(3cl) First-Principles Approaches for Accurate Predictions of Nanostructured Materials

Nanostructured materials have attracted great interest in recent years due to their unusual mechanical, electronic and optical properties, with wide-ranging applications like sustainable energy. Rational design of nanostructured materials requires electronic, atomic and mechanistic level understanding of them particularly being in the process of synthesis, operations and reactions. First-principles quantum mechanical calculations can provide unique insights into such microscopic aspects. Here, we start by studying a representative class of nanoscale materials, semiconducting quantum dots (QDs), to demonstrate the critical role of density functional theory (DFT) in discovering growth pathway. We proceed by investigating the delocalization error in DFT to improve its predictive accuracy for complex materials, such as correlated transitional metal oxides. To complement DFT limitations, we also visit higher level embedded correlated wavefunction theory (ECW) and apply it to understand more challenging electrochemical reactions.

Our research includes that: 1) We introduce a rigorous approach to enable predictions on the growth of QDs from the earliest stage intermediates to nm-scale nanostructures in the paramount case of InP QDs, where the controlled growth represents a pressing challenge. By high-throughput screening of the surface site kinetic properties via our approach, we observe the kinetics depend strongly on surface morphology and the less-In rich surfaces are less reactive due to strengthened interactions with passivating precursor ligands at odds with established surface science paradigms. 2) We carry out a systematic study to identify how diverse approaches for approximate delocalization error correction (i.e., DFT+U and hybrid functionals) perform on density localization, surface and adsorbate energies across rutile transition metal oxides, with promise as water splitting catalysts. We observe divergent behavior between DFT+U and hybrid functionals, where only hybrid functionals are able to correct the paradoxical overbinding of surface adsorbates and underestimation of surface energies. 3) Motivated by DFT’s CO adsorption puzzle, we apply higher level ECW theory to revisit the reaction mechanisms of electrochemical CO₂ reduction on copper. ECW provides a local correction to the exchange-correlation error inherent in DFT, and thus represents a more robust method to tackle complex charge transfer processes. We observe quite different structural and mechanistic behaviors between ECW and DFT in the mechanisms of electrochemical CO₂ reduction.

Altogether, my past work of first-principles modeling demonstrates the unique microscopic insights into optimization and design of nanostructured materials. This not only provides new physical and chemical perspectives of materials in dynamic process, but also brings up huge interdisciplinary opportunities of combining quantum mechanical theory with continuum model and even data-driven machine learning techniques, so as to develop multi-scale understanding of challenging materials and accelerate full cycle of new materials design. This provides potentials towards the dream of designing materials from ab inito approaches and shows impact in revolutionizing fields from materials science, chemistry to engineering.