Fission of a spin-singlet exciton into two triplet excitons, if realized in disordered organic solid, could revolutionize low-cost fabrication of efficient solar cells. Here, a divide-conquer-recombine approach involving nonadiabatic quantum molecular dynamics and kinetic Monte Carlo simulations identifies the key molecular geometry and exciton-flow-network topology for singlet-fission “hot spots” in amorphous diphenyl tetracene, where fission occurs preferentially. The simulation reveals the molecular origin of experimentally observed two time scales in exciton population dynamics and may pave a way to nanostructural design of efficient solar cells from first principles.

We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 10(6)-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of techniques are employed for efficiently calculating the long-range exact exchange correction and excited-state forces. The NAQMD trajectories are analyzed to extract the rates of various excitonic processes, which are then used in KMC simulation to study the dynamics of the global exciton flow network. This has allowed the study of large-scale photoexcitation dynamics in 6400-atom amorphous molecular solid, reaching the experimental time scales.

Nonadiabatic quantum molecular dynamics simulations are performed to study photoexcited charge transfer (CT) and charge recombination (CR) at an interface between a conjugated oligomer donor, quaterthiophene (QT), and an inorganic acceptor (ZnO). Simulations reveal a detrimental effect of static disorder in QT conformation on the efficiency of hybrid QT/ZnO solar cells due to increased CR. On the contrary, dynamic disorder (i.e., fluctuation of carbon-hydrogen bonds in QT) is essential for high efficiency by assisting CT. The separate controllability of CT and CR at the molecular level has impacts on molecular design for efficient solar cells and explains recent experimental observations. V C 2012 American Institute of Physics. [http://dx.

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Reaction of aluminum clusters, Aln (n = 16, 17 and 18), with liquid water is investigated using quantum molecular dynamics simulations, which show rapid production of hydrogen molecules assisted by proton transfer along a chain of hydrogen bonds (H-bonds) between water molecules, i.e. Grotthuss mechanism. The simulation results provide answers to two unsolved questions: (1) What is the role of a solvation shell formed by non-reacting H-bonds surrounding the H-bond chain; and (2) whether the high size-selectivity observed in gas-phase Aln-water reaction persists in liquid phase? First, the solvation shell is found to play a crucial role in facilitating proton transfer and hence H2 production. Namely, it greatly modifies the energy barrier, generally to much lower values (< 0.1 eV). Second, we find that H2 production by Aln in liquid water does not depend strongly on the cluster size, in contrast to the existence of magic numbers in gas-phase reaction. This paper elucidates atomistic mechanisms underlying these observations

A crossover in anisotropic nanomechanochemistry of van der Waals crystals Applied Physics Letters 107, 231903 (2015) Exciton dynamics at an interface between an electron donor, rubrene, and a C 60 acceptor is studied by nonadiabatic quantum molecular dynamics simulation. Simulation results reveal an essential role of the phenyl groups in rubrene in increasing the charge-transfer rate by an order-of-magnitude. The atomistic mechanism of the enhanced charge transfer is found to be the amplification of aromatic breathing modes by the phenyl groups, which causes large fluctuations of electronic excitation energies. These findings provide insight into molecular structure design for efficient solar cells, while explaining recent experimental observations.

Purpose and BackgroundDistinguishing primary central nervous system lymphoma (PCNSL) and glioma on computed tomography (CT) is an important task since treatment options differ vastly from the two diseases. This study aims to explore various machine learning and deep learning methods based on radiomic features extracted from CT scans and end-to-end convolutional neural network (CNN) model to predict PCNSL and glioma types and compare the performance of different models.MethodsA total of 101 patients from five Chinese medical centers with pathologically confirmed PCNSL and glioma were analyzed retrospectively, including 50 PCNSL and 51 glioma. After manual segmentation of the region of interest (ROI) on CT scans, 293 radiomic features of each patient were extracted. The radiomic features were used as input, and then, we established six machine learning models and one deep learning model and three readers to identify the two types of tumors. We also established a 2D CNN model using raw CT scans as input. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.ResultsThe cohort was split into a training (70, 70% patients) and validation cohort (31,30% patients) according to the stratified sampling strategy. Among all models, the MLP performed best, with an accuracy of 0.886 and 0.903, sensitivity of 0.914 and 0.867, specificity of 0.857 and 0.937, and AUC of 0.957 and 0.908 in the training and validation cohorts, respectively, which was significantly higher than the three primary physician's diagnoses (ACCs ranged from 0.710 to 0.742, p &lt; 0.001 for all) and comparable with the senior radiologist (ACC 0.839, p = 0.988). Among all the machine learning models, the AUC ranged from 0.605 to 0.821 in the validation cohort. The end-to-end CNN model achieved an AUC of 0.839 and an ACC of 0.840 in the validation cohort, which had no significant difference in accuracy compared to the MLP model (p = 0.472) and the senior radiologist (p = 0.470).ConclusionThe established PCNSL and glioma prediction model based on deep neural network methods from CT scans or radiomic features are feasible and provided high performance, which shows the potential to assist clinical decision-making.

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