jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sebastian Dick | Mariví Fernández-Serra
Source: ChemRxiv (article, 2019-10-11)
Field: Materials Science — Materials Chemistry

Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density &lt;div&gt;Density Functional Theory (DFT) is the standard formalism to study the electronic structure&lt;/div&gt;&lt;div&gt;of matter at the atomic scale. In Kohn-Sham DFT simulations, the balance between accuracy&lt;/div&gt;&lt;div&gt;and computational cost depends on the choice of exchange and correlation functional, which only&lt;/div&gt;&lt;div&gt;exists in approximate form. Here we propose a framework to create density functionals using&lt;/div&gt;&lt;div&gt;supervised machine learning, termed NeuralXC. These machine-learned functionals are designed to&lt;/div&gt;&lt;div&gt;lift the accuracy of baseline functionals towards that are provided by more accurate methods while&lt;/div&gt;&lt;div&gt;maintaining their efficiency. We show that the functionals learn a meaningful representation of the&lt;/div&gt;&lt;div&gt;physical information contained in the training data, making them transferable across systems. A&lt;/div&gt;&lt;div&gt;NeuralXC functional optimized for water outperforms other methods characterizing bond breaking&lt;/div&gt;&lt;div&gt;and excels when comparing against experimental results. This work demonstrates that NeuralXC&lt;/div&gt;&lt;div&gt;is a first step towards the design of a universal, highly accurate functional valid for both molecules&lt;/div&gt;&lt;div&gt;and solids.&lt;/div&gt; Density functional theory ; Lift (data mining) ; Formalism (music) ; Physics ; Computer science ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Donald J. Siegel
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

(Invited) Computational Discovery of Metal-Organic Frameworks for Hydrogen Storage: Combining High-Throughput Screening, Machine Learning, and Experimental Demonstration Metal organic frameworks (MOFs) are promising materials for the storage of hydrogen fuel due to their high surface areas, tunable properties, and reversible gas adsorption. Although several MOFs are known to exhibit high hydrogen densities on a gravimetric basis, realizing high volumetric capacities a critical attribute for maximizing the driving range of fuel cell vehicles remains a challenge. Here, MOFs that achieve high gravimetric and volumetric H2 densities simultaneously are identified computationally, and demonstrated experimentally. The hydrogen capacities of ~100,000 MOFs drawn from databases of known and hypothetical compounds were predicted using empirical correlations and direct atomistic simulations. Based on these predictions, promising MOF candidates were synthesized and evaluated with respect to their usable H2 capacities. Several MOFs predicted to exhibit high capacities displayed low surface areas upon activation, highlighting the need to understand the factors that control stability. Consistent with the computational predictions, several MOFs were experimentally demonstrated to exhibit an uncommon combination of high usable volumetric and gravimetric capacities. Importantly, the measured capacities exceed those of the benchmark compound MOF-5, the record-holder for combined volumetric/gravimetric performance. Multiple machine learning algorithms were trained on this computationally-generated database. The relative accuracy of these algorithms were compared, with the most promising algorithm applied to predict H2 capacities in an additional 400,000 MOFs. Our study illustrates the value of computational screening in pinpointing materials that optimize overall storage performance. Gravimetric analysis ; Hydrogen storage ; USable ; Benchmark (surveying) ; Metal-organic framework ; Throughput ; Computer science ; Range (aeronautics) ; Adsorption ; Materials science ; Process engineering ; Hydrogen ; Nanotechnology ; Chemistry ; Engineering Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Arunkumar Chitteth Rajan | Avanish Mishra | Swanti Satsangi | Rishabh Vaish | Abhishek K. Singh
Source: NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (article, 2019-03-05)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene MXenes are two-dimensional (2D) transition metal carbides and nitrides, and are invariably metallic in pristine form. While spontaneous passivation of their reactive bare surfaces lends unprecedented functionalities, consequently a many-folds increase in number of possible functionalized MXene makes their characterization difficult. Here, we study the electronic properties of this vast class of materials by accurately estimating the band gaps using statistical learning. Using easily available properties of the MXene, namely, boiling and melting points, atomic radii, phases, bond lengths, etc., as input features, models were developed using kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms. Among these, the GPR model predicts the band gap with lowest root-mean-squared error (rmse) of 0.14 eV, within seconds. Most importantly, these models do not involve the Perdew-Burke-Ernzerhof (PBE) band gap as a feature. Our results demonstrate that machine-learning models can bypass the band gap underestimation problem of local and semilocal functionals used in density functional theory (DFT) calculations, without subsequent correction using the time-consuming GW approach. Materials science ; Band gap ; Computer science ; Optoelectronics MXene and MAX Phase Materials ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Yuzevych, Larysa | Skrynkovskyy, Ruslan | Yuzevych, Volodymyr | Lozovan, Vitalii | Pawlowski, Grzegorz | Yasinskyi, Mykhailo | Ogirko, Ihor
Source: The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy (article, 2019-07-30)
Field: Materials Science — Materials Chemistry

Удосконалення діагностування підземних трубопроводів нафтогазових підприємств на основі визначення водневого показника pH ґрунтових середовищ з використанням нейронних мереж A set of key parameters and information flows has been formed to simulate stages of probing the outside surface of underground metal pipelines (UMP) taking into account pH of the soil contacting with the pipe metal.Specimens of 17G1S steel placed in acid, alkaline and neutral media were examined using a polarization potential meter in a complex with a contactless current meter. Principles of application of neural networks (NN) in processing experimental results were formulated. A database has been developed. It meets the initial conditions for controlling the soil pH at the boundary with the metal under real conditions.Elements of the optimization approach for assessing pH of a coated UMP in the soil medium were proposed. The approach is based on the multiplicative qualimetric criterion of quality for the UMP section taking into account two groups of coefficients. The first group of coefficients refers to the internal coefficients and characterizes the metal pipeline and the second group refers to the external medium (i.e., soil electrolyte). Elements of the optimization approach for assessing pH of the coated pipeline in the soil medium were proposed.An NN was presented for the "pipeline-coating" system, which:1) is capable of solving the problem of cluster analysis and image classification;2) makes it possible to process data without their prior spectral transformation operating with discrete counts of information signals.The proposed NN type allows it to dynamically expand its own knowledge base of possible types of defects in controlled objects (pipelines) in the process of operation. With the help of the NN, soil pH was assessed for an UMP of 17G1S steel for three situations.The above information is important for improving the methods for controlling oil-and-gas enterprise UMPs, in particular, the methods for a correct assessment of anode current density in metal defects taking into account nonlinear character of informative parameters. Pipeline transport ; Process (computing) ; Pipeline (software) ; Base (topology) ; Transformation (genetics) ; Multiplicative function ; Set (abstract data type) ; Base metal Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Structural Integrity and Reliability Analysis
Authors: Chun Ma | Chen Hu | Emre Yengel | Hendrik Faber | Jafar I. Khan | Weimin Zhang | Frédéric Laquai | Iain McCulloch | Thomas D. Anthopoulos
Source:  (article, 2019-01-08)
Field: Materials Science — Polymers and Plastics

Multi-Input Parameter Modulable Memtransistors from Hybrid Perovskite/Conjugated Polymer Heterostructures Neuromorphic computing has attracted enormous attention in recent years due to its impressive capabilities to address the inherent limitations of conventional integrated circuit technology, ranging from perception, pattern recognition to memory and decision-making. Inspired by the morphology and function of 1014 synapses in mammalian brain, silicon-based asynchronous spiking neural networks (SNNs) have ushered in an era of developing non-volatile resistive switching as memristors for artificial intelligence systems. Despite its low power consumption, high endurance and fast read/write time, two-terminal memristor suffers from the singular function and the lack of heterosynaptic plasticity, similar to the conditioned reflex of human beings. Inspired by advanced unconditioned reflex, multi-terminal memristive transistors (memtransistors) were developed to realize complex functions, such as multi-factors modulation and heterosynaptic plasticity. Here, we report the development of hybrid memtransistors, modulable by multiple physical inputs (light, field and electric bias), using hybrid perovskite and conjugated polymer heterojunction channels processed from solution-phase at room temperature. The devices exhibit non-volatile but highly reversible conductance modulation due to I-(Br-) ions migration which result to the formation of a tunable Schottky barrier at the injecting electrode interface. Meanwhile, the memtransistors show excellent gate tunability of six orders of magnitudes with large switching ratios (102), high endurance (&gt;102 times) and long-term retention. Furthermore, photo-induced halides redistribution results in light plasticity of the polymer passivated perovskite memtransistor, which may be applicable in image identification and pattern recognition. Lastly, use of in situ scanning Kelvin probe microscopy and variable temperature measurement reveal the kinetics of the all-important bias- and photo-induced halide ions migration across the channel. Overall, the multi-physical input parameter memtransistor could enable the development of complex neuromorphic system capable of combining electrical with optical signals from the near-infrared to gamma rays. Conjugated system ; Polymer ; Perovskite (structure) ; Heterojunction ; Materials science ; Computer science ; Optoelectronics ; Chemical engineering ; Composite material ; Engineering Conducting polymers and applications ; Perovskite Materials and Applications ; Dielectric materials and actuators
Authors: Matteo Negri | Davide Bergamini | Carlo Baldassi | Riccardo Zecchina | Christoph Feinauer
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-09-30)
Field: Materials Science — Materials Chemistry

Natural representation of composite data with replicated autoencoders ABSTRACT Generative processes in biology and other fields often produce data that can be regarded as resulting from a composition of basic features. Here we present an unsupervised method based on autoencoders for inferring these basic features of data. The main novelty in our approach is that the training is based on the optimization of the ‘local entropy’ rather than the standard loss, resulting in a more robust inference, and enhancing the performance on this type of data considerably. Algorithmically, this is realized by training an interacting system of replicated autoencoders. We apply this method to synthetic and protein sequence data, and show that it is able to infer a hidden representation that correlates well with the underlying generative process, without requiring any prior knowledge. AUTHOR SUMMARY Extracting compositional features from noisy data and identifying the corresponding generative models is a fundamental challenge across sciences. The composition of elementary features can have highly non-linear effects which makes them very hard to identify from experimental data. In biology, for instance, one challenge is to identify the key steps or components of molecular and cellular processes. Representative examples are the modeling of protein sequences as the composition of patterns influenced by phylogeny or the identification of gene clusters in which the presence of specific genes depends on the evolutionary history of the cell. Here we present an unsupervised machine learning technique for the analysis of compositional data which is based on entropic neural autoencoders. Our approach aims at finding deep autoencoders that are highly invariant with respect to perturbations in the inputs and in the parameters. The procedure is efficient to implement and we have validated it both on synthetic and protein sequence data, where it can be shown that the latent variables of the autoencoders are non trivially correlated with the true underlying generative processes. Our results suggests that the local entropy approach represents a general valuable tool for the extraction of compositional features in hard unsupervised learning problems. Representation (politics) ; Generative grammar ; Novelty ; Unsupervised learning ; Generative model ; Novelty detection ; Pattern recognition (psychology) ; Identification (biology) ; Artificial neural network Machine Learning in Materials Science ; Morphological variations and asymmetry ; Genomics and Phylogenetic Studies
Authors: Abdellah Ousaa | B. Elidrissi | Mounir Ghamali | Samir Chtita | Adnane Aouidate | Mohammed Bouachrıne | Tahar Lakhlifi | S Croft | L Seifert | K | Yardley | M Mcconville | E Handman | L Rocha | G Almeida | J R Macedo | R Barbosa-Filho | J | O Kayser | A Kiderlen | S L Croft | P Desjeux | W Denny | M Wainwright | I Antonini | M Demeunynck | F Charmantray | A Martelli | V Marrapu | M Mittal | R Shivahare | S Gupta | K Bhandari | N Srinivas | N Shakya | A Tahghighia | M Hamzeh-Mivehroud | Asadpour Zeynali | K Foroumadi | A Dastmalchi | S | S Chtita | M Ghamali | R Hmamouchi | E Bouhya | M Bourass | M Larif | M Bouachrine | T Lakhlifi | A Ousaa | B Elidrissi | A Aouidate | A Ghaleb | A Golbraikh | A Tropsha | L Eriksson | J Jaworska | A P Worth | M T D Cronin | R Mcdowell | P Gramatica
Source: Chemical Science Transactions (article, 2019-01-24)
Field: Chemistry — Organic Chemistry

QSAR Study of (5-Nitroheteroaryl-1,3,4-thiadiazole-2- yl)piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents To search of newer and potent antileishmanial drugs, a series of 36 compounds of 5-(5nitroheteroaryl-2-yl)-1,3,4-thiadiazole derivatives were subjected to a quantitative structure-activity relationship (QSAR) analysis for studying, interpreting and predicting activities and designing new compounds using several statistical tools, The multiple linear regression (MLR), non-linear regression (RNLM) and artificial neural network (ANN) models were developed using 30 molecules having pIC 50 ranging from 3.155 to 5.046. The best generated MLR, RNLM and ANN models show conventional correlation coefficients R of 0.750, 0.782 and 0.967 as well as their leave-one-out cross-validation correlation coefficients R CV of 0.722, 0.744 and 0.720, respectively. The predictive ability of those models was evaluated by the external validation using a test set of 6 molecules with predicted correlation coefficients R test of 0.840, 0.850 and 0.802, respectively. The applicability domains of MLR and MNLR transparent models were investigated using William's plot to detect outliers and outsides compounds. We expect that this study would be of great help in lead optimization for early drug discovery of new similar compounds. Chemistry ; Quantitative structure–activity relationship ; Stereochemistry ; Combinatorial chemistry Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Research on Leishmaniasis Studies
Authors: Giuseppe D’Alessio | Gianmarco Aversano | Kamila Zdybał | Alberto Cuoci | Alessandro Parente
Source: Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (article, 2019-05-07)
Field: Chemistry — Analytical Chemistry

Feature extraction in combustion applications The high dimensional, non-linear nature of combustion problems modeled by detailed kinetic schemes makes the interpreta- tion of combustion data very challenging. Machine learning methodologies allow for a lower-dimensional representation of high-dimensional data. Thus, they represent an essential tool in supporting data interpretation and visualization. Principal Component Analysis (PCA), Independent Component Analysis (ICA), Dynamic Mode Decomposition (DMD) and auto-encoders are the most employed techniques in data analysis. PCA finds a new, reduced set of uncorrelated variables, which are a linear combination of the original ones, that account for most of the original data variance. ICA finds a new set of variables, linear combination of the original ones, that maximize a certain inde- pendence measure. DMD finds a set of modes each of which is associated with a fixed oscillation frequency and growth rate. Auto-encoders find a non-linear, lower dimensional manifold that approximates the original data with low errors. These methods can be trained on cheap data (1D flames) for subsequent analysis of more complex data-sets (3D turbulent flames). The potential of these methodologies has been investigated on a DNS simulation of a co-flow syngas turbulent jet. Different regions of the flame could be identified in an unsupervised fashion, characterized by different main chemical species and processes. The original data reconstruction from their low-dimensional representation has been used as quality measure for the feature extraction process. Feature (linguistics) ; Extraction (chemistry) ; Combustion ; Computer science ; Artificial intelligence ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Advanced Measurement and Detection Methods
Authors: Vasile‐Dan Hodoroaba
Source:  (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

New reference material candidates for traceable size measurement of nonspherical nanoparticles Size measurement of nanoparticles (NP) becomes a challenging analytical problem when non-spherical shapes must be traceably measured. However, most industrial NPs have irregular shapes and broad size distribution making it often more complex to follow European regulatory to identify a material as a nanomaterial according to which accurate measurement of the smallest dimension and its size Distribution is necessary. The European research project nPSize - Improved traceability chain of nanoparticle size measurements aims to fill this gap by developing potential non-spherical reference nanoparticles, measurement procedures and physical modelling to improve the traceability chain, comparability and compatibility for NP size measurements between different methods. Therefore, new model NP with well-controlled shape has been synthesized and are supposed to be systematically characterized using the traceable methods scanning/transmission electron microscopy, atomic force microscopy and small angle X-ray scattering. Following NP candidates are under investigation with respect to their homogeneity and stability: (i) titania nanoplatelets (10-15 nm thickness x 50-100 nm lateral), (ii) titania bipyramides (~60 nm length x 40 nm width), (iii) titania acicular particles (100 nm length x 15-20 nm width; aspect ratio 5.5/6), (iv) gold nanorods (~10 nm width x 30 nm length), and (v) gold nanocubes (~55 nm x 55 nm x 55 nm). In addition, sample preparation procedures as well as measurement analysis procedures with evaluation of appropriate measurands and descriptors for each material class and method are being developed to support standardization. To underpin the traceability of the size measurement of nonspherical NP, physical modelling of the signals in e.g. electron microscopy techniques will be used and in combination, the implementation of machine learning is aimed to facilitate measurement Analysis procedures, especially regarding the accurate thresholding/segmentation of the NPs. Materials science ; Nanometrology ; Nanorod ; Nanomaterials ; Traceability ; Nanoparticle ; Nanotechnology ; Acicular ; Transmission electron microscopy ; Analytical Chemistry (journal) ; Atomic force microscopy ; Mathematics ; Chemistry ; Composite material ; Statistics ; Chromatography ; Microstructure Electrostatics and Colloid Interactions ; nanoparticles nucleation surface interactions
Authors: Mark Jones
Source: Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Design and Optimisation of Oleochemical Processes Process Systems Engineering (PSE) is a discipline which connects a wide range of chemical engineering topics in a systems view approach. The reason for this systematic view of this scientific field is the need of computational concepts, numerical methods and computer-aided tools which can be applied to different use cases in industry. The multi-scale framework developed in this work encompasses four levels of application: (I) A server based property prediction software prototype which quantifies the uncertainty of group contribution methods and quantitative structure property relationship models and provides the confidence bounds of the estimates. (II) A modelling development level which allows the user to develop models in a flexible way by using common programming languages for fast prototyping (Python) and high performance computing (Fortran). (III) An interface to process simulators to analyse and optimise entire flowsheets with advanced routines. (IV) A superstructure optimisation layer where surrogate models generated from unit operations or process models can be embedded in a superstructure formulation and solved for the optimal process structure and operating point. The contributions presented in this work show how the developed framework allows to tackle research in machine learning, optimisation and Monte Carlo driven methods such as sensitivity analysis. The developed tools were applied to the oleochemical domain with selected processes. In conclusion, this work demonstrates that a modular approach to process systems engineering, combined with tools integration from various vendors, allows to gain new knowledge in a time-efficient and augmentable manner. Chemistry Mesoporous Materials and Catalysis