jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pál D. Mezei | O. Anatole von Lilienfeld
Source: Journal of Chemical Theory and Computation (preprint, 2020-03-04)
Field: Materials Science — Materials Chemistry

Noncovalent Quantum Machine Learning Corrections to Density Functionals We present noncovalent quantum machine learning corrections to six physically motivated density functionals with systematic errors. We demonstrate that the missing massively nonlocal and nonadditive physical effects can be recovered by quantum machine learning models. The models seamlessly account for various types of noncovalent interactions and enable accurate predictions of dissociation curves. The correction improves the description of molecular two- and three-body interactions crucial in large water clusters and provides a reasonable atomic-resolution picture about the interaction energy errors of approximate density functionals that can be useful information in the development of more accurate density functionals. We show that given sufficient training instances the correction is more flexible than standard molecular mechanical dispersion corrections, and thus it can be applied for cases where many dispersion corrected density functionals fail, such as hydrogen bonding. Quantum ; Dissociation (chemistry) ; Non-covalent interactions ; Statistical physics ; Density functional theory ; Physics ; Dispersion (optics) ; Computer science ; Resolution (logic) ; Quantum mechanics ; Artificial intelligence ; Hydrogen bond ; Molecule ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: İbrahim Işık | Esme Işık | Hüseyin Toktamış
Source: DÜMF Mühendislik Dergisi (article, 2020-10-08)
Field: Materials Science — Materials Chemistry

Dose and fading time estimation of glass ceramic by using artificial neural network method Ceramic materials commonly used for dental prosthetics and restorations shows luminescent properties. Dental ceramics are considered the most natural-looking restorative materials for aesthetic rehabilitation due to their transparency. They are commonly used for dose response and fading assessment by using thermoluminescence method in various fields of dosimetric applications. In present study, we use artificial neural networks (ANN) toolbox of Matlab to predict irradiation dose and fading time using glow curve data from dental glass ceramic which is thermoluminescent (TL) dosimetric material. Temperature, dose value and fading time are used for input and TL intensity used for output component of the proposed ANN model. 18 neurons are used for hidden layer to analyze the experimental results of the model. Experimental and simulation results are compared and similarity is found as about 99 % in this present study. Fading ; Artificial neural network ; Thermoluminescence ; Ceramic ; Materials science ; MATLAB ; Dental ceramics ; Computer science ; Composite material ; Luminescence ; Algorithm ; Machine learning ; Optoelectronics Luminescence Properties of Advanced Materials ; Advanced Chemical Sensor Technologies ; Radiation Detection and Scintillator Technologies
Authors: Suresh Kondati Natarajan | Miguel A. Caro
Source: arXiv (Cornell University) (preprint, 2020-12-31)
Field: Materials Science — Materials Chemistry

Particle Swarm Based Hyper-Parameter Optimization for Machine Learned\n Interatomic Potentials Modeling non-empirical and highly flexible interatomic potential energy\nsurfaces (PES) using machine learning (ML) approaches is becoming popular in\nmolecular and materials research. Training an ML-PES is typically performed in\ntwo stages: feature extraction and structure-property relationship modeling.\nThe feature extraction stage transforms atomic positions into a\nsymmetry-invariant mathematical representation. This representation can be\nfine-tuned by adjusting on a set of so-called "hyper-parameters" (HPs).\nSubsequently, an ML algorithm such as neural networks or Gaussian process\nregression (GPR) is used to model the structure-PES relationship based on\nanother set of HPs. Choosing optimal values for the two sets of HPs is critical\nto ensure the high quality of the resulting ML-PES model.\n In this paper, we explore HP optimization strategies tailored for ML-PES\ngeneration using a custom-coded parallel particle swarm optimizer (available\nfreely at https://github.com/suresh0807/PPSO.git). We employ the smooth overlap\nof atomic positions (SOAP) descriptor in combination with GPR-based Gaussian\napproximation potentials (GAP) and optimize HPs for four distinct systems: a\ntoy C dimer, amorphous carbon, $\\alpha$-Fe, and small organic molecules (QM9\ndataset). We propose a two-step optimization strategy in which the HPs related\nto the feature extraction stage are optimized first, followed by the\noptimization of the HPs in the training stage. This strategy is computationally\nmore efficient than optimizing all HPs at the same time by means of\nsignificantly reducing the number of ML models needed to be trained to obtain\nthe optimal HPs. This approach can be trivially extended to other combinations\nof descriptor and ML algorithm and brings us another step closer to fully\nautomated ML-PES generation.\n Particle swarm optimization ; Computer science ; Artificial intelligence ; Test set ; Invariant (physics) ; Energy minimization ; Algorithm ; Gaussian ; Gaussian process ; Kriging ; Machine learning ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: James Chapman | Rampi Ramprasad
Source: The Journal of Chemical Physics (article, 2020-06-11)
Field: Materials Science — Materials Chemistry

Predicting the dynamic behavior of the mechanical properties of platinum with machine learning Over the last few decades, computational tools have been instrumental in understanding the behavior of materials at the nano-meter length scale. Until recently, these tools have been dominated by two levels of theory: quantum mechanics (QM) based methods and semi-empirical/classical methods. The former are time-intensive but accurate and versatile, while the latter methods are fast but are significantly limited in veracity, versatility, and transferability. Recently, machine learning (ML) methods have shown the potential to bridge the gap between these two chasms due to their (i) low cost, (ii) accuracy, (iii) transferability, and (iv) ability to be iteratively improved. In this work, we further extend the scope of ML for atomistic simulations by capturing the temperature dependence of the mechanical and structural properties of bulk platinum through molecular dynamics simulations. We compare our results directly with experiments, showcasing that ML methods can be used to accurately capture large-scale materials phenomena that are out of reach of QM calculations. We also compare our predictions with those of a reliable embedded atom method potential. We conclude this work by discussing how ML methods can be used to push the boundaries of nano-scale materials research by bridging the gap between QM and experimental methods. Transferability ; Bridging (networking) ; Computer science ; Molecular dynamics ; Scale (ratio) ; Scope (computer science) ; Work (physics) ; Computation ; Bridge (graph theory) ; Statistical physics ; Machine learning ; Algorithm ; Chemistry ; Mechanical engineering ; Computational chemistry ; Physics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Meltem KASAPOĞLU ÇALIK | Ebubekir Sıddık Aydın | Özgün Yücel
Source: Mugla Journal of Science and Technology (article, 2020-12-23)
Field: Chemistry — Analytical Chemistry

PREDICTION OF PROXIMATE ANALYSIS AND PROCESS TEMPERATURE OF TORREFIED AND PYROLYZED WOOD PELLETS BY NEAR-INFRARED SPECTROSCOPY COUPLED WITH MACHINE LEARNING Near-Infrared (NIR) Spectroscopy is a time and cost-effective method to characterize the materials in the food, petrochemical, pharmaceutical, and agricultural industries. Proximate analysis of the carbon-containing materials and investigating the effectiveness of the heat treatments on the material are a particularly time-consuming process. This work presents the four regression methods, i.e., decision tree regression, support vector regression and two versions of ensembles of decision trees to predict the proximate analysis of biomass and heat treatment temperature. Thus, effective method has been proposed to reduce experimental effort and present the characterization of heat-treated biomass feedstock theoretically. Prediction results show that SVR and ENS2 regression methods calibrating the NIR spectra to the values of wood pellet properties achieved good performance with the coefficient of determination (R2) of 0.880- 0.984 and RMSE of 0.444- 5.308 for ash and volatile matter. This study suggests that machine learning-based regression methods with integrated NIR spectroscopy of biomass is promising as an alternative method for rapid characterization. Another possible application of the current study is that it can be used for processed fuel recognition prior to a fully automated fuel quality assessment system in the biomass industry. Pellets ; Biomass (ecology) ; Torrefaction ; Petrochemical ; Raw material ; Regression analysis ; Process engineering ; Regression ; Decision tree ; Support vector machine ; Machine learning ; Materials science ; Environmental science ; Pyrolysis ; Computer science ; Mathematics ; Statistics ; Waste management ; Chemistry ; Engineering ; Composite material ; Environmental engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fermentation and Sensory Analysis
Authors: Rémy Brossard | Oriel Frigo | David Dehaene
Source: arXiv (Cornell University) (preprint, 2020-11-30)
Field: Materials Science — Materials Chemistry

Graph convolutions that can finally model local structure Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles. This hints at the fact that current networks fail to catch information about the local structure, which is problematic if the downstream task heavily relies on graph substructure analysis, as in the context of chemistry. We propose a very simple correction to the now standard GIN convolution that enables the network to detect small cycles with nearly no cost in terms of computation time and number of parameters. Tested on real life molecule property datasets, our model consistently improves performance on large multi-tasked datasets over all baselines, both globally and on a per-task setting. Substructure ; Computer science ; Computation ; Graph ; Simple (philosophy) ; Convolution (computer science) ; Task (project management) ; Theoretical computer science ; Context (archaeology) ; Property (philosophy) ; Algorithm ; Artificial intelligence ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Caro, MA | Csányi, G | Laurila, T | Deringer, VL | ,
Source: Cambridge University Engineering Department Publications Database (article, 2020-10-12)
Field: Materials Science — Materials Chemistry

Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon © 2020 American Physical Society. Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on extensive atomistic simulations of the deposition and growth of a-C films, describing interatomic interactions using a machine learning (ML) based Gaussian approximation potential model. We expand widely on our initial work [M. A. Caro, Phys. Rev. Lett. 120, 166101 (2018)PRLTAO0031-900710.1103/PhysRevLett.120.166101] by now considering a broad range of incident ion energies, thus modeling samples that span the entire range from low-density (sp2-rich) to high-density (sp3-rich, "diamondlike") amorphous forms of carbon. Two different mechanisms are observed in these simulations, depending on the impact energy: low-energy impacts induce sp- and sp2-dominated growth directly around the impact site, whereas high-energy impacts induce peening. Furthermore, we propose and apply a scheme for computing the anisotropic elastic properties of the a-C films. Our work provides fundamental insight into this intriguing class of disordered solids, as well as a conceptual and methodological blueprint for simulating the atomic-scale deposition of other materials with ML driven molecular dynamics. Deposition (geology) ; Amorphous solid ; Amorphous carbon ; Carbon fibers ; Range (aeronautics) ; Work (physics) Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials
Authors: Liang Chen | Dongmei Fu | Mindong Chen
Source: Journal of Materials Science (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Modeling and mining dual-rate sampled data in corrosion potential online detection of low alloy steels in marine environment Corrosion ; Alloy ; Seawater ; Materials science ; Metallurgy ; Artificial seawater ; Geology ; Oceanography Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Alok Ranjan Tripathy | Nimisha Srivastava | Ganeswar Nath | Rita Paikaray
Source: Egyptian Journal of Petroleum (article, 2020-12-25)
Field: Chemistry — Analytical Chemistry

Experimental and artificial neural network based analysis of solvent blends for dewaxing of crude oil Sustainability in flow condition by maintaining the pour point and cloud point in storage and transportation of crude oil is always a challengeable task for petroleum industry. Thus solvent dewaxing is an effective process used in oil refinery for monitoring before the transportation of crude oil in pipelines with an efficient and selective blended chemical. The present work describes the solvent dewaxing process through by analysis of basic fundamental interactions with computations of different acoustic parameters in presence of a high frequency ultrasonic wave. Experiments were performed to investigate the molecular interaction in rheological properties of waxy crude oil by blended solvent over a temperature range of 293 K to 323 K. The wax yield efficiency in the crude oil indicates that the performance of solvents blends depends on its mole fraction, compatibility of blends and on temperature of treatment. Addition of sonicated solvent blends in the ratio of 10:1, 15:1 and 20:1 improves flow conditions in crude oil pipe lines and increases its pour point. The sonication study was also designed by artificial neural network (ANN) model using R-Software which has been developed for ultrasonic velocity and other related parameters. The proposed ANN model for dewaxing of crude oil with blended solvent in different operating conditions provides comparable results with average absolute deviation (AAD) less than 0.5%. Pour point ; Solvent ; Materials science ; Enhanced oil recovery ; Petroleum engineering ; Chemical engineering ; Petroleum ; Light crude oil ; Pulp and paper industry ; Artificial neural network ; Chemistry ; Organic chemistry ; Computer science ; Machine learning ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Fluid Dynamics and Mixing
Authors: Antonio Paolo Carlucci | Antonio Ficarella | Luciano Strafella | Gianluca Trullo
Source: Journal of Energy Engineering (article, 2020-08-20)
Field: Materials Science — Materials Chemistry

Comprehensive Characterization of the Behavior of a Diesel Oxidation Catalyst Used on a Dual-Fuel Engine Aim of this work is the evaluation of thermal behavior and performance of a conventional diesel oxidation catalyst (DOC) installed on an engine operating in dual-fuel diesel-methane conditions following a well-defined methodology conceived ad hoc. A catalyst was sized, realized, and instrumented with five thermocouples. Once mounted along the engine exhaust line, the test was run in order to determine several parameters characterizing the DOC behavior, such as downstream emission levels, conversion efficiency, thermal behavior, and light-off temperature. Tests have been performed varying the engine speed and substitution ratio in order to analyze the effect of these operating parameters on the behavior of the catalyst. The results indicate that, in dual-fuel conditions, the oxidation of species takes place in zones closer to the exit section, determining temperatures higher than those measured in conventional diesel combustion conditions at the same engine load. The light-off temperatures related to the conversion efficiency of unburned hydrocarbons and carbon monoxide both increase in dual-fuel conditions. The effect of substitution ratio results from the combined effects on combustion development together with engine-out concentration of long-chain and short-chain unburned hydrocarbons. The resulting effect is, on one hand, the reduction of unburned hydrocarbons light-off temperature as methane increases. On the other hand, carbon monoxide light-off temperature increases as methane increases due to the increased amount of engine-out hydrocarbons emissions. The reduction of engine speed determines the reduction of the temperature of the exhaust gases to the extent that the light-off conditions are sometimes not reached. Finally, a sensitivity analysis base on neural networks showed that, while less variables are sufficient for describing the behavior, in terms of carbon monoxide conversion efficiency, of a DOC mounted on an engine working in dual-fuel diesel-methane conditions, in case of unburned hydrocarbons conversion a set of more variables is required. Methane ; Diesel fuel ; Diesel engine ; Combustion ; Exhaust gas ; Thermal efficiency ; Carbon monoxide ; Internal combustion engine ; Exhaust gas recirculation ; Environmental science ; Materials science ; Catalysis ; Automotive engineering ; Chemistry ; Waste management ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Combustion Engine Technologies
Authors: Yuanfeng Xia | Lixiang Wu | Gaofeng Wang
Source: Computational Materials Science (article, 2020-07-23)
Field: Materials Science — Materials Chemistry

Rapid evaluation method for anisotropic growth of WS2 monolayers by combining machine learning algorithms and kinetic Monte Carlo simulation data Kinetic Monte Carlo ; Monte Carlo method ; Anisotropy ; Monolayer ; Computer science ; Isotropy ; Algorithm ; Materials science ; Experimental data ; Nanotechnology ; Mathematics ; Physics ; Optics 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Samrendra Singh | Daniel Braginsky | Panos Tamamidis | G. Monacelli
Source: SAE International journal of commercial vehicles (article, 2020-09-25)
Field: Materials Science — Materials Chemistry

Designing Next Generation Exhaust Aftertreatment Systems Using Machine Learning &lt;div&gt;With ever tightening emission standards, the automotive industry is continuously seeking novel ways to improve the aftertreatment system (ATS). Exhaust treatment systems using diesel emission fluid (DEF), in conjunction with selective catalytic reduction (SCR) and diesel oxidation converters (DOC), have been gaining popularity in the heavy equipment industry. Spraying DEF (mixture of urea and water) into the exhaust flow can convert harmful NOx gases into N&lt;sub&gt;2&lt;/sub&gt; and H&lt;sub&gt;2&lt;/sub&gt;O. Design of ATSs focuses on high evaporation rate and uniform mixing of ammonia at the entrance to the SCR catalyst. This study applied support vector regressor (SVR), a machine learning (ML) method to a database of computational fluid dynamics (CFD) simulations to develop a highly efficient mixer with high heat exchange characteristics. Over 500 mixer designs were evaluated using CFD and were then used to train the SVR model. The trained ML model was then used as a surrogate to the CFD and coupled with the genetic algorithm (GA), an optimization technique, to further refine the design parameters. The optimal design obtained from this methodology showed a remarkable performance improvement compared to the baseline.&lt;/div&gt; Automotive engineering ; Computer science ; Aeronautics ; Engineering ; Control engineering Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fault Detection and Control Systems
Authors: Hemavathy Nagarajan | Pragna Lakshmi | Umashankar Vetrivel
Source: Computational Biology and Chemistry (article, 2020-08-02)
Field: Chemistry — Organic Chemistry

Deciphering potential inhibitors targeting THI4 of Fusarium solani sp. to combat fungal keratitis: An integrative computational approach Fusarium solani ; Virtual screening ; Docking (animal) ; Keratitis ; Fungal keratitis ; Computational biology ; Blindness ; Antifungal ; Biology ; Pharmacology ; Medicine ; Microbiology ; Bioinformatics ; Drug discovery ; Genetics Synthesis and biological activity ; Research on Leishmaniasis Studies ; Fungal Plant Pathogen Control
Authors: Soojung Yang | Kyung Hoon Lee | Seongok Ryu
Source: arXiv (Cornell University) (preprint, 2020-03-17)
Field: Materials Science — Materials Chemistry

A comprehensive study on the prediction reliability of graph neural\n networks for virtual screening Prediction models based on deep neural networks are increasingly gaining\nattention for fast and accurate virtual screening systems. For decision makings\nin virtual screening, researchers find it useful to interpret an output of\nclassification system as probability, since such interpretation allows them to\nfilter out more desirable compounds. However, probabilistic interpretation\ncannot be correct for models that hold over-parameterization problems or\ninappropriate regularizations, leading to unreliable prediction and decision\nmaking. In this regard, we concern the reliability of neural prediction models\non molecular properties, especially when models are trained with sparse data\npoints and imbalanced distributions. This work aims to propose guidelines for\ntraining reliable models, we thus provide methodological details and ablation\nstudies on the following train principles. We investigate the effects of model\narchitectures, regularization methods, and loss functions on the prediction\nperformance and reliability of classification results. Moreover, we evaluate\nprediction reliability of models on virtual screening scenario. Our result\nhighlights that correct choice of regularization and inference methods is\nevidently important to achieve high success rate, especially in data imbalanced\nsituation. All experiments were performed under a single unified model\nimplementation to alleviate external randomness in model training and to enable\nprecise comparison of results.\n Computer science ; Machine learning ; Reliability (semiconductor) ; Regularization (linguistics) ; Artificial intelligence ; Inference ; Artificial neural network ; Probabilistic logic ; Randomness ; Data mining Machine Learning in Materials Science ; Advanced Graph Neural Networks