jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrés Rafael Zúñiga Pavón
Source:  (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Análise comparativa de correlações e redes neurais para predição de propriedades de fluidos de petróleo Estimation of PVT (Pressure, Volume, Temperature) properties is essential to perform any calculation in the petroleum industry. These properties are derived from laboratory measurements and/or PVT mathematical correlations. Errors in PVT data inference can lead to erroneous reserve estimates, inaccurate prediction of production flows, poorly performing artificial lift system designs, and incorrect parameter estimates for well testing. In the absence of laboratory measurement of PVT properties, the use of appropriate techniques to calculate them as accurately as possible is paramount. Obtaining properties is so important that there is a vast amount of research in the literature in this sense. Numerous PVT correlations have been developed over the years, what shows the relevance of this topic. Several authors have advanced in creating their own correlations and given the complexity of the characterization of petroleum fluids, either by API grade, chemical composition or geographical location, these correlations may change in their parameters and functional structure. In the current scenario where the application of artificial intelligence techniques is growing in the oil industry to meet the many challenges of our daily operating activities, neural networks play a flashy yet powerful role in predicting many of the variables used in the oil industry, and therefore the prediction of PVT properties. This work aims to comparatively analyse the predictability of five petroleum fluid properties: bubble pressure, unsaturated oil viscosity, dead oil viscosity, gas-oil ratio and isothermal compressibility by: 1) Ghetto et al. (1994) correlations based on 195 samples of oil from literature versus parameters reestimation of those correlations using sample groups for training and validation through minimization of relative absolute average error and 2) use of neural networks applied to the same used group in item 1 versus the parameters reestimation of those correlations through minimization of sum of least squares. Among the two approaches, neural networks showed absolute average error below 15% for some properties, while for the rest of them, they imitated approximately the performance of the correlations. Petroleum industry ; Petroleum ; Predictability ; Petroleum engineering ; Oil viscosity ; Enhanced oil recovery ; Viscosity ; Computer science ; Mathematics ; Environmental science ; Thermodynamics ; Statistics ; Geology ; Physics Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Power Transformer Diagnostics and Insulation
Authors: Xiaoyang Liu | Harry C. Dorn
Source: ECS Meeting Abstracts (article, 2019-05-01)
Field: Chemistry — Physical and Theoretical Chemistry

(Invited) New Approaches in Computational Modeling of Fullerene/Metallofullerenes Today, computational models are extensively applied in scientific research to address fundamental as well as practical issues in material science. Fullerenes and metallofullerenes are well recognized as potential materials for next-generation electronic devices, such as quantum computers. The rapid advance in fullerene/metallofullerene research requires improved theoretical tools to understand fundamental as well as practical applications of these new members of fullerene/metallofullerene family. In this presentation, we will describe the extension of the famous spiral program by Fowler and Manolopoulos 1 to generate new non-classical isomers. We have extended this approach beyond 5 and 6 membered fullerene structures to other polygon faces, such as, heptagons. In addition, we have developed a machine learning tool based on a general relationship between the structure patterns and physical properties. Finally, we will describe our current modeling studies regarding chromatographic retention mechanism(s) of fullerene/metallofullerene molecules. Reference: Fowler, Patrick W., and D. E. Manolopoulos. An Atlas of Fullerenes . Clarendon Press-Oxford, 1995. Metallofullerene ; Fullerene ; Computer science ; Nanotechnology ; Computational chemistry ; Materials science ; Chemistry History and advancements in chemistry ; Fullerene Chemistry and Applications
Authors: Vesna Pehar | Davor Oršolić | Višnja Stepanić
Source:  (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Application of machine learning for herbicide characterization Herbicides are chemical molecules used for destruction of weeds. Massive usage of herbicides has resulted in two global problems: increase in weed resistance and harmful impact of human health [1, 2]. In order to facilitate development of novel, more specific herbicides and of strategies for impeding the weed resistance, we have carried out extensive in silico analysis of the set of herbicides. Herein, we present results revealing links between structural, physicochemical, ADME (Absorption, Distribution, Metabolism, Excretion) and toxic features for herbicides (Figure 1). The analysis has been done by using proper machine learning approaches. References: [1] A. Forouzesh, E. Zand, S. Soufizadeh, S. S. Foroushani, Weed Res. 55 (2015) 334-358. [2] V. I. Lushchak, T. M. Matviishyn, V. V. Husak, J. M. Storey, K. B. Storey, EXCLI J. 17 (2018) 1101-1136. ADME ; Weed ; In silico ; Herbicide resistance ; Human health ; Computer science ; Artificial intelligence ; Agronomy ; Biology ; Pharmacokinetics ; Biochemistry ; Bioinformatics ; Medicine ; Environmental health Spectroscopy and Chemometric Analyses
Authors: Nanda Gopala Kilingar | Ludovic Noels | Thierry Massart | Karim Ehab Moustafa Kamel | Bernard Sonon | Christophe Leblanc | Anne Jung
Source: Open Repository and Bibliography (University of Liège) (article, 2019-07-14)
Field: Materials Science — Polymers and Plastics

Data driven computational analysis of open foam RVEs An automated approach, that relies on the use of distance and level set functions as explained in [1], has been described in [2] to build computationally Representative Volume Elements (RVE) of open foam materials, enabling the study of the effects of the microstructural features on the macroscopic behavior. These models have been compared with real foam samples from existing literature to verify statistically the morphological properties like face-to-cell ratio, edge-to-face ratio and strut length distribution along with the variations in the strut morphology like the shape of cross-sections of the struts and their variation along the axis of the struts. The responses obtained from a uniaxial compression test of the sample RVEs have been validated against the experimental observations and the results have showed close similarity with respect to the variations in the foam density. This approach enables us to generate multiple Stochastic volume elements (SVE) and get their material response in a short period of time. In [3], the authors have taken inspiration from artificial neural network concepts and used linear elastic RVE data to train a material network to describe complex material behavior. They have also validated the extrapolations of the trained network to a wide range of problems, including non-linear history-dependent plasticity and finite-strain hyper-elasticity under large deformations. In the current work, the goal is to utilize the material responses obtained from the SVEs as the prespecified material data set ( Figure 1 ) and investigate the performance of various data-driven solvers on these data sets in order to eliminate the experimental testing altogether with the knowledge that the material response is in close agreement to that of the RVEs. Computer science Polymer Foaming and Composites ; Advanced Numerical Analysis Techniques
Authors: Matthew Putman | Vadim Pinskiy | Denis Sharoukhov
Source:  (article, 2019-03-08)
Field: Materials Science — Surfaces, Coatings and Films

Inspection and functionality convergence (Conference Presentation) The last several years has been one of rapid adoption of new technologies that use the convergence of super resolution optical microscopy and Artificial intelligence, not only for research but in factory settings. Detection and classification of defects and devices on semiconductors has commonly been used for process control and quality control. Now, utilizing advances in deep learning and other forms of Artificial intelligence, defect inspection framework is extended to be used not only as a reliable tool for identifying problems, but assigning casualty. This presentation will focus on a new convergence of high throughput inspection with Photoluminescence Imaging. Photoluminescence Imaging allows quantification and colocalization of different types of defects by combining multiple spectral snapshots of the sample collected with different output filters. These defects cannot be detected or classified under conventional brightfield microscopy. This method will provide a way to test functionality on the same wafer or product, and potentially at the same time as routine classification, giving a robust single station for solving production problems and leading faster design iteration. Data will be presented that shows this functionality in its early stages. Computer science ; Artificial intelligence ; Convergence (economics) ; Throughput ; Wafer ; Process (computing) ; Pixel ; Photoluminescence ; Computer vision ; Materials science ; Nanotechnology ; Telecommunications ; Optoelectronics Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Surface Roughness and Optical Measurements
Authors: Alberto Hernandez | Adarsh Balasubramanian | Fenglin Yuan | Simon J. Mason | Tim Mueller
Source: arXiv (Cornell University) (preprint, 2019-04-01)
Field: Materials Science — Materials Chemistry

Fast, accurate, and transferable many-body interatomic potentials by\n symbolic regression The length and time scales of atomistic simulations are limited by the\ncomputational cost of the methods used to predict material properties. In\nrecent years there has been great progress in the use of machine learning\nalgorithms to develop fast and accurate interatomic potential models, but it\nremains a challenge to develop models that generalize well and are fast enough\nto be used at extreme time and length scales. To address this challenge, we\nhave developed a machine learning algorithm based on symbolic regression in the\nform of genetic programming that is capable of discovering accurate,\ncomputationally efficient manybody potential models. The key to our approach is\nto explore a hypothesis space of models based on fundamental physical\nprinciples and select models within this hypothesis space based on their\naccuracy, speed, and simplicity. The focus on simplicity reduces the risk of\noverfitting the training data and increases the chances of discovering a model\nthat generalizes well. Our algorithm was validated by rediscovering an exact\nLennard-Jones potential and a Sutton Chen embedded atom method potential from\ntraining data generated using these models. By using training data generated\nfrom density functional theory calculations, we found potential models for\nelemental copper that are simple, as fast as embedded atom models, and capable\nof accurately predicting properties outside of their training set. Our approach\nrequires relatively small sets of training data, making it possible to generate\ntraining data using highly accurate methods at a reasonable computational cost.\nWe present our approach, the forms of the discovered models, and assessments of\ntheir transferability, accuracy and speed.\n Overfitting ; Simplicity ; Computer science ; Genetic programming ; Symbolic regression ; Set (abstract data type) ; Machine learning ; Simple (philosophy) ; Artificial intelligence ; Interatomic potential ; Algorithm ; Theoretical computer science ; Artificial neural network ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Caroline Desgranges | Jérôme Delhommelle
Source: The Journal of Physical Chemistry (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Ensemble Learning of Partition Functions for the Prediction of Thermodynamic Properties of Adsorption in Metal–Organic and Covalent Organic Frameworks We develop several ensemble learning models to predict the partition function of fluids adsorbed in nanoporous materials. The partition functions so obtained allow rapid access to all thermodynamic properties of adsorption and provide an assessment of the performance of these materials over a wide range of conditions, without performing additional simulations. We examine different types of applications related to the environment and energy, including carbon capture and storage in IRMOF-1, hydrogen storage in COF-102, as well as the separation of light hydrocarbons, methane, and ethane in COF-102 and COF-108. A diversity ensemble learning approach, in which predictions obtained from neural networks with different architectures are averaged, is found to provide the most accurate predictions. Comparisons with simulation results obtained using grand-canonical Monte Carlo simulations and flat-histogram methods establish the versatility of the ensemble learning model. Most notably, the new insights brought by the ensemble learning predictions include the rapid determination of the selectivity and of the desorption free energy over a wide range of operating conditions (pressure or mole fraction of the gas feed). This, in turn, provides a means to assess rapidly the performance of nanoporous materials for separation purposes, as well as of the energetic cost to regenerate the adsorbent. Nanoporous ; Chemistry ; Grand canonical ensemble ; Canonical ensemble ; Adsorption ; Ensemble learning ; Monte Carlo method ; Partition (number theory) ; Covalent organic framework ; Metal-organic framework ; Thermodynamics ; Hydrogen storage ; Range (aeronautics) ; Statistical physics ; Hydrogen ; Machine learning ; Porosity ; Materials science ; Physical chemistry ; Computer science ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Carbon Dioxide Capture Technologies
Authors: Xabier Rodríguez‐Martínez | E. Pascual-San José | Antonio Sánchez‐Díaz | Albert Harillo‐Baños | Mariano Campoy‐Quiles
Source: DIGITAL.CSIC (Spanish National Research Council (CSIC)) (article, 2019-02-11)
Field: Materials Science — Surfaces, Coatings and Films

Finding the needle in the haystack: High-throughput photovoltaic materials screening In this talk I will first describe a novel methodology for the fast evaluation of donor/acceptor systems for photovoltaics. The new approach, up to 100 times faster than conventional optimization protocols, is based on the use of Raman to quantify the local thickness and composition in samples with lateral gradients on parameters of interest [1, 2]. Raman images are combined with photocurrent images (LBIC) to identify the optimum conditions. We demonstrate the potential of the methodology optimizing three systems PCDTBT:PC70BM, PTB7-Th:PC70BM and PffBT4T-2OD:PC70BM, obtaining efficiencies circa 6%, 8% and 10%, respectively, using less than 50 mg of each polymer in the process [2]. I will show that this method can be used also to analyze blends containing non-fullerene acceptors and ternary systems [3] and it can be extended for the case of evaporated bilayer solar cells by using moving shadow masks [4] as well as polymer:polymer processed through microfluidic chips dispensers [5]&#13;\nFinally, I will describe our first attempts to use these large datasets (&gt;25.000 points per material system) as input in machine learning algorithms and what we could learn from this exercise [3].&#13;\n[1] X. Rodríguez-Martínez et al, J. Mater. Chem. C , 5, 7270 (2017)&#13;\n[2] A. Sanchez-Díaz et al, Adv. Elec. Mater., 2018, DOI: 10.1002/aelm.201700477 &#13;\n[3] X. Rodríguez-Martínez, E. Pascual, A. Harillo, R. Gimerá and M. Campoy-Quiles, unpublished.&#13;\n[4] X. Rodríguez-Martínez, et al., Organic Electronics, 59, 288 (2018). &#13;\n[5] X. Rodríguez-Martínez, S. Semih, X. Su, P. Pamies, O. Inganas, J. Puigmartí, M. Campoy Quiles, 2018, in preparation Haystack ; Photovoltaic system ; Throughput ; Computer science ; Medicine ; Engineering ; Artificial intelligence ; Telecommunications ; Electrical engineering Electron and X-Ray Spectroscopy Techniques
Authors: Yuan Dong | Dawei Li | Chi Zhang | Chuhan Wu | Hong Wang | Ming Xin | Jianlin Cheng | Jian Lin
Source: arXiv (Cornell University) (preprint, 2019-08-21)
Field: Materials Science — Materials Chemistry

Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN Inverse design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. The massive combinational spaces due to the constituent elements and their structural configurations are too overwhelming to be all searched even by high-throughput computations. Herein, we demonstrated a novel regressional generative adversarial network (RGAN) for inverse design of representative two-dimensional materials, graphene and boron-nitride (BN) hybrids. A significant novelty of the proposed RGAN is that it combines the supervised and regressional convolutional neural network (CNN) with the traditional unsupervised GAN, thus overcoming the common technical barrier in the traditional GANs, which cannot generate data associated with given continuous quantitative labels. The proposed RGAN enables to autonomously generate graphene/BN hybrids with any given bandgaps. Moreover, the generated structures exhibit high fidelity, yielding bandgaps within ~ 10% MAEF of the desired bandgaps as cross-validated by density functional theory (DFT) calculations. Further analysis by principle component analysis (PCA) and modified locally linear embedding (MLLE) methods on the latent features encoded by the regressor reveals that the generator has successfully generated structures that followed the statistical distribution of the real structures. It implies the possibility of the RGAN in recognizing physical rules hidden in the high-dimensional data. This new inverse design methodology would speed up the discovery and development of other 2D materials and beyond. Boron nitride ; Inverse ; Computer science ; Computation ; Algorithm ; Artificial intelligence ; Materials science ; Mathematics ; Nanotechnology Machine Learning in Materials Science ; Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Yongmin Liu
Source:  (article, 2019-05-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral light-matter interactions for imaging and sensing (Conference Presentation) Chirality refers to the structural property of an object that cannot be superposed onto its mirror image. The existence of chirality in nature is universal, ranging from molecules at the nanoscale to gastropod shells at the macroscale. Light can be chiral as well. Circularly polarized light with opposite helicity has its electric field vector rotating clockwise or counterclockwise during propagation. Chiral light-matter interactions are widely used in molecule detection, optical communication and quantum information processing. In this talk, I will discuss how to engineer the chiral light-matter interaction based on metamaterials and nano photonic structures towards novel imaging and sensing applications. I will first present a new concept of chiral metamirrors, which can achieve near-perfect reflection of designated circularly polarized light without reversing its handedness, yet complete absorption of the other polarization state [1]. Such a metamaterial can be used for polarimetric imaging to extract the polarization information of light [2]. Recently, we have applied the deep learning approach to accelerate the design of chiral metamaterials with prescribed chiroptical responses [3]. Finally, I will discuss the generation of chiral hotspots in silicon nanocube dimers that can amplify circular dichroism signals by one order of magnitude [4]. Our findings would lead to integrated devices for circular dichroism spectroscopy, enantioselective sensing, sorting and synthesis. References: [1] Z. J. Wang et al., "Circular Dichroism Metamirrors with Near-Perfect Extinction", ACS Photonics 3, 2096 (2016); [2] L. Kang et al., "Preserving Spin States upon Reflection: Linear and Nonlinear Responses of a Chiral Meta-Mirror", Nano Letters 17, 7102 (2017); [3] W. Ma et al., "Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials", ACS Nano 12, 6326 (2018). Research Highlight in Nature Photonics 12, 443 (2018); [4] K. Yao and Y. M. Liu, "Enhancing circular dichroism by chiral hotspots in silicon nanocube dimers", Nanocale 10, 8779 (2018). Metamaterial ; Circular polarization ; Circular dichroism ; Physics ; Photonics ; Chirality (physics) ; Optics ; Polarization (electrochemistry) ; Photonic metamaterial ; Dichroism ; Quantum mechanics ; Chemistry ; Crystallography Metamaterials and Metasurfaces Applications
Authors: Ruud van Deursen | Guillaume Godin
Source: arXiv (Cornell University) (preprint, 2019-09-24)
Field: Materials Science — Materials Chemistry

Deep Generative Model for Sparse Graphs using Text-Based Learning with Augmentation in Generative Examination Networks Graphs and networks are a key research tool for a variety of science fields, most notably chemistry, biology, engineering and social sciences. Modeling and generation of graphs with efficient sampling is a key challenge for graphs. In particular, the non-uniqueness, high dimensionality of the vertices and local dependencies of the edges may render the task challenging. We apply our recently introduced method, Generative Examination Networks (GENs) to create the first text-based generative graph models using one-line text formats as graph representation. In our GEN, a RNN-generative model for a one-line text format learns autonomously to predict the next available character. The training is stopped by an examination mechanism checking validating the percentage of valid graphs generated. We achieved moderate to high validity using dense g6 strings (random 67.8 +/- 0.6, canonical 99.1 +/- 0.2). Based on these results we have adapted the widely used SMILES representation for molecules to a new input format, which we call linear graph input (LGI). Apart from the benefits of a short compressible text-format, a major advantage include the possibility to randomize and augment the format. The generative models are evaluated for overall performance and for reconstruction of the property space. The results show that LGI strings are very well suited for machine-learning and that augmentation is essential for the performance of the model in terms of validity, uniqueness and novelty. Lastly, the format can address smaller and larger dataset of graphs and the format can be easily adapted to define another meaning of the characters used in the LGI-string and can address sparse graph problems in used in other fields of science. Computer science ; Generative grammar ; Generative model ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Curse of dimensionality ; Representation (politics) Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Advanced Graph Neural Networks