3] which are abundantly used in heterogeneous and electrocatalysis.[4] [more]

Computational Understanding of Electrochemical Energy Storage Materials

The complete electrification of the transport sector will require batteries that can be made from abundant chemical species and exhibit significantly greater energy density than current Li-ion batteries. [more]

Data-Enabled Materials Structure-Property-Synthesizability Predictions

The constant demand for new functional energy materials calls for efficient strategies to accelerate the materials discovery. In addressing this challenge, materials informatics deals with the use of data, computations, and machine learning (complementary to experts’ intuitions) to establish the materials structure-property relationships and to make a new functional discovery in a rate that is significantly accelerated. [more]

In Silico Design of Single-Atom- and Highly-Dilute-Alloy Catalysts: Success Stories and Opportunities for Innovation

Catalysis is undoubtedly at the heart of the chemical industry: out of all chemicals manufacturing processes, 85-90% are catalytic and about 80-85% thereof employ heterogeneous catalysts. Yet, developing catalysts for given applications is non-trivial, necessitating empirical and resource-intensive trial-and-error experimentation. Theory and simulation, on the other hand, can provide fundamental insight into the mechanisms underpinning catalytic function, and guide the design of catalytic materials for applications of practical interest. [more]

Machine-Learning Potentials: The Accurate, the Fast, and the Applied

Data-driven algorithms ("machine learning") are increasingly used in science and engineering for analysis, prediction, and control, enabling new insights and applications. A promising example are first-principles simulations of the dynamics of atomistic systems. [more]

Photochemistry and spectroscopy of molecules at surfaces – Insights (mostly) from ab initio molecular dynamics –

The interaction of adsorbates on solid surfaces with light is central to surface spectroscopy, surface photochemistry, and non-adiabatic surface science in general. In the present contribution, light-driven molecular adsorbates will be modelled (mostly) by ab initio molecular dynamics. A few examples will be highlighted: [more]

Machine Learning at the Atomic Scale: From Structural Representations to Chemical Insights

When modeling materials and molecules at the atomic scale, achieving a realistic level of complexity and making quantitative predictions are usually conflicting goals. Data-driven techniques have made great strides towards enabling simulations of materials in realistic conditions with uncompromising accuracy. [more]

Hot Electrons in Surface Chemistry: From Molecular Scattering to Plasmonic Chemistry

Nonadiabatic effects that arise from the concerted motion of electrons and atoms at comparable energy and time scales are omnipresent in thermal and light-driven chemistry at metal surfaces. [more]

Machine Learning (ML) for Simulating Complex Energy Materials with Non-Crystalline Structures

Many materials with applications in energy materials, e.g., catalysis or batteries are non-crystalline with amorphous structures, chemical disorder, and complex compositions, which makes the direct modelling with first principles methods challenging. To address this challenge, we developed accelerated sampling strategies based on ML potentials, genetic algorithms, and molecular-dynamics simulations. [more]

The End of Ab Initio MD

A new computational task has been defined and solved over the past 15 years for extended material systems: the analytic fitting of the Born-Oppenheimer potential energy surface as a function of nuclear coordinates under the assumption of medium-range interactions, 5 ~ 10 Å. [more]

Solving Electrochemistry Puzzles by First-Principles Multi-Scale Modeling

Electrochemistry has become the most-promising prospect towards a sustainable energy landscape. Still, most processes have not been optimized to a level that could compete with fossil fuels. Consequently, substantial optimization of electrode materials, electrolytes and electrochemical cells is required on all size scales to quickly reach the industrially desired performance. [more]

Design of Novel Hybrid and Solid State Battery Materials

Next generation of energy storage devices may largely benefit from fast and solid Li+ ceramic electrolyte conductors to allow for safe and efficient batteries and fast data calculation. For those applications, the ability of Li-oxides to be processed as thin film structures and with high control over Lithiation and phases at low temperature is of essence to control conductivity. [more]

New Concepts in Battery and Solid Electrolyte Design

Solid electrolytes (SEs) are a key component of all-solid-state batteries (ASSBs), which promisehigher energy density along with safer operation compared to commercial Li ion batteries. As theASSB technology matures, research in the field gravitates towards questions regarding stability,scalability, and integration of solid electrolytes into ASSBs with extended cycle life. [more]

Quantum-Chemical Methods for Large Systems: Low-, Linear-, and Sublinear-Scaling Methods

An overview of our recently developed low-, linear-, and sublinear-scaling methods ranging from HF, DFT, MP2 to RPA is given. These methods allow — also in combination with graphics processing units (GPUs) — for the efficient description of large systems at QM and QM/MM levels, where QM spheres with typically 500-1000 atoms are necessary for reliable studies. [more]

The Value of Information: From Statistics to Algorithms

A fundamental question in data science is: how much information can one extract from the data that one collects? [more]

DFTB+, the Fast Way of Quantum Mechanical Simulations

The Density Functional Tight Binding (DFTB) method [1] is an approximate Density Functional Theory (DFT) based framework, which allows for quantum mechanicalsimulations of large systems being typically two or three orders of magnitudefaster than comparable ab initio DFT calculations. [more]

Fully Quantum (Bio)Molecular Simulations: Dream or Reality?

The convergence between accurate quantum-mechanical (QM) models (and codes) with efficient machine learning (ML) methods seem to promise a paradigm shift in molecular simulations. Many challenging applications are now being tackled by increasingly powerful QM/ML methodologies. These include modeling covalent materials, molecules, molecular crystals, surfaces, and even whole proteins in explicit water (https://arxiv.org/abs/2205.08306). [more]

Learning Chemistry from a Computer: Recent Applications of Automatic Mechanism Generation for the Microkinetics of CO2 Methanation on Nickel

Microkinetic mechanisms in heterogeneous catalysis can be incredibly complex, and the development of these mechanisms raises many problems. How do you determine which species and reactions to include? How do you obtain the corresponding parameters? [more]

New ’Low-Cost’ Electronic Structure Methods for Large Systems

All widely used semi-empirical quantum chemical methods like PM6, DFTB, or GFN-xTB are formulated in a (almost) minimal basis set of atomic orbitals, which limits the achievable accuracy for many important chemical properties. [more]

Constructing Defect Phase Diagrams from Ab Initio Calculations

Thermodynamic bulk phase diagrams have become the roadmap used by researchers to identify alloy compositions and process conditions that result in novel materials with tailored microstructures. [more]

Fully Quantum (Bio)Molecular Simulations: Dream or Reality?

The convergence between accurate quantum-mechanical (QM) models (and codes) with efficient machine learning (ML) methods seem to promise a paradigm shift in molecular simulations. Many challenging applications are now being tackled by increasingly powerful QM/ML methodologies. These include modeling covalent materials, molecules, molecular crystals, surfaces, and even whole proteins in explicit water (https://arxiv.org/abs/2205.08306). [more]

Coupling the Time-Warp Algorithm with the Graph-Theoretical KMC Approach for Catalysis Simulations on Mega-Lattices and Beyond

Kinetic Monte-Carlo (KMC) simulations have been instrumental in multiscale catalysis studies, enabling the elucidation of the complex dynamics of heterogeneous catalysts and the prediction of macroscopic performance metrics, such as activity and selectivity. However, the accessible length- and timescales are still limited, and handling lattices containing millions of sites with “traditional” sequential KMC implementations becomes prohibitive due to large memory requirements and long simulation times. [more]

An Atom's Eye View of Electrochemical Energy Transformations

The transition away from fossil fuels will provide the defining challenge for the next generation of chemists and engineers, and electrochemical technologies are crucial for this transformation. By providing a link between (renewable) electricity sources and chemicals, these technologies allow not only for storage and transportation of energy, but also provide routes to synthesize a wide range of chemicals and materials that today are integrally reliant upon fossil fuels. [more]

From Levinthal’s Paradox to the Enigma of Disappearing Polymorphs: Using Minima Hopping to Explore the Synthesizability and Stability of Materials

Each local Minimum on the potential energy surfaces corresponds to a stable structure. In a theoretical structure search one typically finds a number of low energy minima that is much larger than the number of experimentally known structures. [more]

Things MOFs do, a Journey Skirting the Edges of Sanity

Metal Organic Frameworks, or MOFs for short, are a class of materials that consist of organic molecules that link together metal centres to form an ordered, often porous solid. This multi-component nature affords MOFs a great versatility in terms of their internal structure and properties and thus a wide array of potential use cases. Unfortunately, at least from the point of a theorist, their metal-organic nature also makes them a pain in the neck to treat. Especially when comparing theoretical results to experiment it often turns out that MOFs are significantly less well behaved than advertised. [more]

Fritzn's Wiesn - FHI Summer Party 2023

The Theory Department invites you to this year's FHI Summer Party, a Prussian-Bavarian summer festival with the motto "Fritzn's Wiesn". [more]

Electron-Phonon Coupling from First-Principles

For this talk, I will highlight the importance of electron-phonon interaction to describe many experimental phenomena including carrier mobility, phonon-assisted optical absorption, phonon-limited superconductivity, zero-point renormalization, temperature dependence of the bandgaps, electron mass enhancement and polaron liquids.I will show how to derive and efficiently compute electron-phonon interaction from first principles focusing on two manifestations of the electron-phonon coupling: carrier mobility and temperature dependence of the bandgap. [more]

Combining Experimental and Theorical Data for Accurate Predictions and Mechanistic Insights in Heterogeneous Catalysis

First-principles prediction of heterogeneous catalytic performance is challenging due to the complexity of real catalysts and their evolution over time. But even for simple model catalyst surfaces, chemical accuracy (or sufficient accuracy to discriminate the rates of many potentially competing mechanisms) in predictions of reaction energetics on transition metal surfaces is lacking due to difficulties in simultaneously describing metallic states in the catalyst and molecular adsorbate states and the interactions between them. [more]

Lithium, Interfaces & Action: Desiging Solid Battery Materials

Next generation of energy storage devices may largely benefit from fast and solid Li+ ceramic electrolyte conductors to allow for safe and efficient batteries. For those applications, the ability of Li-oxides to engineer their interfaces and be processed as thin film structures and with high control over Lithiation and phases at low temperature is of essence to control conductivity. [more]

Nanoparticles with Cubic Symmetry: Classification of Polyhedral Shapes

The detailed characterization of polyhedral bodies, while a subject of mathematical research since ancient times, has attracted new interest in connection with crystalline nanoparticles (NP). [more]

The Exact Factorization, a Universal Approach to Non-Adiabaticity

The adiabatic approximation describes the molecular wave function as a single product of a Born-Oppenheimer state and a nuclear wave packet. This approximation is a corner stone of modern quantum chemistry and solid-state physics. It not only makes computations feasible, it also provides us with an intuitive picture of many chemical processes. [more]

Towards the Accurate Simulation of Electrochemical Interfaces by Combining Electron Density and Long-Range Machine-Learning Methods

The accurate study of electrochemical interfaces calls for simulation techniques that can treat the electronic response of metal electrodes under electrostatic perturbations. Despite recent advancements in atomistic machine-learning (ML) methods applied to electronic-structure properties, predicting the non-local behavior of the charge density in electronic conductors remains a majoropen challenge. [more]

Self-Interaction of Polarons Addressed through the Piecewise Linearity Condition

The piecewise linearity condition is a property satisfied by the exact density functional and has been found to yield band gaps in accord with experiment [1,2] when imposed to hybrid functionals. [more]

Embedded Cluster Models and Solvation for the Simulation of Electrocatalysis

Heterogeneous electrocatalytic processes are notoriously difficult to simulate due to i) the complex structure of the catalyst and the electric double layer, ii) the applied bias potential that drives the reaction and iii) the variety of length and time scales on which relevant transformations happen. Accurate atomistic simulations must therefore be able to explore the configurational space of the catalyst surface – which requires fast electronic-structure methods – and enable the calculation of accurate free energies and reaction kinetics – which requires accurate electronic-structure methods. [more]

Quantum Sensors in Diamond for Nano- and Microscale Magnetic Resonance Applications

Nitrogen vacancy (NV) point defects in diamond have emerged as a promising platform for quantum sensing. The electronic spin state of these solid-state qubits can be optically polarised, coherently manipulated with microwave pulses, and read out via their spin-state-dependent photoluminescence. Using this optically detected magnetic resonance method, magnetic signals from a single molecules or spins can be detected. [more]

TH-Seminar: Prof. Julia Westermayr

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