Computational Materials Chemistry
Part II projects available: We are looking for enthusiastic MChem students to join the group for research projects in materials modelling and machine learning for chemistry. You do not need to have experience in computational techniques or coding, but you should share our excitement about structural chemistry! Current Oxford students are encouraged to get in touch directly via email.
Our research vision is to understand, and ultimately to control, materials structure on the atomic scale. We combine quantum mechanics with machine learning (ML) to study relationships of structure, bonding, and properties. Our work is theoretical and computational, but is done in close collaboration with experimental partners, and with practical applications in mind.
Machine-learning tools for inorganic materials chemistry
Computer simulations based on the laws of quantum mechanics are a cornerstone of materials research – but they are severely limited by their computational cost. We develop and apply interatomic potential models that "learn" from quantum-mechanical data, enabling accurate simulations that are many orders of magnitude faster. We are especially interested in building optimised and efficient databases for ML potential fitting, and in ML tools for chemical discovery.
- V. L. Deringer, M. A. Caro, G. Csányi: "Machine Learning Interatomic Potentials as Emerging Tools for Materials Science" (Progress Report). Adv. Mater. 2019, 31, 1902765 [link].
- N. Bernstein, G. Csányi, V. L. Deringer: "De novo exploration and self-guided learning of potential-energy surfaces". npj Comput. Mater. 2019, 5, 99 [link].
Structure and properties of amorphous solids
Amorphous (non-crystalline) materials are a frontier of current materials research: their disordered structures are difficult to determine experimentally, and they also pose large challenges for simulations. We use computer simulations to explore the structures of amorphous functional materials, and aim to link this structural information to physical and chemical properties.
- V. L. Deringer et al.: "Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics". J. Phys. Chem. Lett. 2018, 9, 2879–2885 [link].
- J.-X. Huang, G. Csányi, J.-B. Zhao, J. Chen, V. L. Deringer: "First-principles study of alkali-metal intercalation in disordered carbon anode materials". J. Mater. Chem. A 2019, 7, 19070–19080 [link].
Visit my Google Scholar profile for a full list (currently 76 journal publications, of which 39 as first and/or corresponding author).
Volker Deringer studied chemistry at RWTH Aachen University (Germany), where he obtained his diploma (2010) and doctorate (2014) under guidance of Richard Dronskowski. In 2015, he moved to the University of Cambridge as a fellow of the Alexander von Humboldt Foundation; in 2017, he was awarded a Leverhulme Early Career Fellowship at the same institution. He joined the Inorganic Chemistry Laboratory in September 2019. In addition to his Associate Professorship in the Department, he holds a Tutorial Fellowship at St Anne's College, Oxford.