Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics

Amorphous silicon (a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 1011 K/s (that is, on the 10 ns time scale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4096-atom system that correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials

Direct imaging of correlated defect nanodomains in a metal-organic framework

Defect engineering can enhance key properties of metal-organic frameworks (MOFs). Tailoring the distribution of defects, for example in correlated nanodomains, requires characterization across length scales. However, a critical nanoscale characterization gap has emerged between the bulk diffraction techniques used to detect defect nanodomains and the sub-nanometer imaging used to observe individual defects. Here, we demonstrate that the emerging technique of scanning electron diffraction (SED) can bridge this gap uniquely enabling both nanoscale crystallographic analysis and the lowdose formation of multiple diffraction contrast images for defect analysis in MOFs. We directly image defect nanodomains in the MOF UiO-66(Hf) over an area of ca. 1 000 nm and with a spatial resolution ca. 5 nm to reveal domain morphology and distribution. Based on these observations, we suggest possible crystal growth processes underpinning synthetic control of defect nanodomains. We also identify likely dislocations and small angle grain boundaries, illustrating that SED could be a key technique in developing the potential for engineering the distribution of defects, or “microstructure”, in functional MOF design

Virtual discussions to support climate risk decision making on farms

Climate variability represents a significant risk to farming enterprises. Effective extension of climate information may improve climate risk decision making and adaptive management responses to climate variability on farms. This paper briefly reviews current agricultural extension approaches and reports stakeholder responses to new web-based virtual world ‘discussion-support’ tools developed for the Australian sugar cane farming industry. These tools incorporate current climate science and sugar industry better management practices, while leveraging the social-learning aspects of farming, to provide a stimulus for discussion and climate risk decision making. Responses suggest that such virtual world tools may provide effective support for climate risk decision making on Australian sugar cane farms. Increasing capacity to deliver such tools online also suggests potential to engage large numbers of farmers globally

A Dimension-Adaptive Multi-Index Monte Carlo Method Applied to a Model of a Heat Exchanger

We present an adaptive version of the Multi-Index Monte Carlo method, introduced by Haji-Ali, Nobile and Tempone (2016), for simulating PDEs with coefficients that are random fields. A classical technique for sampling from these random fields is the Karhunen-Lo\`eve expansion. Our adaptive algorithm is based on the adaptive algorithm used in sparse grid cubature as introduced by Gerstner and Griebel (2003), and automatically chooses the number of terms needed in this expansion, as well as the required spatial discretizations of the PDE model. We apply the method to a simplified model of a heat exchanger with random insulator material, where the stochastic characteristics are modeled as a lognormal random field, and we show consistent computational savings

Strengthening the Magnetic Interactions in Pseudobinary First-Row Transition Metal Thiocyanates, M(NCS)2.

Understanding the effect of chemical composition on the strength of magnetic interactions is key to the design of magnets with high operating temperatures. The magnetic divalent first-row transition metal (TM) thiocyanates are a class of chemically simple layered molecular frameworks. Here, we report two new members of the family, manganese(II) thiocyanate, Mn(NCS)2, and iron(II) thiocyanate, Fe(NCS)2. Using magnetic susceptibility measurements on these materials and on cobalt(II) thiocyanate and nickel(II) thiocyanate, Co(NCS)2 and Ni(NCS)2, respectively, we identify significantly stronger net antiferromagnetic interactions between the earlier TM ions-a decrease in the Weiss constant, θ, from 29 K for Ni(NCS)2 to -115 K for Mn(NCS)2-a consequence of more diffuse 3d orbitals, increased orbital overlap, and increasing numbers of unpaired t2g electrons. We elucidate the magnetic structures of these materials: Mn(NCS)2, Fe(NCS)2, and Co(NCS)2 order into the same antiferromagnetic commensurate ground state, while Ni(NCS)2 adopts a ground state structure consisting of ferromagnetically ordered layers stacked antiferromagnetically. We show that significantly stronger exchange interactions can be realized in these thiocyanate frameworks by using earlier TMs.EPSRC NPIF 2018 fund Laboratory Directed Research and Development Program of Oak Ridge National Laboratory NSERC of Canada PGSD fund Trinity College, Cambridge School of Chemistry, University of Nottingham Hobday Fellowship EPSRC Strategic Equipment Grant EP/M000524/

Can digital discussion support tools provide cost-effective options for agricultural extension services?

Agricultural extension that delivers timely, targeted, and cost-effective support to farmers will help ensure the sustainability and adaptive capacity of agriculture, enhancing both food security and environmental security. Leveraging advances in agriclimate science and adult education, innovative digital technologies offer significant new opportunities to engage with farmers and to support decision making. In this study, animated video clips (machinimas), developed using the Second LifeTM virtual world gaming platform, model conversations around climate risk and critical on-farm decisions in the Australian sugarcane farming industry. Early evaluation indicates that this is an engaging format that promotes discussion by leveraging farmers’ natural modes of information gathering and social learning. Comparison with conventional extension practices indicates that these discussion support tools may be a cost effective addition to existing approaches. The format’s flexibility means machinimas are readily updated with new information and customized to meet the needs of different farmer groups. Rapid growth in digital access globally and the scalability of such approaches promise greater equity of access to high-value information, critical to better risk management decision making, at minimal cost, for millions of farmers

Risk and protective factors for self-harm and suicide in children and adolescents: a systematic review and meta-analysis protocol.

Introduction Self-harm and suicide are major public health concerns among children and adolescents. Many risk and protective factors for suicide and self-harm have been identified and reported in the literature. However, the capacity of these identified risk and protective factors to guide assessment and management is limited due to their great number. This protocol describes an ongoing systematic review and meta-analysis which aims to examine longitudinal studies of risk factors for self-harm and suicide in children and adolescents, to provide a comparison of the strengths of association of the various risk factors for self-harm and suicide and to shed light on those that require further investigation. Methods and analysis We perform a systematic search of the literature using the databases EMBASE, PsycINFO, Medline, CINAHL and HMIC from inception up to 28 October 2020, and the search will be updated before the systematic review publication. Additionally, we will contact experts in the field, including principal investigators whose peer-reviewed publications are included in our systematic review as well as investigators from our extensive research network, and we will search the reference lists of relevant reviews to retrieve any articles that were not identified in our search. We will extract relevant data and present a narrative synthesis and combine the results in meta-analyses where there are sufficient data. We will assess the risk of bias for each study using the Newcastle–Ottawa Scale and present a summary of the quantity and the quality of the evidence for each risk or protective factor. Ethics and dissemination Ethical approval will not be sought as this is a systematic review of the literature. Results will be published in mental health journals and presented at conferences focused on suicide prevention

Muc5ac: a critical component mediating the rejection of enteric nematodes

The mucin Muc5ac is essential for the expulsion of Trichuris muris and other gut-dwelling nematodes