TY - GEN A1 - George, Janine A1 - Petretto, G. A1 - Naik, Aakash Ashok A1 - Esters, M. A1 - Jackson, A. J. A1 - Nelson, R. A1 - Dronskowski, R. A1 - Rignanese, G.-M. A1 - Hautier, G. T1 - Automated bonding analysis with crystal orbital Hamilton populations (program code LobsterPy) N2 - This is the code for the program LobsterPy that can be used to automatically analyze and plot outputs of the program Lobster. KW - Automation KW - High-throughput computations KW - Bonding analysis PY - 2022 UR - https://doi.org/10.5281/zenodo.6320074 UR - https://doi.org/10.5281/zenodo.6415169 UR - https://doi.org/10.5281/zenodo.6415336 UR - https://doi.org/10.5281/zenodo.6581118 DO - https://doi.org/10.5281/zenodo.6320073 PB - Zenodo CY - Geneva AN - OPUS4-55174 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine T1 - Raw data for "Automated bonding analysis with crystal orbital Hamilton populations" N2 - Raw data corresponding to the following paper: 10.1002/cplu.202200123. KW - Automation KW - High-throughput computations KW - Bonding analysis PY - 2022 UR - https://doi.org/10.5281/zenodo.6373369 DO - https://doi.org/10.5281/zenodo.6373368 PB - Zenodo CY - Geneva AN - OPUS4-55175 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine A1 - Naik, Aakash Ashok A1 - Jackson, A. J. A1 - Baird, S. T1 - Scripts to reproduce "Automated bonding analysis with crystal orbital Hamilton populations" N2 - This repo allows to recreate our publication: https://doi.org/10.1002/cplu.202200123 In contrast to 0.2.2, we fixed an issue with absolute path. KW - Automation KW - High-throughput computations KW - Bonding analysis PY - 2022 UR - https://github.com/JaGeo/LobsterAutomation UR - https://doi.org/10.5281/zenodo.6421928 UR - https://doi.org/10.5281/zenodo.6595062 UR - https://doi.org/10.5281/zenodo.6599556 UR - https://doi.org/10.5281/zenodo.6674670 UR - https://doi.org/10.5281/zenodo.6704163 DO - https://doi.org/10.5281/zenodo.6421927 PB - Zenodo CY - Geneva AN - OPUS4-55177 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - New chemical understanding with the help of automation and highthroughput computations N2 - High-throughput computations are nowadays an established way to suggest new candidate materials for applications to experimentalists. Due to new packages for automation and access to databases of computed materials properties, these studies became more and more complex over the last years. Besides suggesting new candidate materials for applications, they also offer a way to understanding the materials properties based on chemical bonds. For example, we have recently used orbital-based bonding analysis to understand the results of high-throughput studies for spintronic materials, ferroelectric materials and photovoltaic materials in detail. To do so, we have developed Python tools for high-throughput bonding analysis with the programs VASP and Lobster (see www.cohp.de). They are based on the Python packages pymatgen, atomate, and custodian. This implementation will be discussed within the talk. We also expect that these tools offer possibilities to arrive at new descriptors based on chemical bonds for materials properties. T2 - High-throughput workflows for materials science with the Atomic Simulation Environment (ASE) and Fireworks CY - Lyngby, Denmark DA - 15. November 2021 KW - Automation KW - High-throughput computations KW - DFT PY - 2021 AN - OPUS4-53840 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Accelerated Materials Discovery Based on Data Analysis and Machine Learning N2 - The stability of materials is a very important property for materials discovery. In recent years, we have explored several approaches for advancing the prediction of the stability of crystals (e.g., classical chemical heuristics and machine-learned interatomic potentials). For example, we have assessed a famous chemical heuristic – the Pauling rules – regarding their usefulness for structure prediction. They are only of limited predictive power, i.e. the rules 2 to 5 only work for 13 % of all tested oxides. Furthermore, we have shown that machine-learned interatomic potentials can be used to predict phonon properties of a range of silicon allotropes accurately and, therefore, they can also be used to assess the dynamic stability of materials. To do so, we have developed new schemes to build reference databases for machine learned interatomic potentials. T2 - PACRIM & GOMD 2021 CY - Online meeting DA - 13.12.2021 KW - Data analysis KW - Machine learning KW - High-throughput computations PY - 2021 AN - OPUS4-54088 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Automation in Computational Materials Science N2 - This lecture illustrated the current state of automation in the field of computational materials science based on recent examples from my research. T2 - NOA Lecture Series FSU Jena CY - Jena, Germany DA - 02.11.2021 KW - Data analysis KW - Machine learning KW - High-throughput computations PY - 2021 AN - OPUS4-54089 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - George, Janine T1 - Automation in DFT-based computational materials science JF - Trends in chemisry N2 - Automation simplifies the use of computational materials science software and makes it accessible to a wide range of users. This enables high-throughput calcula-tionsand makesiteasier for non-specialists to enter computational materials science. However, in-creasing automation also poses threats that should be considered while interacting with automated procedures. KW - DFT KW - Automation KW - High-throughput computations PY - 2021 DO - https://doi.org/10.1016/j.trechm.2021.07.001 SN - 2589-5974 VL - 3 IS - 9 SP - 697 EP - 699 PB - Elsevier CY - Amsterdam AN - OPUS4-53127 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven materials discovery and understanding N2 - Developments in density functional theory (DFT) calculations, their automation and therefore easier access to materials data have enabled ab initio high-throughput searches for new materials for numerous applications. These studies open up exciting opportunities to find new materials in a much faster way than based on experimental work alone. However, performing density functional theory calculations for several thousand materials can still be very time consuming. The use of, for example, faster chemical heuristics and machine-learned interatomic potentials would allow to consider a much larger number of candidate materials. In addition to DFT based high-throughput searches, the seminar will discuss two possible ways to accelerate high-throughput searches. Using data analysis on the structures and coordination environments of 5000 oxides, we were able to investigate a chemical heuristic – the famous Pauling rules – regarding its usefulness for the fast prediction of stable materials. We have also investigated how machine-learned interatomic potentials can be used to accelerate the prediction of (dynamically) stable materials. The use of these potentials makes vibrational properties accessible in a much faster way than based on DFT. Our results based on a newly developed potential for silicon allotropes showed excellent agreement with DFT reference data (agreement of the frequencies within 0.1-0.2 THz). In addition, we have successfully used high-throughput calculations in the search for new candidate materials for spintronic applications and ferroelectrics T2 - ETSF Webinar CY - Online meeting DA - 25.06.2021 KW - High-throughput computations KW - Data analysis KW - Machine learning PY - 2021 AN - OPUS4-52980 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -