jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonathon N. Baker | Preston C. Bowes | Joshua S. Harris | Douglas L. Irving
Source: MRS Communications (preprint, 2019-09-01)
Field: Materials Science — Materials Chemistry

An informatics software stack for point defect-derived opto-electronic properties: the Asphalt Project Materials informatics ; Informatics ; Computer science ; Suite ; Property (philosophy) ; Stack (abstract data type) ; Metric (unit) ; Point (geometry) ; Open research ; Throughput ; Systems engineering ; Distributed computing ; Engineering ; Health informatics ; Engineering informatics ; Electrical engineering ; Wireless ; World Wide Web ; Programming language Machine Learning in Materials Science
Authors: Abbas Farmany
Source: Eurasian Chemical Communications (article, 2019-05-01)
Field: Chemistry — Analytical Chemistry

Informatics aided QSRR study of retention index of some volatile compounds In the present work, an artificial neural network (ANN) model was used to study the quantitative structure retention relationship (QSRR) of retention index (RI) of some volatile compounds in natural cocoa and conched chocolate powder. Molecular structural descriptors are selected using genetic algorithm to construct the nonlinear QSRR models, kernel partial least squares PLS (KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN) were employed. Artificial neural network ; Partial least squares regression ; Kernel (algebra) ; Kovats retention index ; Artificial intelligence ; Molecular descriptor ; Levenberg–Marquardt algorithm ; Computer science ; Chromatography ; Quantitative structure–activity relationship ; Chemistry ; Machine learning ; Mathematics ; Gas chromatography Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Ludwig Gärtner
Source: mediaTUM  (Technical University of Munich) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Modeling and Automatic Tuning in High Performance Computing This literature research lays the groundwork for integrating a machine learning based algorithm selector into the molecular dynamic simulation framework AutoPas. Two refer- ence projects were consulted to analyse the performance of using machine learning models for automatic algorithm selection One is using supervised learning, the other reinforcement learning. The results though show no clear favorit among the two, as there are some key performance measurements missing for a final comparison. Computer science ; Reinforcement learning ; Machine learning ; Artificial intelligence ; Key (lock) ; Selection (genetic algorithm) Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jonathon N. Baker | Preston C. Bowes | Joshua S. Harris | Douglas L. Irving
Source: arXiv (Cornell University) (preprint, 2019-06-06)
Field: Materials Science — Materials Chemistry

An Informatics Software Stack for Point Defect-Derived Opto-Electronic\n Properties: The Asphalt Project Computational acceleration of performance-metric-based materials discovery\nvia high-throughput screening and machine learning methods is becoming\nwidespread. Nevertheless, development and optimization of the opto-electronic\nproperties that depend on dilute concentrations of point defects in new\nmaterials have not significantly benefited from these advances. Here, we\npresent an informatics and simulation suite to computationally accelerate these\nprocesses. This will enable faster and more fundamental materials research, and\nreduce the cost and time associated with the materials development cycle.\nAnalogous to the new avenues enabled by current first-principles-based property\ndatabases, this type of framework will open entire new research frontiers as it\nproliferates.\n Computer science ; Suite ; Materials informatics ; Informatics ; Stack (abstract data type) ; Property (philosophy) ; Metric (unit) ; Point (geometry) ; Open research ; Software ; Throughput ; Distributed computing ; Computer engineering ; Health informatics ; Engineering ; Programming language ; World Wide Web ; Electrical engineering ; Engineering informatics ; Wireless Machine Learning in Materials Science
Authors: David McDonagh | Chris‐Kriton Skylaris | Graeme M. Day
Source: ChemRxiv (preprint, 2019-01-14)
Field: Materials Science — Materials Chemistry

Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevents a reliable assessment of the relative thermodynamic stability of potential structures. Here we present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach, and predicting these corrections with machine learning. We find corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve towards experimentally determined values and more comprehensive energy models when using MP2 corrections, despite remaining at the force field geometry. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results with as little as 10-20% of the training data, reducing the cost of the energy correction by up to an order of magnitude. The machine learning approach opens up the possibility of using fragment-based methods to a greater degree in crystal structure prediction, providing alternative energy models where standard approaches are insufficient. Crystal structure prediction ; Force field (fiction) ; Maxima and minima ; Lattice (music) ; Intermolecular force ; Crystal structure ; Lattice energy ; Statistical physics ; Atom (system on chip) ; Gaussian ; Physics ; Crystal (programming language) ; Molecule ; Computer science ; Algorithm ; Chemistry ; Mathematics ; Quantum mechanics ; Crystallography ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Gayatri Viswanathan | Anton O. Oliynyk | Erin Antono | Julia Ling | Bryce Meredig | Jakoah Brgoch
Source: ChemRxiv (preprint, 2019-01-14)
Field: Materials Science — Materials Chemistry

Single Crystal Automated Refinement (SCAR): A Data-Driven Method for Solving Inorganic Structures &lt;p&gt;Single crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure solution and refinement is not always straightforward. Methods to simplify and rationalize the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure solution. In this work, we propose a new method that uses single crystal data to solve the crystal structures of inorganic, extended solids called “Single Crystal Automated Refinement (&lt;i&gt;SCAR&lt;/i&gt;).” The approach was developed using data mining and machine-learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for &lt;i&gt;SCAR&lt;/i&gt; is presented followed by the implementation of &lt;i&gt;SCAR&lt;/i&gt; to solve two newly synthesized and previously unreported phases, ZrAu&lt;sub&gt;0.5&lt;/sub&gt;Os&lt;sub&gt;0.5&lt;/sub&gt; and Nd&lt;sub&gt;4&lt;/sub&gt;Mn&lt;sub&gt;2&lt;/sub&gt;AuGe&lt;sub&gt;4&lt;/sub&gt;. The structure solutions are found to be comparable with manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed &lt;i&gt;SCAR&lt;/i&gt; program is thusly verified to be a fast and reliable assistant in solving even complex single crystal diffraction data for extended inorganic solids.&lt;/p&gt; Crystal (programming language) ; Crystal structure ; Crystallography ; Algorithm ; Single crystal ; Chemistry ; Materials science ; Computer science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Tristan Hascoet | Xuejiao Deng | Kiyoto Tai | Yuji Adachi | Sachiko Nakamura | Tomoko Hayashi | Mari Sugiyama | Yasuo Ariki | Tetsuya Takiguchi
Source:  (preprint, 2019-10-01)
Field: Materials Science — Materials Chemistry

Assisting human experts in the interpretation of their visual process: A case study on assessing copper surface adhesive potency Deep Neural Networks are often though to lack interpretability due to the distributed nature of their internal representations. In contrast, humans can generally justify, in natural language, for their answer to a visual question with simple common sense reasoning. However, human introspection abilities have their own limits as one often struggles to justify for the recognition process behind our lowest level feature recognition ability: for instance, it is difficult to precisely explain why a given texture seems more characteristic of the surface of a finger nail rather than a plastic bottle. In this paper, we showcase an application in which deep learning models can actually help human experts justify for their own low-level visual recognition process: We study the problem of assessing the adhesive potency of copper sheets from microscopic pictures of their surface. Although highly trained material experts are able to qualitatively assess the surface adhesive potency, they are often unable to precisely justify for their decision process. We present a model that, under careful design considerations, is able to provide visual clues for human experts to understand and justify for their own recognition process. Not only can our model assist human experts in their interpretation of the surface characteristics, we show how this model can be used to test different hypothesis of the copper surface response to different manufacturing processes. Interpretability ; Process (computing) ; Artificial intelligence ; Computer science ; Plastic bottle ; Interpretation (philosophy) ; Introspection ; Machine learning ; Human–computer interaction ; Engineering ; Cognitive psychology ; Psychology ; Bottle Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Non-Destructive Testing Techniques
Authors: P.S. Noori Banu | S. Devaki Rani
Source: International Journal of Microstructure and Materials Properties (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Microstructure evaluation and modelling the tensile strength and yield strength of titanium alloys The rationale of the current study was to develop artificial neural network (ANN) models of titanium alloys for predicting tensile strength and yield strength using the alloy composition and processing parameters as the inputs and validate the models through experimental evaluation and correlate with microstructure characteristics. The robustness of the model was tested through experimental evaluation of tensile strength in Ti-6Al-4V, Ti-5.8Al-4Sn-3.5 Zr-0.7Nb-0.5Mo-0.3 Si, Ti-3Al-8V-4Zr-6Cr-4Mo, and Ti-10V-2Fe-3Al alloys. Microstructure characteristics i.e., volume fraction of alpha, alpha grain area and Feret ratio of Ti, Ti-6Al-4V, Ti-6Al-5V, Ti-6Al-6V-2Sn, Ti-8Mn, and Ti-13V-11Cr-3Al correlated inversely with predicted tensile strength and yield strength. This three-tier validation ensures efficient performance of the developed ANN models. Ultimate tensile strength ; Materials science ; Microstructure ; Titanium alloy ; Volume fraction ; Titanium ; Alloy ; Composite material ; Grain size ; Metallurgy ; Robustness (evolution) Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Advanced machining processes and optimization
Authors: Ana Ma Bastida de Miguel
Source: arXiv (Cornell University) (preprint, 2019-05-28)
Field: Materials Science — Materials Chemistry

Towards the computational experiment We give a brief account of the current limitations and possibilities in the field of computational simulation of materials. We then focus on the effect that the emergence of machine learning interatomic potentials is having on the field and how it will affect its evolution in the near future. Focus (optics) ; Field (mathematics) ; Current (fluid) ; Computational simulation ; Computer science ; Affect (linguistics) ; Computational model ; Statistical physics ; Artificial intelligence ; Computational science ; Physics ; Psychology ; Engineering ; Mathematics ; Electrical engineering ; Optics ; Communication Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: David McDonagh | Chris‐Kriton Skylaris | Graeme M. Day
Source: ChemRxiv (preprint, 2019-01-14)
Field: Materials Science — Materials Chemistry

Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevents a reliable assessment of the relative thermodynamic stability of potential structures. Here we present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach, and predicting these corrections with machine learning. We find corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve towards experimentally determined values and more comprehensive energy models when using MP2 corrections, despite remaining at the force field geometry. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results with as little as 10-20% of the training data, reducing the cost of the energy correction by up to an order of magnitude. The machine learning approach opens up the possibility of using fragment-based methods to a greater degree in crystal structure prediction, providing alternative energy models where standard approaches are insufficient. Crystal structure prediction ; Force field (fiction) ; Maxima and minima ; Lattice (music) ; Crystal structure ; Intermolecular force ; Statistical physics ; Lattice energy ; Gaussian ; Atom (system on chip) ; Physics ; Crystal (programming language) ; Computer science ; Algorithm ; Machine learning ; Molecule ; Artificial intelligence ; Mathematics ; Chemistry ; Quantum mechanics ; Crystallography ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: A. Yousefi | Kamyar Movagharnejad
Source: SHILAP Revista de lepidopterología (article, 2019-03-01)
Field: Materials Science — Materials Chemistry

Prediction of the pharmaceutical solubility in water and organic solvents via different soft computing models Solubility data of solid in aqueous and different organic solvents are very important physicochemical properties considered in the design of the industrial processes and the theoretical studies. In this study, experimental solubility data of 666 pharmaceutical compounds in water and 712 pharmaceutical compounds in organic solvents were collected from different sources. Three different artificial neural networks including multilayer perceptron, radial basis function and support vector machine were constructed to predict the solubility of these different pharmaceutical compounds in water and different solvents. Molecular weight, melting point, temperature and the number of each functional group in the pharmaceutical compound and organic solvents were selected as the input variables of these three different neural network models. The neural network predictions were compared with the experimental data and the SVR-PSO model with the Average Absolute Relative Deviation equal to 0.0166 for the solubility in water and 0.0707 for solubility in organic compounds was selected as the most accurate model. Solubility ; Artificial neural network ; Multilayer perceptron ; Melting point ; Chemistry ; Aqueous solution ; Thermodynamics ; Biological system ; Organic chemistry ; Materials science ; Computer science ; Artificial intelligence Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals
Authors: V. Pitchi Raju
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2019-09-09)
Field: Materials Science — Materials Chemistry

ANN Model Based Calculation of Tensile of Friction Surfaced Tool Steel Friction surface treatment is well established solid technology and is used for deposition, abrasion and corrosion protection coatings on rigid materials. This novel process has wide range of industrial applications, particularly in the field of reclamation and repair of damaged and worn engineering components. In this paper, present the prediction of tensile of friction surface treated tool steel using ANN for simulated results of friction surface treatment. This experiment was carried out to obtain tool steel coatings of low carbon steel parts by changing input process parameters such as friction pressure, rotational speed and welding speed. The simulation is performed by a 33 factor design that takes into account the maximum and minimum limits of the experimental work performed by the 23 factor design. Neural network structures, such as the Feed Forward Neural Network FFNN , were used to predict tensile tool steel sediments caused by friction. V. Pitchi Raju "ANN Model Based Calculation of Tensile of Friction Surfaced Tool Steel" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29169.pdf Ultimate tensile strength ; Materials science ; Structural engineering ; Metallurgy ; Forensic engineering ; Composite material ; Engineering Metallurgy and Material Science ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Jonathan Vandermause | Steven B. Torrisi | Simon Batzner | Alexie M. Kolpak | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2019-04-03)
Field: Materials Science — Materials Chemistry

On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events Machine learning based interatomic potentials currently require manual construction of training sets consisting of thousands of first principles calculations and are often restricted to single-component and nonreactive systems. This severely limits the practical application of these models due to both low training efficiency and limited accuracy in treating important rare events such as reactions and diffusion. We present an adaptive Bayesian inference method for automating and accelerating the on-the-fly construction of accurate interatomic force fields using structures drawn from molecular dynamics simulations. Within an online active learning algorithm, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-component systems and shown to achieve state-of-the-art accuracy with minimal ab initio data. Computer science ; Inference ; Bayesian probability ; Component (thermodynamics) ; Gaussian process ; Bayesian inference ; Kriging ; Machine learning ; Artificial intelligence ; Gaussian ; Process (computing) ; On the fly ; Set (abstract data type) ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Mass Spectrometry Techniques and Applications
Authors: Alexandre Derville | Sergio Martínez | Guillaume Gey | Julien Baderot | Guilhem Bernard | Xavier Chevalier | J. Foucher
Source:  (article, 2019-03-25)
Field: Materials Science — Materials Chemistry

Accelerate the analysis and optimization of lamellar BCP process using machine learning This paper presents how to use machine learning to accelerate the development of lamellar block copolymer process. The first part introduces an automated algorithm to measure lamellar CD-SEM images oriented to allow fast images screening, identify and select the most relevant chemical composition. The second part is the use of machine learning in the process development which is a data driven approach. This part is also divided in two sections, the first one for the prediction of the process, where having a set of experiments in a process window, a model is created so that the outcome from is estimated from different parameters. The second part is the estimation of the process window. These tools are oriented to assist process engineers in the process optimization which must be driven under expert supervision. Process (computing) ; Computer science ; Lamellar structure ; Process window ; Set (abstract data type) ; Machine learning ; Block (permutation group theory) ; Artificial intelligence ; Materials science ; Mathematics Machine Learning in Materials Science
Authors: Matsumoto, Kaname | Horide, Tomoya
Source: Institutional Repositories DataBase (IRDB) (article, 2019-06-28)
Field: Materials Science — Materials Chemistry

An acceleration search method of higher Tc superconductors by a machine learning algorithm We propose a method to efficiently search for superconductors with higher critical temperature Tc by machine learning based on a superconductor database. The Tc prediction and the search for new superconductors are still difficult problems. With the progress of computer power and calculation algorithms, the possibility of finding new materials with higher Tc at high throughput is emerging. Using the obtained Tc prediction model, the scope is expanded to the search space of multielement materials that have never been searched, and candidates for superconductors with higher Tc that can be synthesized are proposed. Superconductivity ; High-temperature superconductivity ; Power (physics) ; Acceleration ; Scope (computer science) ; Throughput Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Physics of Superconductivity and Magnetism