Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next-generation functional materials but has been severely hampered due to its extremely large configurational spa...
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Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next-generation functional materials but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires extensive size of the training dataset, making it a less feasible approach. Here, we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. We employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green's function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. In this paper, we show that a machine learning technique combined with conventional wisdom (e.g., structural unit model) can extend the recent success of ab initio thermal transport simulation, which has been mostly limited to single crystals, to complex yet practically important polycry
Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-l...
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Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objective: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM;and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). Results: WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation
Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next generation functional materials, but has been severely hampered due to its extremely large configurational sp...
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Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next generation functional materials, but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires an extensive size of the training dataset, making it a less feasible approach. Here we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. In particular, we employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green's function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. Our work shows that a machine learning technique combined with conventional wisdom (e.g., structural unit model) can extend the recent success of ab initio thermal transport simulation, which has been mostly limited to single crystals, to complex yet practically importa
Lanarkite is a mineral formed by a combination of lead, sulphur, and oxygen atoms arranged in the general chemical formula Pb2SO5 (PSO) that crystallises with monoclinic symmetry (belonging to the C2/m space group, No...
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Lanarkite is a mineral formed by a combination of lead, sulphur, and oxygen atoms arranged in the general chemical formula Pb2SO5 (PSO) that crystallises with monoclinic symmetry (belonging to the C2/m space group, No. 12). This mineral was first discovered in Lanarkshire, Scotland and was named after its location. PSO has a unique structure comprising alternating penta-coordinated lead [PbO5] and tetra-coordinated sulphur [SO4] clusters. This lanarkite-type structure has recently attracted significant scientific interest and has been the focus of the superconducting material research community. However, its chemistry needs to be explored further. This article presents a comprehensive investigation on the chemical bonding, electronic structure, and spectroscopic properties of the lanarkite-type PSO structure from a computational perspective. Thus, different functionals in the DFT (e.g., PBE, PBE0, PBESOL, PBESOL0, BLYP, WC1LYP37, and B3LYP) were assessed to accurately predict their fundamental properties. All the DFT calculations were performed using a triple-zeta valence plus polarisation basis set. Among all the DFT functionals tested in this study, PBE showed the best agreement with the experimental data available in the literature. Our results also reveal that the [PbO5] clusters are formed with three Pb–O bond lengths, with values of about 2.32, 2.59, and 2.84 Å, respectively, while the [SO4] clusters have the same S–O bond length of 1.57 Å. We performed a complete topological analysis of this system to comprehend these structural differences. Additionally, the PSO structure has an indirect band gap energy of 2.9 eV and an effective mass ratio (mℎ∗ /me∗) of about 0.415 (using PBE calculations) which may, in principle, indicate a low recombination of electron-hole pairs in the lanarkite structure. Therefore, we believe that a detailed understanding of their electronic structures, spectroscopic properties as well as their chemical bonding is critically important
We describe the synthetically useful enantioselective addition of Br−CX 3 (X=Cl or Br) to terminal olefins to introduce a trihalomethyl group and generate optically active secondary bromides. computational and experim...
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We describe the synthetically useful enantioselective addition of Br−CX 3 (X=Cl or Br) to terminal olefins to introduce a trihalomethyl group and generate optically active secondary bromides. computational and experimental evidence supports an asymmetric atom‐transfer radical addition (ATRA) mechanism in which the stereodetermining step involves outer‐sphere bromine abstraction from a [(bisphosphine)Rh II BrCl] complex by a benzylic radical intermediate. This mechanism appears unprecedented in asymmetric catalysis.
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 20...
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