TY - CONF A1 - Ertural, Christina T1 - Phonon-accurate machine-learning potentials from automated workflows N2 - Data-driven materials design aims to predict and optimise material properties, such as stability and thermal conductivity, that are influenced by vibrational behaviour. Approaches such as DFT are computationally demanding and have limitations for phonon calculations. Machine learning based interatomic potentials (MLIP), such as the Gaussian Approximation Potential (GAP), offer a more efficient alternative. We have developed a Python workflow to automate MLIP generation using the Materials Project database. DFT calculations, MLIP fitting and benchmarking steps are automated. This approach speeds up phonon calculations and allows testing of different data generation strategies and hyperparameters. Our goal is to provide open source and share these capabilities to improve reproducibility and accessibility in computational chemistry. This talk will provide a hih-level overview of the automated software and recent preliminary results. T2 - Berlin PostDoc Day 2024 CY - Berlin, Germany DA - 07.11.2024 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Thermoelectrics KW - Automated workflows PY - 2024 AN - OPUS4-61576 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Materials design using chemical heuristics, workflows, and machine learning N2 - Software implementations such as ChemEnv and LobsterEnv identify local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis (here using Crystal Orbital Hamilton Populations as computed with LOBSTER). Fully automated workflows and analysis tools now enable large-scale quantum-chemical bonding analysis. The first part of the lecture will demonstrate how these tools help develop new machine-learning models and intuitive understandings of material properties. New universal machine-learned interatomic potentials, such as MACE-MP-0, have been developed. The second part of the lecture will showcase how these potentials, combined with DFT, can accelerate research. It will focus on the interplay between DFT and machine-learned interatomic potentials, presenting automated workflows for training, fine-tuning, and benchmarking these potentials, implemented in our software autoplex. Additionally, it will show how to train new interatomic potentials from scratch by exploring potential energy surfaces, with the potential to enhance current universal machine-learned potentials. The lecture will also discuss the trend toward automation in computational materials science and our recent contributions T2 - Group Seminar at the University of Toronto CY - Online Meeting DA - 18.07.2025 KW - Automation KW - Magnetism KW - High-throughput KW - Amorphous Materials KW - Phase Change Materials KW - Phonons PY - 2025 AN - OPUS4-63743 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding N2 - Chemical heuristics are essential to understanding molecules and materials in chemistry. The periodic table, atomic radii, and electronegativities are only a few examples. Initially, they have been developed by a combination of physical insight and a limited amount of data. It is now possible to test these heuristics and generate new ones using automation based on Materials Informatic tools like pymatgen and greater amounts of data from databases such as a Materials Project. In this session, I'll speak about heuristics and design rules based on coordination environments and the concept of chemical bonding. For example, we have tested the Pauling rules which describe the stability of materials based on coordination environments and their connections on 5000 oxides from the Materials Project. In addition, we have created automated processes for analyzing the chemical bonding situation in crystalline materials with Lobster (www.cohp.de) in order to discover new heuristics and design rules. T2 - Materials Project Seminar Series CY - Online meeting DA - 18.05.2022 KW - DFT KW - Chemical heuristics KW - Crystal Orbital Hamilton Populations KW - Machine learning KW - Phonons PY - 2022 UR - https://www.youtube.com/watch?v=e7zYrz6fgog UR - https://next-gen.materialsproject.org/community/seminar AN - OPUS4-55008 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ertural, Christina T1 - Automation of machine learning driven interatomic potential generation for predicting vibrational properties N2 - Knowing phonon properties is beneficial for predicting low thermal conductivity thermoelectric materials. Employing DFT consumes lots of computational resources. Using ML-driven interatomic potentials (MLIP, e.g., GAP) opens up a faster route, but most potentials are specifically tailored to a certain compound. We aim to generalize the MLIP generation in a Python code-based workflow, combining automatic DFT runs with automated GAP fits. Automation enables easier tests, benchmarks, and validation. T2 - SALSA Make and Measure Conference: Interfaces CY - Berlin, Germany DA - 13.09.2023 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Thermoelectrics KW - Automated workflows PY - 2023 AN - OPUS4-58374 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding with bonding analysis N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials. T2 - 2023 MRS Fall Meeting & Exhibit CY - Boston, Massachusetts, USA DA - 26.11.2023 KW - Automation KW - Machine learning KW - Materials Understanding KW - Magnetism KW - Phonons PY - 2023 AN - OPUS4-59002 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-throughput Approaches for Materials Understanding and Design N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties.[1] Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available.[2,3] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[3,4] The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.[7–10] T2 - International Materials Science and Engineering Congress - MSE 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Automation KW - High-throughput KW - Chemically Complex Materials KW - Thermal Properties KW - Phonons KW - Bonding Analysis PY - 2024 AN - OPUS4-61118 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-Throughput Approaches for Materials Understanding and Design N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed. T2 - International Symposium on Computational Structure Prediction and Advanced Materials CY - Louvain-la-Neuve, Belgium DA - 22.08.2024 KW - Automation KW - High-throughput KW - Magnetism KW - Phonons KW - Machine learning KW - Chemically complex materials PY - 2024 AN - OPUS4-60870 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-Driven Approaches for Materials Understanding and Design N2 - Data-Driven Approaches for Materials Understanding and Design Analyzing the crystal structure (including coordination environments, bond distances, and angles) and chemical bonding are standardly used in chemistry to understand material properties. In this talk, I will show how we can use this information in machine learning of material properties. For this purpose, I will present our software tools ChemEnv[1] for analyzing coordination environments and for automation of orbital-based bonding analysis with LOBSTER[2] (LobsterPy[3] and density-functional theory workflows in atomate2[4]). Enabled by these software tools, we have built interpretable machine-learned models for magnetic and vibrational properties that allow us to test and further develop intuitive rules.[5,6] T2 - Institutskolloqium Institut für Anorganische Chemie und Kristallographie CY - Leipzig, Germany DA - 23.10.2024 KW - Automation KW - Bonding Analysis KW - Materials Design KW - Chemically Complex Materials KW - Phonons KW - Thermal Properties KW - Synthesizability KW - Machine Learning PY - 2024 AN - OPUS4-61445 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Neue Wege in der Materialforschung: Zusammenspiel von Hochdurchsatz-Simulationen und maschinellem Lernen N2 - Dies ist meine Antrittsvorlesung an der Friedrich-Schiller-Universität Jena, die im Rahmen der BAM-Universität-Jena-Kooperation entstanden ist. Hier stelle ich die Materialinformatik und unsere Forschung im speziellen vor. T2 - Antrittsvorlesung an der Friedrich-Schiller-Universität Jena CY - Jena, Germany DA - 20.01.2025 KW - Automation KW - Ab initio KW - Machine learning KW - Phonons KW - Materialdesign KW - Materials Acceleration Platforms PY - 2025 AN - OPUS4-62437 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Phonon workflow and applications N2 - This talk introduced the participants to phonons and how they are typically computed. Then, I introduced the participants to the harmonic phonon, the Grüneisen, and the quasi-harmonic workflow that allows the computation of properties related to phonons. I also had detailed examples of how these workflows can be used in practice. Beyond this, I showed how these workflows can be used to automatically benchmark interatomic potentials and use them for the development of such potentials. T2 - "Automated ab initio workflows with Jobflow and Atomate2" CECAM Flagship school CY - Lausanne, Switzerland DA - 17.03.2025 KW - Automation KW - Phonons KW - Thermal Conductivity KW - Machine Learning KW - Software Development KW - Machine Learned interatomic Potentials PY - 2025 AN - OPUS4-62949 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -