The ability to automatically extend and improve molecular force fields to accurately describe an ever wider range of compounds is a key enabler for the application of molecular modelling of chemicals and materials in industry. In this work we have developed a set of tools to process structural data available in well-known databases, to find deficiencies in the force field, and where necessary, automatically fit force- field parameters to on-the-fly calculated quantum data based on Density Functional Theory, supplemented by experimental data. The protocols have been applied to structures from the Maybridge, PoLyInfo and ILThermo databases, covering drug-like molecules, polymers and ionic liquids respectively. We demonstrate that the new version of the force field can type all structures in the extended data set without missing parameters, with a similar high-quality prediction of geometry (bonds, angles, torsions), energy and forces of earlier versions.

Background: Many patients with chronic obstructive pulmonary disease (COPD) suffer from exacerbations, a worsening of their respiratory symptoms that warrants medical treatment. Exacerbations are often poorly recognized or managed by patients, leading to increased disease burden and health care costs. Objective: This study aimed to examine the effects of a smart mobile health (mHealth) tool that supports COPD patients in the self-management of exacerbations by providing predictions of early exacerbation onset and timely treatment advice without the interference of health care professionals. Methods: In a multicenter, 2-arm randomized controlled trial with 12-months follow-up, patients with COPD used the smart mHealth tool (intervention group) or a paper action plan (control group) when they experienced worsening of respiratory symptoms. For our primary outcome exacerbation-free time, expressed as weeks without exacerbation, we used an automated telephone questionnaire system to measure weekly respiratory symptoms and treatment actions. Secondary outcomes were health status, self-efficacy, self-management behavior, health care utilization, and usability. For our analyses, we used negative binomial regression, multilevel logistic regression, and generalized estimating equation regression models. Results: Of the 87 patients with COPD recruited from primary and secondary care centers, 43 were randomized to the intervention group. We found no statistically significant differences between the intervention group and the control group in exacerbation-free weeks (mean 30.6, SD 13.3 vs mean 28.0, SD 14.8 weeks, respectively; rate ratio 1.21; 95% CI 0.77-1.91) or in health status, self-efficacy, self-management behavior, and health care utilization. Patients using the mHealth tool valued it as a more supportive tool than patients using the paper action plan. Patients considered the usability of the mHealth tool as good. Conclusions: This study did not show beneficial effects of a smart mHealth tool on exacerbation-free time, health status, self-efficacy, self-management behavior, and health care utilization in patients with COPD compared with the use of a paper action plan. Participants were positive about the supportive function and the usability of the mHealth tool. mHealth may be a valuable alternative for COPD patients who prefer a digital tool instead of a paper action plan. Trial Registration: ClinicalTrials.gov NCT02553096; https://clinicaltrials.gov/ct2/show/NCT02553096.

Establishing a link between atomistic processes and battery cell behavior is a major challenge for lithium ion batteries. Focusing on liquid electrolytes, we describe parameter-free molecular dynamics predictions of their mass and charge transport properties. The simulations agree quantitatively with experiments across the full range of relevant ion concentrations and for different electrolyte compositions. We introduce a simple analytic form to describe the transport properties. Our results are used in an extended Newman electrochemical model, including a cell temperature prediction. This cross-scale approach provides quantitative agreement between calculated and measured discharge voltage of a battery and enables the computational optimization of the electrolyte formulation.

Current tools for recording chronic obstructive pulmonary disease (COPD) exacerbations are limited and often lack validity testing. We assessed the validity of an automated telephonic exacerbation assessment system (TEXAS) and compared its outcomes with existing tools.Over 12 months, 86 COPD patients (22.1% females; mean age 66.5 yrs; mean post-bronchodilator forced expiratory volume in 1 s 53.4% predicted) were called once every 2 weeks by TEXAS to record changes in respiratory symptoms, unscheduled healthcare utilisation and use of respiratory medication. The responses to TEXAS were validated against exacerbation-related information collected by observations made by trained research assistants during home visits. No care assistance was provided in any way. Diagnostic test characteristics were estimated using commonly used definitions of exacerbation. Detection rates, compliance and patient preference were assessed, and compared with paper diary cards and medical record review.A total of 1,824 successful calls were recorded, of which 292 were verified by home visits (median four calls per patient, interquartile range three to five calls per patient). Independent of the exacerbation definition used, validity was high, with sensitivities and specificities between 66% and 98%. Detection rates and compliance differed extensively between the different tools, but were highest with TEXAS. Patient preference did not differ.TEXAS is a valid tool to assess COPD exacerbation rates in prospective clinical studies. Using different tools to record exacerbations strongly affects exacerbation occurrence rates.