LCMD’s expertise in quantum chemistry includes tools, methodological developments and machine learning techniques, along with conceptual work relevant to the field of catalysis and organic electronic materials.
The laboratory has tackled some of the main shortcomings affecting standard density functional approximations and developed efficient and accurate methods to achieve quantitative results for energies, geometries, and molecular dynamic trajectories of non-covalently bound systems subject to these shortcomings. LCMD has also made seminal contributions in introducing several classes of approaches enhancing the fundamental comprehension of phenomena governed by non-covalent interactions. These tools and methods were exploited to identify promising organic semiconductors. Design principles were introduced to “boost” non-covalent interactions and enhance tight packing in order to promote field-effect mobility in both amorphous hole transport materials and molecular crystals. Most recent work within the NCCR MARVEL involves the development machine learning models to access some of the key electronic structure properties at a negligible computational cost.
In the area of design, the group has also established a considerable expertise in computational catalysis. LCMD recently expanded the concept of volcano plots to homogeneous catalysis and developed machine learning models to enable a rapid screening of thousands prospective homogeneous catalytic species for several classes of chemical reactions. This broad expertise in the field of computational organic electronics and catalysis has led to productive collaborations with experimental colleagues.