Reaction Pathways Based on the Gradient of the Mean First-Passage Time

Finding representative reaction pathways is necessary for understanding mechanisms of molecular processes, but is considered to be extremely challenging. We propose a new method to construct reaction paths based on mean first-passage times. This approach incorporates information of all possible reaction events as well as the effect of temperature. The method is applied to exemplary reactions in a continuous and in a discrete setting. The suggested approach holds great promise for large reaction networks that are completely characterized by the method through a pathway graph.Comment: v2; 4 pages including 5 figure

Phonon modes in InAs quantum dots

Phonon modes in spherical InAs quantum dots (QDs) with up to 11 855 atoms (about 8.5 nm in diameter) are calculated by using a valence force field model, and all the vibration frequencies and vibration amplitudes of the QDs are calculated directly from the lattice-dynamic matrix. The projection operators of the irreducible representations of the group theory are employed to reduce the computational intensity, which further allows us to investigate the quantum confinement effect of phonon modes with different symmetries. It is found that the size effects of phonon modes depend on the symmetry of the modes. For zinc-blende structure, the modes with A(1) symmetry has the strongest quantum confinement effect and the T-1 mode the weakest. There could be a crossover of symmetries of the highest frequencies from A(1) to T-2 as the size of the QDs decreases. The behavior of vibration amplitudes and vibration energies of phonon modes in different symmetries are also investigated in detail. These results provide microscopic details of the phonon properties of QDs that are important to the fundamental understanding and potential applications of semiconductor QDs

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning

Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an $R^2$ score of 0.89 and a root mean square valence error of 0.21 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real time EELS/XAS analysis pipeline on mixtures of copper containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials