1,721,017 research outputs found
Developing Large Language Models for Quantum Chemistry Simulation Input Generation
No full textThis repository encompasses all code used to run the experiments described in the study titled "Developing Large Language Models for Quantum Chemistry Simulation Input Generation". In addition to the code for our system architecture, we include the datasets described in the study, which can be used for further research. The repository also contains some generally helpful classes.
To reproduce the results from our study, refer to the Scripts folder, where we explain the scripts used to run our experiments and gather data. For more insight into the classes used and how to implement them in your own research, refer to the Classes folder. We for instance show how to easily use our rule-based system to generate different calculations. Additionally, you can inspect and extract the various datasets we used from the Data folder, where all available datasets are explained. The Orca Output folder stores all output files gathered from running ORCA calculations.
One important note is that to use the code in this repository, you should configure your own OpenAI API key in your system path. Moreover, to use RAG, one should scrape the ORCA input library with our provided script and add the ORCA manual to the Documents/Regular folder. We do not publish this here as we are not the writers
Uncovering Internal Prediction Mechanisms of Transformer-Based Chemical Foundation Models
No full textSupervised deep learning has become a standard approach to
deliver competitive predictive tools that allow relating the structure
of molecules and their physicochemical features to properties such as
binding to protein targets, performance as electronic materials, and
reactivity. However, efforts to understand how these models learn and
how specific predictions can be explained are still limited in
chemistry, which hinders trust in these predictions. To overcome both
the current widespread approach of considering supervised deep learning
models as black boxes and the limitations of explainable deep learning
based on feature attribution, we introduce two general methods from the
field of mechanistic interpretability to molecular property prediction.
Specifically, we leverage ablation and adapt existing techniques into
the regression lens to inspect model predictions of the
Transformer-based ChemBERTa foundation model. Our results for 3
ChemBERTa-based models finetuned on distinct datasets allow us to
propose the internal mechanisms operable within each of the
corresponding model layers that lead to the final predictions. Our
results are a stepping stone towards more trustworthy deep learning
models in the molecular domain
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“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
Full text linkWe 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
Full text linkWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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