jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammad M. Sultan | Vijay S. Pande
Source: arXiv (Cornell University) (preprint, 2018-02-28)
Field: Materials Science — Materials Chemistry

Automated design of collective variables using supervised machine learning Selection of appropriate collective variables for enhancing sampling of molecular simulations remains an unsolved problem in computational biophysics. In particular, picking initial collective variables (CVs) is particularly challenging in higher dimensions. Which atomic coordinates or transforms there of from a list of thousands should one pick for enhanced sampling runs? How does a modeler even begin to pick starting coordinates for investigation? This remains true even in the case of simple two state systems and only increases in difficulty for multi-state systems. In this work, we solve the initial CV problem using a data-driven approach inspired by the filed of supervised machine learning. In particular, we show how the decision functions in supervised machine learning (SML) algorithms can be used as initial CVs (SML_cv) for accelerated sampling. Using solvated alanine dipeptide and Chignolin mini-protein as our test cases, we illustrate how the distance to the Support Vector Machines' decision hyperplane, the output probability estimates from Logistic Regression, the outputs from deep neural network classifiers, and other classifiers may be used to reversibly sample slow structural transitions. We discuss the utility of other SML algorithms that might be useful for identifying CVs for accelerating molecular simulations. Artificial intelligence ; Machine learning ; Hyperplane ; Computer science ; Support vector machine ; Sampling (signal processing) ; Artificial neural network ; Supervised learning ; Sample (material) ; Algorithm ; Mathematics ; Detector Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Jennifer N. Wei | David Belanger | Ryan P. Adams | D. Sculley
Source: arXiv (Cornell University) (preprint, 2018-11-21)
Field: Chemistry — Spectroscopy

Predicting Electron-Ionization Mass Spectrometry using Neural Networks When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules. Achieving high accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine learning-based work on spectrum prediction. Fragmentation (computing) ; Artificial neural network ; Computer science ; Ionization ; Mass spectrum ; Electron ionization ; Mass spectrometry ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Data mining ; Chemistry ; Chromatography ; Ion Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Oses Corey | Toher Cormac | Stefano Curtarolo
Source: arXiv (Cornell University) (preprint, 2018-03-13)
Field: Materials Science — Materials Chemistry

Autonomous data-driven design of inorganic materials with AFLOW The expansion of programmatically-accessible materials data has cultivated opportunities for data-driven approaches. Highly-automated frameworks like AFLOW not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams and properties of disordered materials. In combination with standardized parameter sets, the wealth of data is ideal for training machine learning algorithms, which have already been employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. These methods promise to revolutionize the path to synthesis and, ultimately, transform the practice of traditional materials discovery to one of rational and autonomous materials design. Process engineering ; Computer science ; Engineering Machine Learning in Materials Science
Authors: Nasruddin Nasruddin | Maria Lucia Ardhani Dwi Lestari | Supriyadi Supriyadi | S. Sholahudin
Source: IOP Conference Series Materials Science and Engineering (article, 2018-03-01)
Field: Materials Science — Materials Chemistry

Optimization Study of Hydrogen Gas Adsorption on Zig-zag Single-walled Carbon Nanotubes: <i>The Artificial Neural Network Analysis</i> The use of hydrogen gas in fuel cell technology has a huge opportunity to be applied in upcoming vehicle technology. One of the most important problems in fuel cell technology is the hydrogen storage. The adsorption of hydrogen in carbon-based materials attracts a lot of attention because of its reliability. This study investigated the adsorption of hydrogen gas in Single-walled Carbon Nano Tubes (SWCNT) with chilarity of (0, 12), (0, 15), and (0, 18) to find the optimum chilarity. Artificial Neural Networks (ANN) can be used to predict the hydrogen storage capacity at different pressure and temperature conditions appropriately, using simulated series of data. The Artificial Neural Network is modeled as a predictor of the hydrogen adsorption capacity which provides solutions to some deficiencies in molecular dynamics (MD) simulations. In a previous study, ANN configurations have been developed for 77k, 233k, and 298k temperatures in hydrogen gas storage. To prepare this prediction, ANN is modeled to find out the configurations that exist in the set of training and validation of specified data selection, the distance between data, and the number of neurons that produce the smallest error. This configuration is needed to make an accurate artificial neural network. The configuration of neural network was then applied to this research. The neural network analysis results show that the best configuration of artificial neural network in hydrogen storage is at 233K temperature i.e. on SWCNT with chilarity of (0.12). Carbon nanotube ; Adsorption ; Artificial neural network ; Hydrogen ; Zigzag ; Materials science ; Nanotechnology ; Chemical engineering ; Computer science ; Chemistry ; Artificial intelligence ; Engineering ; Organic chemistry ; Mathematics Hydrogen Storage and Materials ; Graphene research and applications ; Advancements in Battery Materials
Authors: Peter Zaspel | Bing Huang | Helmut Harbrecht | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2018-08-08)
Field: Materials Science — Materials Chemistry

Boosting quantum machine learning models with multi-level combination\n technique: Pople diagrams revisited Inspired by Pople diagrams popular in quantum chemistry, we introduce a\nhierarchical scheme, based on the multi-level combination (C) technique, to\ncombine various levels of approximations made when calculating molecular\nenergies within quantum chemistry. When combined with quantum machine learning\n(QML) models, the resulting CQML model is a generalized unified recursive\nkernel ridge regression which exploits correlations implicitly encoded in\ntraining data comprised of multiple levels in multiple dimensions. Here, we\nhave investigated up to three dimensions: Chemical space, basis set, and\nelectron correlation treatment. Numerical results have been obtained for\natomization energies of a set of $\\sim$7'000 organic molecules with up to 7\natoms (not counting hydrogens) containing CHONFClS, as well as for $\\sim$6'000\nconstitutional isomers of C$_7$H$_{10}$O$_2$. CQML learning curves for\natomization energies suggest a dramatic reduction in necessary training samples\ncalculated with the most accurate and costly method. In order to generate\nmilli-second estimates of CCSD(T)/cc-pvdz atomization energies with prediction\nerrors reaching chemical accuracy ($\\sim$1 kcal/mol), the CQML model requires\nonly $\\sim$100 training instances at CCSD(T)/cc-pvdz level, rather than\nthousands within conventional QML, while more training molecules are required\nat lower levels. Our results suggest a possibly favourable trade-off between\nvarious hierarchical approximations whose computational cost scales differently\nwith electron number.\n Quantum machine learning ; Quantum chemistry ; Kernel (algebra) ; Quantum ; Basis set ; Basis (linear algebra) ; Boosting (machine learning) ; Molecule ; Quantum chemical ; Set (abstract data type) ; Algorithm ; Statistical physics ; Mathematics ; Computer science ; Physics ; Quantum mechanics ; Machine learning ; Quantum algorithm ; Discrete mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Dmitry Nazarenko | И. А. Родин | О. А. Шпигун
Source: Industrial laboratory Diagnostics of materials (article, 2018-10-26)
Field: Chemistry — Analytical Chemistry

The use of machine learning in the analytical control of the preparations of medicinal plants Despite the fact that the global market for medicinal plants amounts to hundreds of billions of dollars, there is almost no government control over the quality of such pharmaceuticals in most countries of the world. This is partly attributed to the complex composition of plant materials: traditional analytical methodology is based on the use of standard reference samples for each analyte. In this case, preparations based on medicinal plants may contain tens and hundreds of physiologically active components. Isolation of those compounds in a pure form in practice is carried out using preparative chromatography, which leads to their high cost. Moreover, varying of the chemical composition of the medicinal plants depending on the geographical origin of the raw materials interfere with prescribing strict ranges of permissible contents for all physiologically active components. Combination of the above factors limits the possibilities of using traditional approaches to analysis, requiring strict standardization, the list of compounds for each type of plant, levels of contents and the availability of the reference materials and standards of comparison. This led to the study of the possibility of introducing various mathematical approaches as an auxiliary methodology. Unlike traditional methodologies, machine learning approaches are based on the correct collection of the data samples. Such a sample should contain groups of the samples that correspond to the states of the object which the developed algorithm must distinguish: authentic/fake, pure/containing impurities, effective/not containing a certain level of active components, etc. This review is devoted to consideration of the application of machine learning technique to the problems of chemical analysis and production control of raw materials of medicinal plants and preparations on their base for the last 15 years. Standardization ; Analyte ; Raw material ; Medicinal plants ; Sample (material) ; Biochemical engineering ; Computer science ; Quality (philosophy) ; Chemistry ; Traditional medicine ; Engineering ; Chromatography ; Organic chemistry ; Medicine Chromatography in Natural Products ; Phytochemicals and Antioxidant Activities ; Pharmacological Effects of Natural Compounds
Authors: Iryna V. Biblik | К. В. Аврамов | Roman Rusanov
Source: Problemy mašinostroeniâ/Problemy mašinostroeniâ (article, 2018-09-30)
Field: Materials Science — Materials Chemistry

Prediction of Flow Accelerated Corrosion of NPP Pipeline Elements by Network Simulation Method Based on a comprehensive approach that uses the computer simulation of the process of destroying structural materials and technology of self-learning neural networks, a methodology has been developed for predicting the rate of flow accelerated corrosion (FAC) of pipeline elements with a single-phase medium of the second circuit of nuclear power plants (NPPs). The neural network model has been implemented in the Delphi Integrated Development Environment. The neural network consists of an input layer containing seven elements and an output layer with two elements. As the input variables of the neural network, the parameters that have the greatest influence on FAC process are chosen. These are the medium temperature, the pipeline internal diameter, the oxygen content in the medium, the coolant flow velocity, the hydrogen index, the time of monitoring (or the start of operation), and the time for which the prediction is performed. For each of the network input parameters, intervals of possible values were chosen. At that, the factors that affect FAC rate, but not included in the feasible model (chromium, copper and molybdenum content in the pipeline material, amine type) are assumed to be permanent. As the output parameters of the neural network, FAC rate and the variation of the pipeline element wall thickness within the predicted time interval have been selected. As the activation function of the neural network the sigmoid function is used. As a method of training the neural network, the error back propagation method has been chosen, which assumes both a forward and reverse passage through the network layers. As the learning algorithm of the neural network, the one with a teacher has been chosen. As a test sample for the neural network, it is proposed, along with operational control data, to use the results of calculations based on a statistical model created in the framework of a special calculation-experimental method. The application of the developed methodology makes it possible to improve the prediction accuracy of FAC rate without determining all the dependencies between the many factors that influence FAC process. The low errors of the constructed models make it possible to use the results of calculations both to determine the resource characteristics of NPP pipelines and optimize operational control. Pipeline (software) ; Corrosion ; Computer science ; Flow (mathematics) ; Environmental science ; Petroleum engineering ; Engineering ; Materials science ; Mechanics ; Metallurgy ; Physics ; Operating system Material Properties and Failure Mechanisms ; Environmental and Industrial Safety ; Engineering Diagnostics and Reliability
Authors: Meimei Liu | Yu Zexin | Chaoyue Chen | Liao Hanlin | Sihao Deng
Source: Thermal spray (article, 2018-05-07)
Field: Materials Science — Materials Chemistry

Analysis and Optimization of the HVOF Process by Artificial Neural Networks Model Abstract In the High Velocity Oxygen Fuel (HVOF) technology, the coating properties are sensitive to the behaviors of in-flight particles, which are mainly influenced by the processing parameters. However, due to the complex chemical and thermodynamic reactions, the real-time optimization of the coating properties during the HVOF process is still a challenging issue. This study focused on establishing an Artificial Neural Networks (ANN) model to analyze the influence of the processing parameters on the characteristics of in-flight particles. Hydroxyapatite (HA) powders were selected to deposit onto the stainless steel substrates via an improved HVOF spraying system. Combined with an Accuraspray-g3 system applied to acquire the temperature and velocity of inflight HA particles, the artificial neural network algorithm was well trained to predict the velocity and temperature of in-flight particles. The relationship between the variations of the operating parameters (gas flow rates and fuel-to-oxygen ratio) and the behaviors of in-flight HA particles was investigated, which therefor contributes to analyzing and optimizing the mechanical performance and crystallinity of the HA coatings. Thermal spraying ; Crystallinity ; Materials science ; Artificial neural network ; Coating ; Process (computing) ; Oxygen ; Mechanical engineering ; Process engineering ; Chemical engineering ; Biological system ; Composite material ; Computer science ; Engineering ; Artificial intelligence ; Chemistry Catalytic Processes in Materials Science ; High-Temperature Coating Behaviors
Authors: Liangtao Ma | Haifang Dong
Source: IOP Conference Series Earth and Environmental Science (article, 2018-07-01)
Field: Materials Science — Metals and Alloys

Prediction of Corrosion Rate of Q235 Steel under the Marine Environment A generalized regression neural network is used to predict the corrosion rate of metals in marine environment. The environmental temperature, oxygen content, pH value, salinity and potential are taken as input, and the corrosion rate is taken as output. A3 steel was selected to test the 25 sets of data, 18 groups of data were selected for training, and 7 sets of data were used as the verification. The results show that the generalized regression neural network prediction, select the default S value, the average prediction error is 5.72%, higher than the BP neural network was used to predict the 6.56%, using cross validation method to select the optimal S value, the optimal value of S under the average prediction error of the forecast is 2.38%. It shows that the prediction of corrosion rate of metal materials in marine environment by generalized regression neural network is feasible in technology, and has high prediction accuracy and application value. Artificial neural network ; Corrosion ; Regression ; Regression analysis ; Mean squared prediction error ; Value (mathematics) ; Linear regression ; Predictive modelling ; Data mining ; Computer science ; Statistics ; Environmental science ; Mathematics ; Artificial intelligence ; Metallurgy ; Materials science Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: I.I. Novoselov | A. V. Yanilkin | Alexander V. Shapeev | Evgeny V. Podryabinkin
Source: arXiv (Cornell University) (preprint, 2018-12-07)
Field: Materials Science — Materials Chemistry

Moment tensor Potentials as a Promising Tool to Study Diffusion Processes A recently proposed class of machine-learning interatomic potentials --- Moment tensor potentials (MTPs) --- is investigated in this work. MTPs are able to actively select configurations and parametrize the potential on-the-fly. It is shown that MTPs accurately reproduce energies, forces and stresses calculated ab initio. As a more comprehensive test, MTPs are employed to calculate vacancy diffusion rates in Al, Mo and Si. We demonstrate that the results are in a good agreement with ab initio data for the materials considered. Tensor (intrinsic definition) ; Ab initio ; Moment (physics) ; Work (physics) ; Moment tensor ; Ab initio quantum chemistry methods ; Diffusion ; Statistical physics ; Physics ; Materials science ; Thermodynamics ; Classical mechanics ; Mathematics ; Magnitude (astronomy) ; Quantum mechanics ; Molecule ; Geometry Microstructure and mechanical properties ; Machine Learning in Materials Science ; Semiconductor materials and interfaces
Authors: Pietro Cantù | Chiara Catarisano | Nicoletta Corneo | Emma Litterio | Benedetta Triulzi | Matteo Patelmo | Alessandro Dundulachi | V. Mantovani
Source:  (article, 2018-03-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Characterization of metal line-width variation in via first dual-damascene approach and its modeling using machine learning artificial neural network algorithms Line patterning, in via first dual damascene approach, is conditioned by vias density: bottom anti–reflective coating (BARC), used to minimize thin film interference effects by reducing reflected light, and photoresist reflow into vias, leading to materials thickness variation, and so to unwanted modification of metal lines critical dimension (CD), due to local reflectivity change and to swing effect. Aim of the work is to assess CD variations to be expected at device level when applying via first integration scheme, in order to compensate them, where and when feasible, or to setup restrictions to vias density at design level, forbidding critical configurations that might lead to patterning failures. The paper presents an experimental characterization of metal line CD variation as a function of vias density based on the study of a test pattern, designed to explore a wide variety of vias and metals respective configurations, and investigates different approaches to model and predict CD deviations from expected targets. Vias densities, or their convolution with specific kernels, are extracted using conventional design rule check (DRC) tools, and are used as predictors to model metal lines CD variation behavior. Simple via density computation is not able to capture the effect, so we propose a flow, based on machine learning artificial neural network algorithms, able to predict metal line width variations to be expected on product devices as a function of the vias pattern underneath. Artificial neural network ; Materials science ; Back end of line ; Algorithm ; Electronic engineering ; Chemical-mechanical planarization ; Copper interconnect ; Critical dimension ; Convolution (computer science) ; Photoresist ; Wafer ; Computer science ; Process variation ; Optoelectronics ; Artificial intelligence ; Nanotechnology ; Engineering ; Optics ; Physics Copper Interconnects and Reliability ; Semiconductor materials and devices ; Advancements in Photolithography Techniques
Authors: Konstantin Gubaev | Evgeny V. Podryabinkin | Gus L. W. Hart | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2018-06-27)
Field: Materials Science — Materials Chemistry

Accelerating high-throughput searches for new alloys with active\n learning of interatomic potentials We propose an approach to materials prediction that uses a machine-learning\ninteratomic potential to approximate quantum-mechanical energies and an active\nlearning algorithm for the automatic selection of an optimal training dataset.\nOur approach significantly reduces the amount of DFT calculations needed,\nresorting to DFT only to produce the training data, while structural\noptimization is performed using the interatomic potentials. Our approach is not\nlimited to one (or a small number of) lattice types (as is the case for cluster\nexpansion, for example) and can predict structures with lattice types not\npresent in the training dataset. We demonstrate the effectiveness of our\nalgorithm by predicting the convex hull for the following three systems: Cu-Pd,\nCo-Nb-V, and Al-Ni-Ti. Our method is three to four orders of magnitude faster\nthan conventional high-throughput DFT calculations and explores a wider range\nof materials space. In all three systems, we found unreported stable structures\ncompared to the AFLOW database. Because our method is much cheaper and explores\nmuch more of materials space than high-throughput methods or cluster expansion,\nand because our interatomic potentials have a systematically improvable\naccuracy compared to empirical potentials such as EAM, etc., it will have a\nsignificant impact in the discovery of new alloy phases, particularly those\nwith three or more components.\n Interatomic potential ; Convex hull ; Cluster (spacecraft) ; Lattice (music) ; Computer science ; Throughput ; Cluster expansion ; Space (punctuation) ; Algorithm ; Statistical physics ; Materials science ; Computational science ; Regular polygon ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Molecular dynamics ; Thermodynamics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques