Scholarly work


Calculation of nanomaterials properties that is based on a description of their electronic structure can be mathematically very complicated, however within the framework of density functional theory it suffices to know the average number of electrons located at any one point in space, i.e., the electron density. The development and use of quantum mechanical methods based on density functional theory enable more efficient and accurate calculations of the ground and excited states of known and not-yet-synthesized materials. They are used for example to elucidate the movement and interactions of the charge carriers that govern photo-catalysis, and the dielectric response of electrochemical systems. Monte Carlo, molecular dynamics, and a range of other simulation techniques are used to sample the states of such physical systems.

Methoxy photo-oxidation
Sampling the photo-chemical oxidation of
methoxy on titania with femtosecond resolution (A-hole, B-electron).


The possibility to fabricate 2D device architectures with desired combination of graphene-like materials has posed fundamental questions about their physics and chemistry. As an extreme case in surface science, graphene and layered materials like transition metal chalcogenides (e.g. SnS, MoS2, WSe2) exploit physics that cannot be derived by scaling down the associated bulk structures and phenomena. Combining the properties of these 2D layers opens almost unlimited possibilities for novel devices with tailor-made electronic, optical, magnetic, thermal and mechanical properties with applications ranging from energy to quantum technologies. Materials modeling and simulation can be used for example to provide insights into the physics of Moiré assemblies and guide their design.

Twisted graphene
Probing unconventional electronic behavior in
twisted multi-layer graphene.


Significantly enhancing the performance of systems and devices for energy conversion and storage most often critically depends on the introduction of novel materials with tailored functionality. The use of theoretical concepts and materials models that describe important physical and chemical processes, from the atoms to device components, reduce uncertainty in the lab and expedite the development of various energy technologies such as photovoltaics, batteries and thermoelectrics. On the basis of quantum mechanical calculations for example it is possible to accurately predict the course of chemical transformations of important fuels and produce reliable descriptions of the electronic excitations induced by light pertinent to conversion of solar energy into secondary energy sources.

Si lithiation
Obtaining atomistic insights into diffusion-induced fracture in
silicon-based Li-ion battery electrodes.


Evaluation of the performance of a given material can be resource demanding but remains a tractable problem. The challenge lies in the inverse problem, i.e., the identification of a material with target properties since the materials space can be enormous. It is desirable to circumvent the trial and error nature of the lab and accelerate materials innovation by using supercomputers to conduct virtual high-throughput experiments and by using intelligent algorithms to automate the scientific process towards self-driving research labs. Development of workflows for integrating information streams from theory (e.g. multiscale modeling), experiment (e.g. characterization), and other sources (eg. literature) nevertheless pose challenges like devising effective systems and protocols for information exchange at the interface of distinct but complimentary knowledge domains.

Materials innovation platforms
Coupling high-throughput atomistic simulations with
machine learning for inverse materials design.

Complete list of publications available here.