Host: NOMAD Laboratory

The Key Role of Lattice Anharmonicity for Heat and Charge Transport in Solids: Fundamental Concepts, Novel Methods, and Relevant Applications

A Joint Seminar of the NOMAD Laboratory and of the Ma group
Heat and charge transport play a key role in materials science and thus for many technological applications that are key to establish a sustainable energy economy and ecology. Examples include improving the fuel-efficiency of aeronautic turbines [1], for developing efficient thermoelectric devices able to recover useful voltage from otherwise wasted heat [2], and for designing novel battery materials for advancing e-mobility [3]. [more]

Sharing is caring! Mit FAIRem Datenmanagement und KI die Materialien der Zukunft entdecken

Berlin Science Week 2020. Prof. Matthias Scheffler und Prof. Claudia Draxl erklären, wie AI und eine faire Dateninfrastruktur dazu beitragen können, neue Materialien für Umwelt, Energie, Gesundheit und IT-Technologien zu entdecken. [more]

Smart Sampling for Chemical Property Landscapes with BOSS

A Seminar of the NOMAD Laboratory
Atomistic structure search for organic/inorganic heterostructures is made complex by the many degrees of freedom and the need for accurate but costly density-functional theory (DFT) simulations. To accelerate and simplify structure determination in such heterogeneous functional materials, we developed the Bayesian Optimization Structure Search (BOSS) approach [1]. BOSS builds N-dimensional surrogate models for the energy or property landscapes to infer global optima. The models are iteratively refined by sequentially sampling DFT data points with high information content. The uncertainty-led exploration/exploitation sampling strategy delivers global minima with modest sampling, but also ensures visits to less favorable regions of phase space to gather information on rare events and energy barriers. [more]

Introduction to Approximate Bayesian Computation

The goal of statistical inference is to draw conclusions about properties of a population given a finite observed sample. This typically proceeds by first specifying a parametric statistical model (that identifies a likelihood function) for the data generating process which is indexed by parameters that need to be calibrated (estimated). There is always a trade-off between model simplicity / inferencial effort / prediction power. [more]

Interpretable Artificial Intelligence for the not-so-big Data of Materials Science

A Joint Seminar of the NOMAD Laboratory and of the Ma group
The number of possible materials is practically infinite, while only few hundred thousands of (inorganic) materials are known to exist and for few of them even basic properties are systematically known. In order to speed up the identification and design of new and novel optimal materials for a desired property or process, strategies for quick and well-guided exploration of the materials space are highly needed. [more]

Automatic topography of multidimensional probability densities

A Seminar of the NOMAD Laboratory
Unsupervised methods in data analysis aim at obtaining a synthetic description of high-dimensional data landscapes, revealing their structure and their salient features. We will describe an approach for charting complex and heterogeneous data spaces, providing a topography of the high-dimensional probability density from which the data are harvested. [more]

Reaching for the stars with density functional theory

Accurately modeling warm dense matter deep inside astrophysical objects is a grand challenge.The associated thermodynamic states are characterized by solid-state densities, temperatures ofthousands of Kelvin, and GPa pressures. The extreme of the conditions can vary gravely dependingon the mass, radius, and composition of the studied object ranging from several GPa in planetarymantles to millions of GPa at the center of stellar interiors. A method that has proven highlysuccessful in describing this peculiar state of matter is density functional theory moleculardynamics (DFT-MD). [more]

Advancing fundamental science with Machine Learning at DeepMind

Deep learning has had a transformative impact in computer science and is recently being applied to the natural sciences. In this talk, I will give an overview of recently published work on applying Machine Learning techniques to fundamental science problems at DeepMind. I will cover: super-human Quantum Dot tuning, advances in quantum Monte Carlo with neural network ansatz, transfer learning for predicting experimental material properties, and finally, touch upon recent advances in protein structure prediction. These case studies will hopefully allow me to exemplify the three kinds of impact that we can expect in future years: automating the experimental research pipeline, exploiting the representation power of neural network as function forms and finally extracting knowledge from data. [more]

Towards ex-machina computations of transport and transformations in complex materials

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Thermodynamic properties by on-the-fly machine-learned interatomic potentials: thermal transport and phase transitions

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Machine Learning Models of Many-body Atomic and Electronic Interactions

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Excited-electron mediated defect diffusion, secondary electrons, and problems withthermalization

In this talk, I will present on our simulation work of using electronic excitations, induced by laser or ion irradiation, to trigger defect mobility. [more]

From BayBE Steps to Giant Leaps - Industrial Applications of Modern Bayesian Optimization

Bayesian optimization is an old technique revived by using machine learning models at its core. This enables a plethora of extensions that really make the technique a great match for several real world applications. We showcase several examples such as custom and chemical encodings, transfer learning and slot-based mixture modelling enabled by our open-source code BayBE (https://github.com/emdgroup/baybe). [more]

From BIG-Data to HOT-Extreme-Properties of High-Entropy Carbides, Carbo-Nitrides and Borides

The need for improved functionalities in extreme environments is fueling interest in high-entropy ceramics. [more]

Electron and Phonon Transport for Bulk Thermoelectrics, From High-Throughput Screening, Machine Learning Potential, to Electron-Phonon Renormalization

The basic transport properties of thermoelectrics (TEs) are governed by electron and phonon, as well as their interaction. The transport properties have been well-documented for more than half century. [more]

Accurate and Transferable DFT: Machine-Learned aPBE0 and Physics-Based XDM in FHI-Aims

I present the implementation and application of the accurate, machine-learned aPBE0 functional and the exchange-hole dipole moment (XDM) dispersion correction within the FHI-aims code. [more]

Guiding Experiments in Materials Science Using Information Driven Approaches Based on Data as well as Theory

My aim is to show how data, as well as physics based models, can be utilized in conjunction with data science to guide materials discovery. [more]
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