jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anton V. Sinitskiy | Vijay S. Pande
Source: arXiv (Cornell University) (preprint, 2018-09-08)
Field: Materials Science — Materials Chemistry

Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT) Density functional theory (DFT) is one of the main methods in Quantum Chemistry that offers an attractive trade off between the cost and accuracy of quantum chemical computations. The electron density plays a key role in DFT. In this work, we explore whether machine learning - more specifically, deep neural networks (DNNs) - can be trained to predict electron densities faster than DFT. First, we choose a practically efficient combination of a DFT functional and a basis set (PBE0/pcS-3) and use it to generate a database of DFT solutions for more than 133,000 organic molecules from a previously published database QM9. Next, we train a DNN to predict electron densities and energies of such molecules. The only input to the DNN is an approximate electron density computed with a cheap quantum chemical method in a small basis set (HF/cc-VDZ). We demonstrate that the DNN successfully learns differences in the electron densities arising both from electron correlation and small basis set artifacts in the HF computations. All qualitative features in density differences, including local minima on lone pairs, local maxima on nuclei, toroidal shapes around C-H and C-C bonds, complex shapes around aromatic and cyclopropane rings and CN group, etc. are captured by the DNN. Accuracy of energy predictions by the DNN is ~ 1 kcal/mol, on par with other models reported in the literature, while those models do not predict the electron density. Computations with the DNN, including HF computations, take much less time that DFT computations (by a factor of ~20-30 for most QM9 molecules in the current version, and it is clear how it could be further improved). Density functional theory ; Organic molecules ; Set (abstract data type) ; Artificial neural network ; Molecule ; Computational chemistry ; Electron ; Computer science ; Biological system ; Chemical physics ; Chemistry ; Statistical physics ; Materials science ; Physics ; Artificial intelligence ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Long Bian | Dan C. Sorescu | Lucy Chen | David L. White | Seth C. Burkert | Yassin Khalifa | Zhenwei Zhang | Ervin Sejdić | Alexander Star
Source: ACS Applied Materials & Interfaces (article, 2018-12-14)
Field: Chemistry — Electrochemistry

Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors Carbon nanotube-based field-effect transistors (NTFETs) are ideal sensor devices as they provide rich information regarding carbon nanotube interactions with target analytes and have potential for miniaturization in diverse applications in medical, safety, environmental, and energy sectors. Herein, we investigate chemical detection with cross-sensitive NTFETs sensor arrays comprised of metal nanoparticle-decorated single-walled carbon nanotubes (SWCNTs). By combining analysis of NTFET device characteristics with supervised machine-learning algorithms, we have successfully discriminated among five selected purine compounds, adenine, guanine, xanthine, uric acid, and caffeine. Interactions of purine compounds with metal nanoparticle-decorated SWCNTs were corroborated by density functional theory calculations. Furthermore, by testing a variety of prepared as well as commercial solutions with and without caffeine, our approach accurately discerns the presence of caffeine in 95% of the samples with 48 features using a linear discriminant analysis and in 93.4% of the samples with only 11 features when using a support vector machine analysis. We also performed recursive feature elimination and identified three NTFET parameters, transconductance, threshold voltage, and minimum conductance, as the most crucial features to analyte prediction accuracy. Materials science ; Carbon nanotube ; Analyte ; Nanotechnology ; Nanoparticle ; Xanthine ; Field-effect transistor ; Transistor ; Voltage ; Chemistry ; Organic chemistry ; Electrical engineering ; Chromatography Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Ruimin Ma | Dezhao Huang | Teng Zhang | Tengfei Luo
Source: Chemical Physics Letters (article, 2018-05-16)
Field: Materials Science — Materials Chemistry

Determining influential descriptors for polymer chain conformation based on empirical force-fields and molecular dynamics simulations Radius of gyration ; Molecular dynamics ; Dihedral angle ; Chain (unit) ; Force field (fiction) ; Polymer ; Chemistry ; Computational chemistry ; Chemical physics ; Physics ; Molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Steven R. Young | Artem Maksov | Maxim Ziatdinov | Ye Cao | Matthew Burch | Janakiraman Balachandran | Linglong Li | Suhas Somnath | Robert M. Patton | Sergei V. Kalinin | Rama K. Vasudevan
Source: Journal of Applied Physics (article, 2018-03-20)
Field: Materials Science — Materials Chemistry

Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides The pursuit of more advanced electronics, and finding solutions to energy needs often hinges upon the discovery and optimization of new functional materials. However, the discovery rate of these materials is alarmingly low. Much of the information that could drive this rate higher is scattered across tens of thousands of papers in the extant literature published over several decades but is not in an indexed form, and cannot be used in entirety without substantial effort. Many of these limitations can be circumvented if the experimentalist has access to systematized collections of prior experimental procedures and results. Here, we investigate the property-processing relationship during growth of oxide films by pulsed laser deposition. To do so, we develop an enabling software tool to (1) mine the literature of relevant papers for synthesis parameters and functional properties of previously studied materials, (2) enhance the accuracy of this mining through crowd sourcing approaches, (3) create a searchable repository that will be a community-wide resource enabling material scientists to leverage this information, and (4) provide through the Jupyter notebook platform, simple machine-learning-based analysis to learn the complex interactions between growth parameters and functional properties (all data/codes available on https://github.com/ORNL-DataMatls). The results allow visualization of growth windows, trends and outliers, which can serve as a template for analyzing the distribution of growth conditions, provide starting points for related compounds and act as a feedback for first-principles calculations. Such tools will comprise an integral part of the materials design schema in the coming decade. Leverage (statistics) ; Visualization ; Schema (genetic algorithms) ; Extant taxon ; Software ; Categorical variable ; Data visualization ; Resource (disambiguation) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Michael P. Howard | Wesley F. Reinhart | Tanmoy Sanyal | M. Scott Shell | Arash Nikoubashman | Athanassios Z. Panagiotopoulos
Source: The Journal of Chemical Physics (article, 2018-09-04)
Field: Materials Science — Materials Chemistry

Evaporation-induced assembly of colloidal crystals Colloidal crystals are often prepared by evaporation from solution, and there is considerable interest to link the processing conditions to the crystal morphology and quality. Here, we study the evaporation-induced assembly of colloidal crystals using massive-scale nonequilibrium molecular dynamics simulations. We apply a recently developed machine-learning technique to characterize the assembling crystal structures with unprecedented microscopic detail. In agreement with previous experiments and simulations, faster evaporation rates lead to earlier onset of crystallization and more disordered surface structures. Surprisingly, we find that collective rearrangements of the bulk crystal during later stages of drying reduce the influence of the initial surface structure, and the final morphology is essentially independent of the evaporation rate. Our structural analysis reveals that the crystallization process is well-described by two time scales, the film drying time and the crystal growth time, with the latter having an unexpected dependence on the evaporation rate due to equilibrium thermodynamic effects at high colloid concentrations. These two time scales may be leveraged to control the relative influence of equilibrium and nonequilibrium growth mechanisms, suggesting a route to rapidly process colloidal crystals while also removing defects. Our analysis additionally reveals that solvent-mediated interactions play a critical role in the crystallization kinetics and that commonly used implicit-solvent models do not faithfully resolve nonequilibrium processes such as drying. Crystallization ; Evaporation ; Non-equilibrium thermodynamics ; Chemical physics ; Crystal (programming language) ; Colloid ; Crystal growth ; Colloidal crystal ; Materials science ; Thermodynamics ; Nanotechnology ; Chemistry ; Crystallography ; Physical chemistry ; Physics Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; nanoparticles nucleation surface interactions
Authors: Chen Wang | Akshay Tharval | John R. Kitchin
Source: Molecular Simulation (article, 2018-01-03)
Field: Materials Science — Materials Chemistry

A density functional theory parameterised neural network model of zirconia We have developed a Behler–Parrinello Neural Network (BPNN) that can be employed to calculate energies and forces of zirconia bulk structures with oxygen vacancies with similar accuracy as that of the density functional theory (DFT) calculations that were used to train the BPNN. In this work, we have trained the BPNN potential with a reference set of 2178 DFT calculations and validated it against a dataset of untrained data. We have shown that the bulk structural parameters, equation of states, oxygen vacancy formation energies and diffusion barriers predicted by the BPNN potential are in good agreement with the reference DFT data. The transferability of the BPNN potential has also been benchmarked with the prediction of structures that were not included in the reference set. The evaluation time of the BPNN is orders of magnitude less than corresponding DFT calculations, although the training process of the BPNN potential requires non-negligible amount of computational resources to prepare the dataset. The computational efficiency of the NN enabled it to be used in molecular dynamics simulations of the temperature-dependent diffusion of an oxygen vacancy and the corresponding diffusion activation energy. Density functional theory ; Transferability ; Work (physics) ; Diffusion ; Molecular dynamics ; Artificial neural network ; Vacancy defect ; Basis set ; Chemistry ; Statistical physics ; Computational chemistry ; Materials science ; Biological system ; Thermodynamics ; Computer science ; Artificial intelligence ; Machine learning ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties
Authors: Ankit Mishra | Sungwook Hong | Pankaj Rajak | Chunyang Sheng | Ken‐ichi Nomura | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: npj Computational Materials (article, 2018-07-27)
Field: Materials Science — Materials Chemistry

Multiobjective genetic training and uncertainty quantification of reactive force fields Abstract The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes. ReaxFF parameters are commonly trained to fit a predefined set of quantum-mechanical data, but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions. Here, we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest. ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly, where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification. Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications. The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO 3 by H 2 S precursor, which is an essential reaction step for chemical vapor deposition synthesis of MoS 2 layers. Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes, which quantitatively reproduces quantum molecular dynamics simulations while providing error bars. ReaxFF ; Molecular dynamics ; Computer science ; Force field (fiction) ; Workflow ; Biological system ; Genetic algorithm ; Materials science ; Chemistry ; Computational chemistry ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Semiconductor materials and devices
Authors: Pietro Romano | Giuseppe Lo Presti | Antonino Imburgia | R. Candela
Source: IEEE Electrical Insulation Magazine (article, 2018-07-01)
Field: Materials Science — Materials Chemistry

A new approach to partial discharge detection under DC voltage The continuing development of HVDC power transmission systems presents many problems related to evaluation of the reliability of power system assets [1]-[5]. In this context the identification of insulation defects plays a key role in preventing unexpected failures of electrical components. Partial discharge (PD) measurement is a useful approach to assessing the condition of HV power apparatus and cables. Such measurements are also widely employed for HVAC systems. The inception mechanisms of PD in AC systems are well-known, and measurements are usually performed following the IEC 60270 standard [6]. PD measurements under DC voltage present complexities related to the nature of the phenomenon and the supply conditions [7]. In AC systems phase-resolved-partial-discharge (PRPD) patterns allow assessment of the insulation condition, and provide information on the types of defect present [8]. Such analysis cannot be performed under DC voltage, since each discharge event cannot be related to a phase value. The interpretation of the acquired data therefore requires a different approach. Partial discharge ; Reliability (semiconductor) ; Context (archaeology) ; Voltage ; Electric power system ; Reliability engineering ; Electrical engineering ; High voltage ; Computer science ; Electronic engineering ; Power (physics) ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Aimin Miao | Jiajun Zhuang | Yu Tang | Yong He | Xuan Chu | Shaoming Luo
Source: Sensors (article, 2018-12-11)
Field: Chemistry — Analytical Chemistry

Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7⁻1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher's discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis. Hyperspectral imaging ; Principal component analysis ; Pattern recognition (psychology) ; Procrustes analysis ; Linear discriminant analysis ; Kernel principal component analysis ; Artificial intelligence ; Kernel (algebra) ; Mathematics ; Embedding ; Computer science ; Kernel method ; Support vector machine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Shalv Amit Parekh | Rolf David | Kranthi K. R. Bannuru | Lakshminarasimhan Krishnaswamy | Avinash Baji
Source: Membranes (article, 2018-08-08)
Field: Chemistry — Organic Chemistry

Electrospun Silver Coated Polyacrylonitrile Membranes for Water Filtration Applications The scarcity of drinking water and the contamination of water sources in underdeveloped countries are serious problems that require immediate low-tech and low-cost solutions. In this study, we fabricated polyacrylonitrile (PAN) porous membranes coated with silver nanoparticles (AgNP) and demonstrated their use for water filtration and water treatment applications. The membranes were prepared by electrospinning a PAN solution and treating in a hydroxylamine (NH2OH) aqueous solution to form –C(NH2)N–OH groups that were used for functionalization (Ag+ ions) of the membrane. The coordinated silver ions were then converted to silver nanoparticles. The microstructure of the membrane, water permeability, antimicrobial effect (using Escherichia coli), and particulate filtration capabilities were studied. This study verified that the membrane demonstrated a 100% reduction for Gram-negative bacteria with an effective filtration rate of 8.0 mL/cm2 min. Furthermore, the membrane was able to eliminate 60% of latex beads as small as 50 nm and over 80% of the 2 µm beads via gravity filtration. This study demonstrated that PAN–AgNP membranes can be employed as antimicrobial membranes for the filtration of water in underdeveloped countries. Polyacrylonitrile ; Filtration (mathematics) ; Membrane ; Materials science ; Chemical engineering ; Polymer chemistry ; Composite material ; Polymer science ; Chemistry ; Polymer ; Engineering ; Mathematics Nanomaterials for catalytic reactions ; Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies
Authors: Yuemin Wang | Christof Wöll
Source: Catalysis Letters (article, 2018-06-02)
Field: Chemistry — Inorganic Chemistry

Chemical Reactions at Isolated Single-Sites Inside Metal–Organic Frameworks Isolated, coordinatively unsaturated metal sites within metal–organic framework (MOF) materials feature interesting chemical properties and offer applications as single-site catalysts. Here, we report on the recent progress in providing fundamental insight into chemical reactions occurring inside MOFs. In addition to the common form, powders, we discuss the potential of MOF thin films (SURMOFs). The combined spectroscopic and modeling approach applied to selected systems demonstrated that the catalytic activity of MOFs can be precisely tuned. A particular interesting case is the so-called defect-engineering, where structural imperfections are created in a controlled fashion. The chemical properties of MOF materials can be further modified by integration and decoration of linkers or loading with guest species, such as metal or metal–oxide nanoparticles or nanoclusters. A particularly interesting aspect of layer-by-layer approaches for the fabrication of MOF thin films is the prospect to realize tandem catalysts. Nanoclusters ; Catalysis ; Nanotechnology ; Metal-organic framework ; Chemistry ; Fabrication ; Oxide ; Layer (electronics) ; Metal ; Heterogeneous catalysis ; Organometallic chemistry ; Materials science ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Andrea Anelli | Edgar A. Engel | Chris J. Pickard | Michele Ceriotti
Source: Physical Review Materials (article, 2018-10-22)
Field: Materials Science — Materials Chemistry

Generalized convex hull construction for materials discovery Searching for novel materials involves identifying potential candidates and selecting those that have desirable properties and facile synthesis. It is relatively easy to generate large numbers of potential candidates, for instance, by computational searches or elemental substitution. The identification of synthesizable compounds, however, is a needle-in-a-haystack problem. Conventionally, the screening is based on a convex hull construction, which identifies structures stabilized by a particular thermodynamic constraint, such as pressure, chosen based on prior experimental evidence or intuition. We introduce a generalized convex hull framework that instead relies on data-driven coordinates, and represents the full structural diversity of the candidate compounds in an unbiased way. Its probabilistic construction addresses the inevitable uncertainty in input structure data and provides a superior measure of stability compared to the input (free) energies, that can, for instance, also be used to assist experimental crystal structure determination. It efficiently identifies candidates with high probabilities of being synthesizable and suggests the relevant experimentally realizable constraints, thereby providing a much needed starting point for the determination of viable synthetic pathways. Convex hull ; Hull ; Probabilistic logic ; Regular polygon ; Identification (biology) ; Point (geometry) ; Measure (data warehouse) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions