Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi

Nickel titanium (NiTi) is a protypical shape-memory alloy used in a range of biomedical and engineering devices, but direct molecular dynamics simulations of the martensitic B19' -> B2 phase transition driving its shape-memory behavior are rare and have relied on classical force fields with limited accuracy. Here, we train four machine-learned force fields for equiatomic NiTi based on the LDA, PBE, PBEsol, and SCAN DFT functionals. The models are trained on the fly during NPT molecular dynamics, with DFT calculations and model updates performed automatically whenever the uncertainty of a local energy prediction exceeds a chosen threshold. The models achieve accuracies of 1-2 meV/atom during training and are shown to closely track DFT predictions of B2 and B19' elastic constants and phonon frequencies. Surprisingly, in large-scale molecular dynamics simulations, only the SCAN model predicts a reversible B19' -> B2 phase transition, with the LDA, PBE, and PBEsol models predicting a reversible transition to a previously uncharacterized low-volume phase, which we hypothesize to be a new stable high-pressure phase. We examine the structure of the new phase and estimate its stability on the temperature-pressure phase diagram. This work establishes an automated active learning protocol for studying displacive transformations, reveals important differences between DFT functionals that can only be detected in large-scale simulations, provides an accurate force field for NiTi, and identifies a new phase

Mii and MiiBump: Supporting pregnant women to continue or commence an active lifestyle

The purpose of this study was to investigate how women perceive exercising during pregnancy and whether existing technologies could be used to support active behaviours. Information for pregnant women who wish to continue or start exercising is limited and not readily offered. With the ever growing capability and accessibility to technologies research is warranted to investigate whether it could be used to help support exercising during pregnancy. An interpretative phenomenological approach was used to conduct in-depth interviews with five women, via purposive sampling. The data revealed that the participants perceived a number of barriers to active behaviours, such as the lack of a pregnant buddy with which to exercise. A lack of provided information was evident with the women claiming to have sought appropriate information themselves. Findings highlighted the potential for existing technologies to be utilised in order to support and enhance exercise behaviours during pregnancy

Adamantane-Resistant Influenza Infection During the 2004–05 Season

Adamantane-resistant influenza A is an emerging problem, but infections caused by resistant and susceptible viruses have not been compared. We identified adamantane resistance in 47% of 152 influenza A virus (H3N2) isolates collected during 2005. Resistant and susceptible viruses caused similar symptoms and illness duration. The prevalence of resistance was highest in children

Identifying the need for good practices in Health Technology Assessment : summary of the ISPOR HTA Council Working Group Report on Good Practices in HTA

The systematic use of evidence to inform healthcare decisions, particularly health technology assessment (HTA), has gained increased recognition. HTA has become a standard policy tool for informing decision makers who must manage the entry and use of pharmaceuticals, medical devices, and other technologies (including complex interventions) within health systems, for example, through reimbursement and pricing. Despite increasing attention to HTA activities, there has been no attempt to comprehensively synthesize good practices or emerging good practices to support populationbased decision-making in recent years. After the identification of some good practices through the release of the ISPOR Guidelines Index in 2013, the ISPOR HTA Council identified a need to more thoroughly review existing guidance. The purpose of this effort was to create a basis for capacity building, education, and improved consistency in approaches to HTA-informed decision-making. Our findings suggest that although many good practices have been developed in areas of assessment and some other key aspects of defining HTA processes, there are also many areas where good practices are lacking. This includes good practices in defining the organizational aspects of HTA, the use of deliberative processes, and measuring the impact of HTA. The extent to which these good practices are used and applied by HTA bodies is beyond the scope of this report, but may be of interest to future researchers

Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics

Accurate modeling of chemically reactive systems has traditionally relied on either expensive ab initio approaches or flexible bond-order force fields such as ReaxFF that require considerable time, effort, and expertise to parameterize. Here, we introduce FLARE++, a Bayesian active learning method for training reactive many-body force fields on the fly during molecular dynamics (MD) simulations. During the automated training loop, the predictive uncertainties of a sparse Gaussian process (SGP) force field are evaluated at each timestep of an MD simulation to determine whether additional ab initio data are needed. Once trained, the SGP is mapped onto an equivalent and much faster model that is polynomial in the local environment descriptors and whose prediction cost is independent of the training set size. We apply our method to a canonical reactive system in the field of heterogeneous catalysis, hydrogen splitting and recombination on a platinum (111) surface, obtaining a trained model within three days of wall time that is twice as fast as a recent Pt/H ReaxFF force field and considerably more accurate. Our method is fully open source and is expected to reduce the time and effort required to train fast and accurate reactive force fields for complex systems

Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning

Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale of billions of atoms has been achieved for the heterogeneous catalytic system of H$_2$/Pt(111) using the FLARE Bayesian force field. This achievement provides accelerated time-to-solution from first principles, with Bayesian active learning enabling efficient and autonomous training of the machine learning model. The resulting model is then deployed in LAMMPS on GPUs using the Kokkos performance portability library. The Bayesian force field provides quantitative uncertainty of predictions on every atomic environment, critical for detecting configurations in large reactive simulations that are outside of the training set. Scaling benchmarks were performed using real-application MD of the H$_2$/Pt(111) heterogeneous catalysis on the Summit supercomputer, with simulations reaching 0.5 trillion atoms on 4556 GPU nodes.Comment: 10 pages, 9 figure

Clinical and virologic outcomes in patients with oseltamivir-resistant seasonal influenza A (H1N1) infections: Results from a clinical trial

Nineteen patients with oseltamivir-resistant seasonal influenza A (H1N1) infections were randomized to receive oseltamivir or placebo. Nasopharyngeal swabs were obtained, and clinical and virologic outcomes were compared, stratified by early or late treatment. Neuraminidase inhibition assay and pyrosequencing for H275Y confirmed resistance. Twelve (63%) patients received oseltamivir; 8 (67%) received late treatment. Seven (37%) patients received placebo; 6 (86%) presented >48hours after onset. Time to 50% decrease in symptom severity, complete symptom resolution, and first negative culture were shortest among the early treatment group. While sample size prohibits a strong conclusion, future studies should evaluate for similar trends. © 2011 Blackwell Publishing Ltd