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Dataset supporting the University of Southampton Doctoral Thesis "Machine learning of quantum mechanical lattice energies for molecular crystal structure prediction"
No full textDataset supporting the University of Southampton Doctoral Thesis "Machine learning of quantum mechanical lattice energies for molecular crystal structure prediction".
The dataset includes input files, output files and summarised data involved in the writing of the thesis. The data was generated using the in-house code CSPy by the Day group. The data was also generated using VASP, CRYSTAL17, Gaussian09 and Python3 packages.</span
Machine learning of quantum mechanical lattice energies for molecular crystal structure prediction
No full textIt is essential to study the crystal phases, i.e. polymorphs, of an organic molecule, under different conditions for the discovery and safety of new functional materials, and the effectiveness and toxicity of pharmaceuticals. This can be avoided by understanding the differences and likely transitions between polymorphs. Crystal Structure Prediction (CSP) is the process of generating crystal structures of a molecule(s). One way of doing this is to apply symmetry operations to a unit cell containing a molecule(s) representing the periodic structure, and optimising the geometries to local energy minima on the potential energy surface (PES) for the lattice. A local optimisation algorithm can be more useful than finding the global minimum of the energy landscape since several low-lying energy polymorphs can be observed.Due to the number of atoms involved in organic molecular crystals, classical force fields (FFs) are typically used for calculating the lattice energy, and only the most likely, low energy candidates, are re-optimised using a higher level of the hierarchy of electronic structure methods.The final re-optimisation step using a suitable electronic structure method is crucial since the lattice energy of polymorphs can differ by sub kJ/mol (Nyman et al. (2016); Nyman and Day (2015)) so method errors can significantly change the ranking of the lowest energy polymorphs, particularly if free energies are calculated. This becomes even more problematic due to the thousands of polymorphs in the low energy window of the energy landscape, for example 20 kJ/mol or below.The aim is to reduce the enormous computational cost of this using Machine Learning (ML). This work will begin by reviewing current CSP methods and ML techniques. A set of thiophenes will become the focus to demonstrate the usefulness of CSP in the discovery of organic semiconductor materials. ML techniques will be applied to the PES to investigate an inexpensive geometry optimisation, where poorly predicted structures may be limiting the accuracy of the energy ranking of the CSP landscape
Electron localisation descriptors in ONETEP: a tool for interpreting localisation and bonding in large-scale DFT calculations
No full textElectron localisation descriptors, such as the electron localisation function (ELF) and localisedorbital locator (LOL) provide a visual tool for interpreting the results of electronic structurecalculations. The descriptors produce a quantumvalence shell electron pair repulsion (VSEPR)representation, indicating the localisation of electron pairs into bonding pairs and lone pairs insingle molecules, coordination compounds andcrystalline solids. We have implemented the ELFand LOL within ONETEP, a DFT code designed to perform calculations on systems containingthousands of atoms with plane-wave accuracy. This is possible using a linear-scaling formulation ofDFT in which the Kohn–Sham orbitals are expressed in terms of a set of strictly localisednon-orthogonal generalised Wannier functions (NGWFs), themselves expanded in a psinc basisset. In this paper, we describe our implementation and explore the chemical insights offered byelectron localisation descriptors in ONETEP in a range of bonding and nonbondedsituations
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications
No full textThe field of crystal structure prediction (CSP) has changed dramatically over the past decade and methods now exist that will strongly influence the way that new materials are discovered, in areas such as pharmaceutical materials and the discovery of new, functional molecular materials with targeted properties. Machine learning (ML) methods, which are being applied in many areas of chemistry, are starting to be explored for CSP. This overview will discuss the areas where ML is expected to have the greatest impact on CSP and its applications: improving the evaluation of energies; analyzing the landscapes of predicted structures and for the identification of promising molecules for a target property
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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