AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.Comment: 30 pages, 7 figure

The impact of Chinese FDI in Africa: evidence from Ethiopia

We exploit exogenous variation in China’s export taxes to investigate the impact of Chinese foreign direct investment (FDI) in Ethiopia. Higher sector-specific export taxes in China lead to more Chinese FDI in Ethiopian districts specialized in those sectors and generate highly heterogeneous effects. Domestic firms competing with Chinese FDI reduce their sales, investment, inputs and prices, while firms in upstream and downstream sectors expand. We build a 20-year district panel of night lights and observe that Chinese FDI leads to no instantaneous impact on local growth, but significant and persistently positive effects after 6-12 years

Modelling the distribution of health related quality of life of advancedmelanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression

Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper, we present an approach based on quantile and M-quantile regression to achieve this goal. We applied the methodologies to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood

Learning from Major Accidents: a Machine Learning Approach

A B S T R A C T Learning from past mistakes is crucial to prevent the reoccurrence of accidents involving dangerous sub-stances. Nevertheless, historical accident data are rarely used by the industry, and their full potential is largely unexpressed. In this setting, this study set out to take advantage of improvements in data sci-ence and Machine Learning to exploit accident data and build a predictive model for severity prediction. The proposed method makes use of classification algorithms to map the features of an accident to the corresponding severity category (i.e., the number of people that are killed and injured). Data extracted from existing databases is used to train the model. The method has been applied to a case study, where three classification models - i.e., Wide, Deep Neural Network, and Wide&Deep - have been trained and evaluated on the Major Hazard Incident Data Service database (MHIDAS). The results indicate that the Wide&Deep model offers the best performance.(c) 2022 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/

Texture analysis as a tool to study the kinetics of wet agglomeration processes

In this work wet granulation experiments were carried out in a planetary mixer with the aim to develop a novel analytical tool based on surface texture analysis. The evolution of a simple formulation (300 g of microcrystalline cellulose with a solid binders pre-dispersed in water) was monitored from the very beginning up to the end point and information on the kinetics of granulation as well as on the effect of liquid binder amount were collected. Agreement between texture analysis and granules particle size distribution obtained by sieving analysis was always found. The method proved to be robust enough to easily monitor the process and its use for more refined analyses on the different rate processes occurring during granulation is also suggested

Bloch's theorem in orbital-density-dependent functionals: Band structures from Koopmans spectral functionals

Koopmans-compliant functionals provide an orbital-density-dependent framework for an accurate evaluation of spectral properties; they are obtained by imposing a generalized piecewise-linearity condition on the total energy of the system with respect to the occupation of any orbital. In crystalline materials, due to the orbital-density-dependent nature of the functionals, minimization of the total energy to a ground state provides a set of minimizing variational orbitals that are localized and thus break the periodicity of the underlying lattice. Despite this, we show that Bloch symmetry can be preserved and it is possible to describe the electronic states with a band-structure picture, thanks to the Wannier-like character of the variational orbitals. We also present a method to unfold and interpolate the electronic bands from supercell ($\Gamma$-point) calculations, which enables us to calculate full band structures with Koopmans-compliant functionals. The results obtained for a set of benchmark semiconductors and insulators show very good agreement with state-of-the-art many-body perturbation theory and experiments, underscoring the reliability of these spectral functionals in predicting band structures.Comment: 34 pages, 4 figure