Improving DFT with deep learning In the past 30 years, density functional theory (DFT) has emerged as the most widely used electronic structure method to predict the properties of various systems in chemistry, biology, and materials science. Despite a long history of successes, state-of-the-art DFT functionals have crucial limitations. In particular, significant systematic errors are observed for charge densities involving mobile charges and spins. Kirkpatrick et al . developed a framework to train a deep neural network on accurate chemical data and fractional electron constraints (see the Perspective by Perdew). The resulting functional outperforms traditional functionals on thorough benchmarks for main-group atoms and molecules. The present work offers a solution to a long-standing critical problem in DFT and demonstrates the success of combining DFT with the modern machine-learning methodology. —YS
Hot carriers produced from the decay of localized surface plasmons in metallic nanoparticles are intensely studied because of their optoelectronic, photovoltaic and photocatalytic applications. From a classical perspective, plasmons are coherent oscillations of the electrons in the nanoparticle, but their quantized nature comes to the fore in the novel field of quantum plasmonics. In this work, we introduce a quantum-mechanical material-specific approach for describing the decay of single quantized plasmons into hot electrons and holes.We find that hot carrier generation rates differ significantly from semiclassical predictions.We also investigate the decay of excitations without plasmonic character and show that their hot carrier rates are comparable to those from the decay of plasmonic excitations for small nanoparticles. Our study provides a rigorous and general foundation for further development of plasmonic hot carrier studies in the plasmonic regime required for the design of ultrasmall devices. arXiv:1904.03697v1 [cond-mat.mes-hall]
We present an approach to master the well-known challenge of calculating the contribution of dbands to plasmon-induced hot carrier rates in metallic nanoparticles. We generalise the widely used spherical well model for the nanoparticle wavefunctions to flat d-bands using the envelope function technique. Using Fermi's golden rule, we calculate the generation rates of hot carriers after the decay of the plasmon due to transitions from either a d-band state to an sp-band state or from an sp-band state to another sp-band state. We apply this formalism to spherical silver nanoparticles with radii up to 20 nm and also study the dependence of hot carrier rates on the energy of the d-bands. We find that for nanoparticles with a radius less than 2.5 nm sp-band state to sp-band state transitions dominate hot carrier production while d-band state to sp-band state transitions give the largest contribution for larger nanoparticles.
Understanding and controlling properties of plasmon-induced hot carriers is a key step toward next-generation photovoltaic and photocatalytic devices. Here, we uncover a route to engineering hot-carrier generation rates of silver nanoparticles by designed embedding in dielectric host materials. Extending our recently established quantum-mechanical approach to describe the decay of quantized plasmons into hot carriers we capture both external screening by the nanoparticle environment and internal screening by silver d-electrons through an effective electron–electron interaction. We find that hot-carrier generation can be maximized by engineering the dielectric host material such that the energy of the localized surface plasmon coincides with the highest value of the nanoparticle joint density of states. This allows us to uncover a path to control the energy of the carriers and the amount produced, for example, a large number of relatively low-energy carriers are obtained by embedding in strongly screening environments.
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