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]

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]

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]

Sabatier’s principle,^[1] developed in the first decades of the 20^th 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Die Abteilung Theorie läd zum diesjährigen FHI-Sommerfest, einem preußisch-bayerisches Sommerfest unter dem Motto “Fritzn's Wiesn” ein. [more]

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

AI-accelerated Organic Synthesis is an emerging field that uses machine learning algorithms to improve the efficiency and productivity of chemical synthesis. [more]

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]

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]

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]

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 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]

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]

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]

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]

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]

Chemical reactions are fundamental to drive the transformation of matter and are pivotal across diverse domains like medicine, materials science, and energy generation. [more]

In transmission electron microscopy we record the intensity of an electron wave that has been created by the illumination optics, transmitted through a thin specimen, and then projected onto a detector. From that recorded intensity we want to learn about the specimen. [more]

With machine-learnt molecular potential energy functions, most notably neural-network potentials, one can now sample chemical reactions and the influence of the environment on these reactions, as well as processes at high temperature and pressure at which the approximations of empirical molecular potentials break down. [more]

Electronic orbitals are a foundational concept in modern approaches to interfacial electrochemistry. While they are essential in many cases, the high computational cost of finding them could hinder the proper understanding of important practical problems. [more]

Large-scale reconstructions, nanostructures, defects and moire patterns typically entail length scales of several nanometers. Their ab-initio modeling thus requires unit cells with a prohibitively large number of atoms. For example, the unit cell of magic-angle bilayer graphene, featuring superconducting many-body states, includes around 12 000 carbon atoms. [more]

The presence of water has been shown to enhance hydrogenation of polar chemical functional groups, such as C=O and N=O bonds, through proton shuttling. To demonstrate such rather sophisticated reaction pathways, explicit solvent models with dynamic change of local solvent structures should be considered. Beyond what we reported previously for water-promoted C=O hydrogenation in furfural¹, in this presentation, we will highlight how the dynamics of the local water structures within the first solvation shell may affect the hydrogenation kinetics. [more]

Transition metal oxide and nitride semiconductors offer considerable promise for a range of applications, from sustainable (opto)electronics to photocatalytic energy conversion. However, such materials are characterized by complex carrier-lattice couplings, defect properties, and chemical susceptibilities that must be characterized and controlled to enable their implementation in functional systems. [more]

While the GW method is well-established for calculating valence photoemission spectra of solids and molecules [1], its application to deep core levels with excitation energies exceeding 100 eV is a more recent advancement. [more]

The functional properties of ceramics are usually tailored by designing point defects and interfaces. Dislocations as heavily charged nanoscale one-dimensional line defects are so far underrepresented means to tune functionality. However, the opportunity to tune ceramics beyond what can be achieved by chemical doping is of significant interest. [more]

Atomic simulations allow fantastic insight for chemistry and materials science, but in silico data has many uncertainties which complicate interpretation and prediction. I will discuss recent Bayesian methods to quantify two important sources of uncertainty: those arising from imperfect models for atomic interaction[1,2] and incomplete sampling of the atomic energy landscape[3]. [more]

[more]

[more]