jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elodie Niemiec
Source: theses.fr (ABES) (preprint, 2021-10-04)
Field: Chemistry — Inorganic Chemistry

Durability of a zinc oxide nanostructured coating formulated by soft chemistry for antibacterial and self-cleaning surfaces Cette étude est focalisée sur l’élaboration d’un revêtement nanostructuré par procédé chimie douce afin d’obtenir des propriétés antibactériennes et autonettoyantes. La stratégie adoptée a été de mettre en place une méthode chimique en voie aqueuse peu énergivore pour des raisons environnementales : le protocole adopté met en jeu la formation d’oxyde de zinc nanométrique par voie liquide à des températures inférieures à 100°C. En premier lieu, nous nous sommes attachés à étudier la relation entre la méthode de préparation du substrat et la durabilité du revêtement final déposé (état de surface initial, géométrie de la pièce…). Afin d’améliorer la tenue des germes initiateurs de croissance, un gel de silice a été mis au point, optimisé et préalablement déposé sur le substrat comme base d’accroche chimique. Le choix de l’oxyde de zinc en tant que cristallites nano-structurants apporte ensuite un caractère photo-catalytique qui active la dégradation de diverses substances organiques (salissures, bactéries). Cette propriété a par ailleurs été vérifiée via d’une part l’étude de la mortalité de bactéries modèles et d’autre part le suivi de décoloration d’un colorant, le bleu de méthylène, permettant de simuler une salissure. Les tests avec des souches bactériennes communes comme E. coli ont été réalisés et ont montré une prolifération plus réduite sur une surface traitée comparé à une surface nue. Une population de bactéries 3 à 4 fois moins importante sur la surface a été relevée. Il y a également 3 fois plus de mortalité dans les eaux de lavage. Ceci montre une difficulté d’accroche des bactéries ainsi qu’un effet de mortalité induit par le revêtement. Un dépôt en phase vapeur d’une molécule à longue chaîne carbonée permet d’accroître la propriété superhydrophobe de la surface, empêchant ainsi un dépôt de salissure et réduisant l’utilisation de produits de nettoyage, sans pour autant amoindrir les propriétés recherchées. La durabilité mécanique du revêtement a également été étudiée afin de vérifier le maintien des propriétés autonettoyantes à l’usage. Un test tribologique sur-mesure a été élaboré pour permettre de simuler le frottement d’un doigt sur la surface. Malgré l’usure constatée des aiguilles, le revêtement conserve son caractère hydrophobe et sa capacité de dégradation. Il a également été observé que la couche superhydrophobe se dégradait en priorité au profit de l’intégrité des nano-aiguilles. Durability ; Soft chemistry ; Zinc ; Coating ; Nanotechnology ; Materials science ; Oxide ; Metallurgy ; Chemistry ; Composite material Pigment Synthesis and Properties
Authors: Thomas de Dios Miguel
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2021-02-24)
Field: Chemistry — Spectroscopy

Préparation d’esters et d’aldéhydes à partir d’huiles végétales Vegetable oils are renewable resources that are available, cheap and safe. They are used in the preparation of biodiesel, polymers and surfactants. The cleavage of vegetable oils gives access to compounds with higher added values. In this context, we have been interested in the cleavage of oxidized intermediates such as 1,2-diketones and β-hydroxy hydroperoxides to obtain biobased esters and aldehydes. These diketones derived from vegetable oils have been prepared from α-hydroxyketones via a catalytic oxidation under oxygen. These diketones were cleaved via an organocatalytic method to obtain the corresponding esters. β-hydroxy hydroperoxides were synthesized from fatty epoxides with hydrogen peroxide and a metal catalyst and under mild conditions. Then β-hydroxy hydroperoxides has been thermally cleaved into aldehydes by a continuous flow method. Subsequently, this methodology was applied to cardanol to obtain monomers of interest. Finally, new fatty 1,2,4-trioxanes have been synthesized from β-hydroxy hydroperoxide derived from methyl oleate. Organic chemistry ; Chemistry ; Pulp and paper industry ; Engineering Analytical Chemistry and Chromatography ; Edible Oils Quality and Analysis ; Various Chemistry Research Topics
Authors: Victor Fung | Jiaxin Zhang | Eric Juarez | Bobby G. Sumpter
Source: ChemRxiv (preprint, 2021-01-21)
Field: Materials Science — Materials Chemistry

Benchmarking Graph Neural Networks for Materials Chemistry Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are proposed. Benchmarking ; Computer science ; Workflow ; Machine learning ; Hyperparameter ; Artificial intelligence ; Benchmark (surveying) ; Graph ; Strengths and weaknesses ; Flexibility (engineering) ; Artificial neural network ; Data science ; Theoretical computer science ; Database Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Fuel Cells and Related Materials
Authors: Xiaofei Sun | Jingyuan Zhu | Hengzhi You | Bin Chen | Fen‐Er Chen
Source: Research Square (preprint, 2021-08-26)
Field: Materials Science — Materials Chemistry

Highly accurate prediction of reaction performance and enantioselectivity with a uniform machine learning protocol Protocol (science) ; Computer science ; Machine learning ; Artificial intelligence ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Mohammadamin Tavakoli | Aaron Mood | David L. Van Vranken | Pierre Baldi
Source: arXiv (Cornell University) (preprint, 2021-03-24)
Field: Materials Science — Materials Chemistry

Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity There is a lack of scalable quantitative measures of reactivity for functional groups in organic chemistry. Measuring reactivity experimentally is costly and time-consuming and does not scale to the astronomical size of chemical space. In previous quantum chemistry studies, we have introduced Methyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a solvation model, as quantitative measures of reactivity for organic functional groups over the broadest range. Although MCA* and MAA* offer good estimates of reactivity parameters, their calculation through Density Functional Theory (DFT) simulations is time-consuming. To circumvent this problem, we first use DFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby establishing a large dataset of chemical reactivity scores. We then design deep learning methods to predict the reactivity of molecular structures and train them using this curated dataset in combination with different representations of molecular structures. Using ten-fold cross-validation, we show that graph attention neural networks applied to informative input fingerprints produce the most accurate estimates of reactivity, achieving over 91% test accuracy for predicting the MCA* plus-minus 3.0 or MAA* plus-minus 3.0, over 50 orders of magnitude. Finally, we demonstrate the application of these reactivity scores to two tasks: (1) chemical reaction prediction; (2) combinatorial generation of reaction mechanisms. The curated dataset of MCA* and MAA* scores is available through the ChemDB chemoinformatics web portal at www.cdb.ics.uci.edu. Reactivity (psychology) ; Density functional theory ; Affinities ; Computer science ; Chemical space ; Scalability ; Solvation ; Cheminformatics ; Computational chemistry ; Chemistry ; Molecular descriptor ; Artificial neural network ; Artificial intelligence ; Machine learning ; Molecule ; Drug discovery ; Quantitative structure–activity relationship ; Organic chemistry ; Database ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Esther Heid | William H. Green
Source: ChemRxiv (preprint, 2021-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction The estimation of chemical reaction properties such as activation energies, rates or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction, and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark datasets for future work. All datasets and the developed reaction GCNN model are available online, free of charge and open-source. Cheminformatics ; Computer science ; Benchmark (surveying) ; Convolutional neural network ; Artificial intelligence ; Graph ; Embedding ; Machine learning ; Molecular graph ; Reaction rate ; Theoretical computer science ; Chemistry ; Computational chemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Michael Gastegger | Kristof T. Schütt | Klaus‐Robert Müller
Source: DepositOnce (article, 2021-07-26)
Field: Chemistry — Physical and Theoretical Chemistry

Datasets: Machine learning of solvent effects on molecular spectra and reactions Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance. Beyond that, it is able to describe implicit and explicit molecular environments, operating as a polarizable continuum model for solvation or in a quantum mechanics / molecular mechanics setup. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction. Based on these results, we use FieldSchNet to design an external environment capable of lowering the activation barrier of the rearrangement reaction significantly, demonstrating promising venues for inverse chemical design. Computer science ; Chemistry ; Solvent ; Artificial intelligence ; Machine learning ; Organic chemistry Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: André Colliard-Granero | Mariah Batool | Jasna Janković | Jenia Jitsev | Michael Eikerling | Kourosh Malek | Mohammad J. Eslamibidgoli
Source:  (preprint, 2021-09-30)
Field: Materials Science — Materials Chemistry

Deep Learning for the Automation of Particle Analysis in Catalyst Layers for Polymer Electrolyte Fuel Cells The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often \textit{ad hoc}, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. Our approach employs training an instance segmentation model, called StarDist [Schmidt et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2018, Lecture Notes in Computer Science, vol 11071. Springer, Cham], which resolves the main challenge in the pixel-wise localization of nanoparticles in TEM images: the overlapping particles. The segmentation maps outperform models reported in the literature, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost. Computer science ; Preprocessor ; Segmentation ; Pixel ; Deep learning ; Automation ; Artificial intelligence ; Particle (ecology) ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Enhanced Oil Recovery Techniques
Authors: Nguyễn Tiến Cường | Van-Quyen Nguyen | Van-Linh Ngo | Quang-Khoat Than | Tien-Lam Pham
Source: Computational Materials Science (preprint, 2021-08-17)
Field: Materials Science — Materials Chemistry

Learning hidden chemistry with deep neural networks Atom (system on chip) ; Artificial neural network ; Computer science ; Visualization ; Artificial intelligence ; Deep learning ; Cluster (spacecraft) ; Scaling ; Machine learning ; Multidimensional scaling ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Abhishek S. Bhesania | Parvesh Kamboj | Sai Abhishek Peddakotla | Rakesh K. Mathpal | Vaibhav K. Arghode
Source: AIAA AVIATION 2021 FORUM (article, 2021-07-28)
Field: Materials Science — Materials Chemistry

On Extraction of Parameters for Pyrolysis Reaction Kinetics of Organic Ablative Materials with Molecular Dynamics and Unsupervised Machine Learning Approach View Video Presentation: https://doi.org/10.2514/6.2021-3127.vid In the current study, we aim to demonstrate a methodology to extract parameters of reaction kinetics associated with an ablative material using molecular dynamics simulations and machine learning techniques. We perform molecular dynamics simulations on crosslinked and non-crosslinked forms of a polymer in temperature regime where they would undergo pyrolysis decomposition. We perform dimensionality reduction on the bonding parameters obtained during the pyrolysis simulations to extract concentration profiles of the reactants, which are then used to derive the reaction kinetics parameters. As an additional study, we perform a macroscale one-dimensional heat conduction analysis to study thermal response of crosslinked and non-crosslinked polymers with the kinetics parameters derived in the study. Kinetics ; Pyrolysis ; Molecular dynamics ; Polymer ; Materials science ; Chemical kinetics ; Curse of dimensionality ; Biological system ; Chemical physics ; Chemistry ; Computer science ; Computational chemistry ; Composite material ; Organic chemistry ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Injection Molding Process and Properties
Authors: Amir Abbas Kazemzadeh Farizhandi | Omar Betancourt | Mahmood Mamivand
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning Approach for Chemistry and Processing History Prediction From Materials Microstructure: Application to Spinodal Decomposition Spinodal decomposition ; Microstructure ; Spinodal ; Decomposition ; Deep learning ; Artificial intelligence ; Materials science ; Nanotechnology ; Statistical physics ; Computer science ; Chemistry ; Physics ; Metallurgy ; Organic chemistry ; Phase (matter) Machine Learning in Materials Science
Authors: Edirisuriya M. Dilanga Siriwardane | Steph-Yves Louis | Rajendra P. Joshi | Neeraj Kumar | Jianjun Hu
Source:  (preprint, 2021-08-03)
Field: Materials Science — Materials Chemistry

ACCURATE PREDICTION OF VOLTAGE OF BATTERY ELECTRODE MATERIALS USING ATTENTION BASED GRAPH NEURAL NETWORKS Performing first principle calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use pre-defined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed method, which combines both atomic composition and atomic coordinates in 3D-space, improves the accuracy in voltage prediction by 17% when compared to composition based ML models. The first model directly learns the chemical reaction of electrodes and metal-ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (Eform) to compute their average voltage. Our models demonstrates improved accuracy in transferability from our subset of learned metal-ions to other metal-ions. Computer science ; Transferability ; Voltage ; Electrode ; Convolutional neural network ; Graph ; Chemical space ; Ion ; Artificial neural network ; Battery (electricity) ; Artificial intelligence ; Algorithm ; Biological system ; Machine learning ; Theoretical computer science ; Chemistry ; Electrical engineering ; Physics ; Physical chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Lenz Fiedler | Karan Shah | Michael Bußmann | Attila Cangi
Source: arXiv (Cornell University) (preprint, 2021-10-03)
Field: Materials Science — Materials Chemistry

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex datasets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of density functional theory and machine learning has the potential to rapidly advance electronic structure applications such as in-silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both density functional theory and machine learning on a generally accessible level. This serves as the basis of our comprehensive review including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis. Computer science ; Density functional theory ; Artificial intelligence ; Machine learning ; Categorization ; Data science ; Computational learning theory ; Field (mathematics) ; Scope (computer science) ; Scalability ; Chemistry ; Active learning (machine learning) ; Computational chemistry ; Database ; Mathematics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Yixuan Che | Junyi Zhao | Hao Wang
Source: ChemRxiv (preprint, 2021-12-20)
Field: Materials Science — Materials Chemistry

Machine learning prediction of the electronic property of binary transition metal alloys Machine learning methods have garnered much attention and use in computational catalysis. Previous studies have demonstrated rapid and accurate prediction of a variety of catalytic properties as well as the underlying potential energy landscapes. In particular, d-band center, defined as the first moment of the d-projected density of states, has been widely used as the key descriptor of activity trends for reactions catalyzed by metal surfaces. In this work, we construct a gradient boosting regression (GBR) model for prediction of the d-band center of bulk binary transition metal alloys. An accurate model is obtained using a dataset of over 1200 alloys from the Materials Project database spanning the entire d-block of the periodic table. The d-band centers, periodic groups, and relative compositions of the constituent metals are determined to have the highest feature importance scores, consistent with the underlying physics of the alloy. The regression model presented here offer a promising strategy of rapid property prediction with physical interpretability to aid the optimization and discovery of efficient heterogeneous catalysts. Interpretability ; Binary number ; Gradient boosting ; Machine learning ; Artificial intelligence ; Periodic table ; Boosting (machine learning) ; Property (philosophy) ; Computer science ; Materials science ; Mathematics ; Chemistry ; Random forest Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques