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Title: Predicting ultrafast quantum dynamics of 2D antiferromagnets with neuromorphic computing

Speaker: Johan Mentink (Radboud University)
Time: Oct. 6, 2022, 10:00–11:00
Location: TU/e Flux 5.265 and online (MS Teams)

Antiferromagnets host the fastest and smallest magnetic waves of all magnets. However, understanding the properties of these waves at the shortest wavelengths has been challenging even for the simplest model: the antiferromagnetic Heisenberg model in two dimensions [1]. This challenge stems from the failure of quasi-classical approximations, which otherwise have been highly successful in describing both equilibrium and dynamical properties of magnetic materials. To bypass these limitations, I will present our recent efforts that exploit neural network quantum states (NQS) [2], a method inspired from artificial neural networks. The key advantage of NQS is that it provides a systematically improvable approximation of the full many-body wave function with only polynomial computational costs, thereby overcoming limitations of existing methods.

To illustrate the potential of NQS for ultrafast magnetism, I will present recent predictions on the propagation of antiferromagnetic waves [3,4]. Interestingly, we discovered that at the shortest length and time scales the dynamics features supermagnonic propagation: spin correlations that propagate faster than the fastest group or phase velocity of the system. By comparing with interacting spin-wave theory, we identified that the origin stems from the discrete nature of the spins, yielding an effective propagation speed that is up to 40% higher [4].

Inspired by the algorithmic efficiency of NQS, I will outline our efforts towards enhancing the capabilities by running these algorithms on dedicated neuromorphic hardware [5]. As a first step, we measured the energy cost and compute time for inference tasks on conventional hardware and estimated the same metrics for state-of-the-art analog in-memory computing (AIMC). The comparison shows that AIMC can perform these tasks not only with up to three orders of magnitude smaller energy cost, but also in one order of magnitude shorter computation time. This suggests great potential for more sustainable simulation with neuromorphic algorithms, potentially enabling so far uncomputable tasks in computational quantum physics.

[1] H. Shao et al, Phys. Rev. X 7, 041072 (2017)
[2] G. Carleo and M. Troyer, Science 355, 602 (2017)
[3] G. Fabiani & J.H. Mentink, SciPost Phys 7, 004(2019)
[4] G. Fabiani, M.D. Bouman and J.H. Mentink, Phys.

The CCER seminars are aimed at researchers interested in computational approaches to (energy) research. The seminar is small-scale, typically 15 participants, and interactive, offering lots of room for discussion. If you would like to attend, just This email address is being protected from spambots. You need JavaScript enabled to view it. so as to receive the MS Teams meeting link.

Title: t.b.d.

Speaker: Murat Sorkun (DIFFER, Autonomous Energy Materials Discovery group)
Time: Sept. 8, 2022, 10:00–11:00
Location: TU/e Flux 1.124 and online (MS Teams)

Abstract | Visualizing chemical spaces streamlines the analysis of molecular datasets by reducing the information to the human perception level, hence it forms an integral piece of molecular engineering, including chemical library design, high-throughput screening, diversity analysis, and outlier detection. In this talk, I will introduce ChemPlot, a Python library for chemical space visualization. ChemPlot is the first visualization software that tackles the activity/property cliff problem by incorporating tailored similarity. With tailored similarity, the chemical space is constructed in a supervised manner considering target properties.

The CCER seminars are aimed at researchers interested in computational approaches to (energy) research. The seminar is small-scale, typically 15 participants, and interactive, offering lots of room for discussion. If you would like to attend, just This email address is being protected from spambots. You need JavaScript enabled to view it. so as to receive the MS Teams meeting link.

Title: Interfaces in Perovskite Devices through the Lens of DFT

Speaker: Sofia Apergi (TU/e Applied Physics)
Time: June 23, 2022, 10:00–11:00
Location: Hybrid: TU/e (Flux 3.256) and online (MS Teams)

Abstract: In optoelectronic devices consisting of multiple layers of different materials, understanding the properties and phenomena that take place at surfaces and interfaces is of great importance. Such studies are quite challenging for either theory or experiment alone because the complex interplay of the materials properties is decisive for the device performance. The combination of DFT with experiments is an indispensable tool, where DFT can provide atomistic insights, which either explain experimental observations or make predictions to guide experiments. Here, several interfaces between metal halide perovskites and metal oxides present in perovskite devices will be presented. Through these examples it will be demonstrated that with DFT a variety of surface features can be derived, such as chemical reactivity, stability, and electronic properties. The observations made based on DFT results provide important information for the interpretation of experiments and materials engineering strategies to improve the functionality of these interfaces in devices.

The CCER seminars are aimed at researchers interested in computational approaches to (energy) research. The seminar is small-scale, typically 15 participants, and interactive, offering lots of room for discussion. If you would like to attend, just This email address is being protected from spambots. You need JavaScript enabled to view it..

 

 

Title: Thermodynamics of hydrogen/water systems

Speaker: Thijs Vlugt (TUD 3ME)
Time: June 2, 2022, 10:00–11:00
Location: Hybrid: TU/e (Flux 0.300) and online (MS Teams)

Abstract: Hydrogen is one of the most popular alternatives for energy storage. Because of its low volumetric energy density, hydrogen should be compressed for practical storage and transportation purposes. Recently, electrochemical hydrogen compressors (EHCs) have been developed that are capable of compressing hydrogen up to P = 1000 bar at much lower costs. As EHC compressed hydrogen is saturated with water, the maximum water content in gaseous hydrogen should meet the fuel requirements issued by the International Organization for Standardization (ISO) when refuelling fuel cell electric vehicles (< 5ppm). Knowledge on the vapor liquid equilibrium of H2O–H2 mixtures is crucial for designing a method to remove H2O from compressed H2. To the best of our knowledge, the only experimental high pressure data (P > 300 bar) for the H2O–H2 phase coexistence is from 1927 [J. Am. Chem. Soc., 1927, 49, 65–78]. Molecular simulation and thermodynamic modeling was used to study the phase coexistence and thermodynamic properties of the H2O–H2 system. Special simulations using so-called fractional molecules are needed for these simulations. It was found that the presence of water has a significant effect of the properties of compressed hydrogren, and that the water content is generally much larger than 5 ppm so that a drying step is needed. The electro osmotic drag of water inside the membrane of the electrochemical hydrogen compressor is also studied. At the end of my presentation, I present to recent cases of the use of Machine Learning (ML) in thermodynamics: (1) for the computation of partial molar properties from simulations and (2) the combination of thermodynamics and ML to solve arson cases.

The CCER seminars are aimed at researchers interested in computational approaches to (energy) research. The seminar is small-scale, typically 15 participants, and interactive, offering lots of room for discussion. If you would like to attend, just This email address is being protected from spambots. You need JavaScript enabled to view it..