1,721,008 research outputs found
AI3SD Video: Accelerating design of organic materials with machine learning and AI
No full textDeep learning is revolutionizing many areas of science and technology, particularly in natural language processing, speech recognition, and computer vision. In this talk, we will provide an overview of the latest developments of machine learning and AI methods and application to the problem of drug discovery and development at Isayev’s Lab at CMU. We identify several areas where existing methods have the potential to accelerate materials research and disrupt more traditional approaches. First we will present a deep learning model that approximates the solution of Schrodinger equation. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions. Second, we proposed a novel ML-guided materials discovery platform that combines synergistic innovations in automated flow synthesis and automated machine learning (AutoML) method development. A software-controlled, continuous polymer synthesis platform enables rapid iterative experimental–computational cycles that resulted in the synthesis of hundreds of unique copolymer compositions within a multi-variable compositional space. The non-intuitive design criteria identified by ML, which was accomplished by exploring less than 0.9% of overall compositional space, upended conventional wisdom in the design of 19F MRI agents and led to the identification of >10 copolymer compositions that outperformed state-of-the-art materials
Humans of AI3SD: Olexandr Isayev
No full textIn this Humans of AI4SD interview he discusses developing the next generation of computational chemistry methods, the challenges and rewards of multidisciplinary projects, how AI is changing science and the importance of cultural change for open science
A High-Throughput Computational Study Driven by the AiiDA Materials Informatics Framework and the PAULING FILE as Reference Database
No full textConfronted with the explosion of computing power and the vast quantities of materials data thus produced, Gray proposed in 2009 the “Fourth Paradigm of Science: Data-Intensive Discovery through Data Exploration (eScience)" [Gray, J. (2009). The Fourth Paradigm, Data-Intensive Scientific Discovery (ed. T. Hey, S. Tansley and K. Tolle), xvii-xxxi. Redmond, Washington: Microsoft Corporation], defined by the unification of experiment, theory and computation. Within computational materials science, the third (computational) and fourth (data) paradigms can become the supporting pillars for a fifth one, of database-driven and database-filling research. There, our goal is to map out the missing pieces of all possible combinations of two elements in arbitrary stoichiometries and for any structure-a search simple in its definition, but that even for very simple model potentials (e.g. a binary Lennard-Jones) can give rise to millions of different structures for just one binary compound. In addition the number of potential chemical element combinations (equal to all potential inorganic solids) forces us to develop approaches that are able to reduce it to a realistic subset of the most likely ones, to be the first targets for a simulation and then to be experimentally investigated, in the search for novel inorganic solids. Here, we outline how a trustworthy database (comprehensively spanning the published experimental inorganic solids) linked to a database of high-throughput density functional theory (DFT) calculations developed from a curated reference database of experimental results (namely, the PAULING FILE-Binaries Edition (http://www.paulingfile.com)) can bridge the fourth and fifth paradigms in the case of materials science.THEO
Artificial Intelligence (AI) Solutions for Computational & Organic Chemistry
No full textOlexandr Isayev talks about using neural networks to create fast and accurate molecular potentials trained on high-level QM data. The resulting ANI model (ANAKIN-ME: Accurate NeurAl networK engINefor Molecular Energies) seems to be very promising.
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DEVELOPMENT OF INTELLIGENT SYSTEMS FOR ORGANIC MATERIALS ENGINEERING
Full text linkThe accelerated availability of data to 21st century chemists has created unprecedented opportunities and two fundamental challenges for the chemical discovery process. The first challenge is that the number of potentially synthesizable organic molecules is so large that uncovering complete structure property relationships is infeasible. The second challenge is that, as scientists explore this chemical space, they produce equally large amounts of experimental, computational, and verbal data that is difficult to manage and integrate. While the methods for addressing these challenges have historically remained separated, this thesis presents computational and conceptual frameworks for navigating these challenges simultaneously through a combination of data curation, machine learning, and knowledge representation and reasoning. Here is presented a general purpose data curation pipeline for organic materials informatics and a demonstration of its utility through the production of polymer and dye databases. This foundation is then used to develop machine learning models for chemical use case predictions in a departure from the traditional property based modeling paradigm. Finally, ontology-guided systems and structured knowledge are leveraged to improve reasoning and accuracy in large language models, enabling more interpretable and reliable question answering in chemistry. Taken together, these individual contributions form the basis of intelligent systems that integrate data, knowledge, and reasoning into unified discovery workflows that can accelerate the discovery and production of organic materials.Doctor of Philosoph
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
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