Digital Materials Chemistry
Division 6.6
Digital materials chemistry is a multidisciplinary field that combines traditional materials science methods with modern digital tools to enable more efficient, sustainable, and targeted material development. While traditional techniques often rely on resource-intensive cycles of synthesis and testing, digital approaches allow for less resource-intensive prediction and optimization of material properties. Central to this approach are quantum chemical calculations, which analyze the electronic structures and properties of materials at the atomic level, and molecular dynamics simulations, which model how atoms and molecules behave over time. Combined with experimental materials data, the data from these simulations feed into machine learning algorithms that identify patterns and enable faster predictions of material behavior and synthesis pathways. High-throughput screening speeds up the materials design process by simultaneously testing thousands of material combinations, quickly identifying promising candidates. An important advantage of digital methods is their ability to incorporate sustainability and safety considerations from the start—such as emphasizing environmentally friendly materials or including life cycle assessments in the design process. These methods are increasingly supported by open, reproducible software tools, which help accelerate innovation across the materials science community.
Projects:
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Main activities
- Designing functional materials for energy applications using computational methods
- Formulating chemical heuristics to guide materials discovery
- Creating automated workflows for high-throughput quantum chemical calculations, accelerated by machine learning (e.g., machine learning potentials)
- Investigating vibrational properties to understand material stability and heat transport
- Developing machine learning models to predict and optimize material properties
- Collaborating with experimental groups to validate simulations and address real-world challenges
- Building open-source tools for bonding analysis, property prediction, and simulation automation
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Competences
- Digital materials design: Computer-aided discovery and optimization of materials for energy, safety, and sustainability
- Solid-state chemistry and physics: Exploring structure-property relationships and developing heuristic rules for crystalline materials
- Quantum chemistry and electronic structure: Investigating atomic-scale interactions to understand and predict material behavior
- High-throughput simulations: Automated screening of thousands of materials using scalable workflows based on quantum-chemical simulations (typically density-functional theory)
- Vibrational and thermal transport modeling: Studying heat conduction and material stability through phonon calculations and lattice dynamics
- Machine learning in materials science: Predicting material properties, guiding synthesis, and identifying patterns in large datasets
- Chemical bonding analysis: Creating intuitive and automated tools to explore bonding environments in solids and applying them in machine learning contexts
- Automated training and use of machine learning potentials: Developing workflows for efficient training and deployment of interatomic potentials (e.g., as part of autoplex)
- Open-source software development: Contributing to widely used tools in computational materials science, such as Atomate2 and pymatgen, and serving as core developers of bonding analysis tools such as LobsterPy
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Prof. Dr. rer. nat. Janine George
