We developed a machine-learning-based approach for solving computing the elastoplastic mechanical response of polycrystalline structures. In particular, a recursive deep neural network based on U-Net and applied recursively is proposed as a surrogate model for predicting the von Mises stress field under quasi-static tensile loading. We show that the model can accurately predict both the...
The performance of heterogeneous catalysts is governed by an intricate interplay of several multi-scale processes. Thus, it is rather challenging to identify the most relevant parameters for the design of the catalyst and its support material. Here, we combine experimental and theoretical descriptive parameters characterizing cobalt nanoparticles dispersed on SiO2 supports modified...
An accurate description of the surface of Pd-based catalysts under reaction conditions is a critical step toward a deeper understanding of catalyst reactivity. Herein, by modeling the phase diagram of the (111) and (100) surfaces of face-centered cubic Pd via ab initio atomistic thermodynamics, we predict the stable hydrogen coverages for a wide range of temperatures and H2 pressures. The...
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) obtains chemical information on a sub-micron scale. Traditionally, experts analyze the spectra in a time-consuming manner, and the complexity of the data limits what can be extracted by inspection. Machine learning could push the limits of ToF-SIMS on various aspects. Machine-learning-enhanced identification of atomic and molecular...
Photo-electron spectra obtained with intense pulses generated by free-electron lasers through self-amplified spontaneous emission are intrinsically noisy and vary from shot to shot. We extract the purified spectrum, corresponding to a Fourier-limited pulse, with the help of a deep neural network. It is trained on a huge number of spectra, which was made possible by an extremely efficient...
