jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dezső L. Beke | Lajos Daróczi | László Z. Tóth | M.K. Bolgár | Nora Mohareb Samy | Anikó Hudák
Source: Metals (article, 2019-01-09)
Field: Materials Science — Materials Chemistry

Acoustic Emissions during Structural Changes in Shape Memory Alloys Structural changes (martensitic transformation, rearrangements of martensitic variants) in shape memory alloys have an intermittent character that is accompanied by the emission of different (thermal, acoustic, and magnetic) noises, which are fingerprints of the driven criticality, resulting in a damped power-law behaviour. We will illustrate what kinds of important information can be obtained on the structural changes in shape memory alloys. It was established that the power exponents of distributions of acoustic emission (AE) parameters (energy, amplitude, etc.), belonging to martensitic transformations, show quite a universal character and depend only on the symmetry of the martensite. However, we have shown that the asymmetry of the transformation (the exponents are different for the forward and reverse transformations) results in as large differences as those due to the martensite symmetry. We will also demonstrate how the recently introduced AE clustering method can help to identify the different contributions responsible for the asymmetry. The usefulness of the investigations of time correlations between the subsequent events and correlations between acoustic and magnetic noise events in ferromagnetic shape memory alloys will be demonstrated too. Finally, examples of acoustic and magnetic emissions during variant rearrangements (superplastic or superelastic behaviour) in the martensitic state will be described. Shape-memory alloy ; Asymmetry ; Acoustic emission ; Martensite ; Diffusionless transformation ; Symmetry (geometry) ; Materials science ; Magnetic shape-memory alloy ; Ferromagnetism ; Condensed matter physics ; Transformation (genetics) ; Character (mathematics) ; Statistical physics ; Physics ; Magnetic field ; Magnetic domain ; Magnetization ; Metallurgy ; Mathematics ; Microstructure Shape Memory Alloy Transformations
Authors: Thomas C. Draper | Claire Fullarton | Richard Mayne | Neil Phillips | Giacomo Canciani | Ben de Lacy Costello | Andrew Adamatzky
Source: Soft Matter (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Mapping outcomes of liquid marble collisions Liquid marbles (LMs) have many promising roles in the ongoing development of microfluidics, microreactors, bioreactors, and unconventional computing. In many of these applications, the coalescence of two LMs is either required or actively discouraged, therefore it is important to study liquid marble collisions and establish parameters which enable the desired collision outcome. Recent reports on LM coalescence have focused on either two mobile LMs colliding, or an accelerating LM hitting a sessile LM with a backstop. A further possible scenario is the impact of a mobile LM against a non-supported static LM. This paper investigates such a collision, using high-speed videography for single-frame analysis. Multiple collisions were undertaken whilst varying the modified Weber number (We*) and offset ratios (X*). Parameter ranges of 1.0 < We* < 1.4 and 0.0 < X* < 0.1, resulted in a coalescence rate of approximately 50%. Whereas, parameter ranges X* > 0.25, and We* < 0.95 or We* > 1.55 resulted in 100% non-coalescence. Additionally, observations of LMs moving above a threshold velocity of 0.6 m s-1 have revealed a new and unusual deformation. Comparisons of the outcome of collisions whilst varying both the LM volume and the powder grain size have also been made, revealing a strong link. The results of this work provide a deeper understanding of LM coalescence, allowing improved control when designing future collision experiments. Offset (computer science) ; Collision ; Physics ; Computer science ; Computer security Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Micro and Nano Robotics
Authors: Federica Valentini
Source: Sensors (review, 2019-10-04)
Field: Materials Science — Materials Chemistry

Smart Electrochemical Portable Tools for Cultural Heritage Analysis: A Review . Protecting Cultural Heritage (CH) from corrosion and other environmental damages, mainly involving metallic or organic layers contained in artwork, represents a major challenge for conservation scientists. Electrochemical techniques provide useful information about the deterioration effects of metallic coatings and organic layers. Recently, Electrochemical Impedance Spectroscopy (EIS) has been successfully applied in the study of metallic corrosion. However, EIS has not succeeded in becoming a routine technique, due to problems regarding both instrumental apparatus (which is not ideal for in situ analysis, especially with previous cell configurations), and the difficulty with data processing. At the same time, new portable electrochemical sensors, immunosensors, and biosensors have successfully made a scientific impact, mainly with in situ diagnosis of organic components contained in CH objects. For this purpose, this review presents two sections: the first describes the analytical optimization of impedance electrochemical cell geometries that are suitable for in situ metal-coating investigation; the second reports on the assembly of small electrochemical sensors, immunosensors, and biosensors, which useful for in situ organic layer characterization. This overview summarizes the state of the art regarding the application of electrochemical techniques and small electrochemical devices as alternative tools for the understanding of CH. Dielectric spectroscopy ; Conservation ; Nanotechnology ; Corrosion ; Materials science ; Characterization (materials science) ; Coating ; Electrochemistry ; Cultural heritage ; Galvanic cell ; Computer science ; Process engineering ; Biochemical engineering ; Electrode ; Engineering ; Environmental science ; Chemistry ; Metallurgy Corrosion Behavior and Inhibition ; Electrochemical Analysis and Applications ; Concrete Corrosion and Durability
Authors: Soheila Asadi | Farid Moeinpour
Source: Applied Water Science (article, 2019-01-29)
Field: Chemistry — Organic Chemistry

Inactivation of Escherichia coli in water by silver-coated Ni0.5Zn0.5Fe2O4 magnetic nanocomposite: a Box–Behnken design optimization The present research studied the antibacterial effect of silver-coated Ni0.5Zn0.5Fe2O4 magnetic nanoparticles on Gram-negative bacteria Escherichia coli (E. coli) from water. The effects of pH (6, 7 and 9), disinfectant dose (2, 5 and 10 g/L) and contact time (10, 20 and 30 min) have been also investigated. To obtain important factors, the interactions between factors and optimal experimental design in surface response method were used based on Box–Behnken design. According to the research findings, the system is efficient in eliminating E. coli. The results showed that E. coli elimination efficiency intensified through increasing the amount of nanoparticles from 2 to 10 g/L. The results also demonstrated no significant change in E. coli elimination through pH increasing of 6 to 9. Expanding contact time from 10 to 30 min also heightened E. coli elimination rate. R2 for E. coli elimination is 0.9994 indicating a good agreement between model experimental data and forecasting data. Box–Behnken design ; Escherichia coli ; Response surface methodology ; Industrial and production engineering ; Disinfectant ; Water disinfection ; Nanocomposite ; Chemistry ; Silver nanoparticle ; Bacteria ; Magnetic nanoparticles ; Nanoparticle ; Microbiology ; Nuclear chemistry ; Materials science ; Chromatography ; Nanotechnology ; Environmental engineering ; Biology ; Biochemistry ; Organic chemistry Nanomaterials for catalytic reactions ; Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications
Authors: Kristof T. Schütt | Michael Gastegger | Alexandre Tkatchenko | Kilian Müller | Reinhard J. Maurer
Source: arXiv (Cornell University) (preprint, 2019-06-24)
Field: Materials Science — Materials Chemistry

Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. Wave function ; Quantum chemistry ; Differentiable function ; Electronic structure ; Atomic orbital ; Quantum ; Computer science ; Representation (politics) ; Statistical physics ; Chemical space ; Chemistry ; Quantum mechanics ; Molecule ; Physics ; Mathematics ; Drug discovery ; Electron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Adarsh Dave | Jared Mitchell | Kirthevasan Kandasamy | Sven Burke | Biswajit Paria | Barnabás Póczos | Jay Whitacre | Venkatasubramanian Viswanathan
Source: ChemRxiv (preprint, 2019-11-20)
Field: Materials Science — Materials Chemistry

Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine-Learning &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Innovations in batteries take years to formulate, requiring extensive experimentation during the design and optimization phases. We approach the design of a battery electrolyte as a black-box optimization problem. We report here the discovery of a novel battery electrolyte by a robotic electrolyte experiment guided by machine-learning software. Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries, we utilize Dragonfly - a Bayesian machine-learning software package - to search mixtures of commonly used lithium and sodium salts for super-concentrated aqueous electrolytes with wide electrochemical stability windows. Dragonfly autonomously managed the robotic test-stand, recommending electrolyte designs to test and receiving experimental feed- back in real time. Within 40 hours of continuous experimentation, Dragonfly discovered a novel, high-performing aqueous sodium electrolyte that a human-guided design process may have missed. This result demonstrates the possibility of integrating robotics with machine-learning to rapidly and autonomously discover novel battery materials.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Electrolyte ; Battery (electricity) ; Computer science ; Artificial intelligence ; Robotics ; Black box ; Robot ; Chemistry ; Physics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Advanced Battery Technologies Research
Authors: Mojtaba Haghighatlari | Gaurav Vishwakarma | Doaa Altarawy | R. Subramanian | Bhargava Urala Kota | Aditya Sonpal | Srirangaraj Setlur | Johannes Hachmann
Source: ChemRxiv (preprint, 2019-06-26)
Field: Materials Science — Materials Chemistry

ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data ChemML is an open machine learning and informatics program suite that is designed to support and advance the data-driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute machine learning workflows that are adapted specifically for the chemical and materials context. Key features are automation, general-purpose utility, versatility, and user-friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data-driven in silico research outlined in our recent publication1. Workflow ; Suite ; Computer science ; Data science ; Informatics ; Context (archaeology) ; Domain (mathematical analysis) ; Automation ; Key (lock) ; Software engineering ; Software ; Engineering ; Database ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Seoin Back | Junwoong Yoon | Nianhan Tian | Wen Zhong | Kevin Tran | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2019-05-21)
Field: Materials Science — Materials Chemistry

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis. Voronoi diagram ; Convolutional neural network ; Catalysis ; Polyhedron ; Throughput ; Surface (topology) ; Artificial neural network ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Chemical physics ; Mathematics ; Combinatorics ; Geometry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ankur Baliyan | Hideto Imai
Source: Scientific Reports (article, 2019-12-03)
Field: Chemistry — Analytical Chemistry

Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery Electrodes The intelligence to synchronously identify multiple spectral signatures in a lithium-ion battery electrode (LIB) would facilitate the usage of analytical technique for inline quality control and product development. Here, we present an analytical framework (AF) to automatically identify the existing spectral signatures in the hyperspectral Raman dataset of LIB electrodes. The AF is entirely automated and requires fewer or almost no human assistance. The end-to-end pipeline of AF own the following features; (i) intelligently pre-processing the hyperspectral Raman dataset to eliminate the cosmic noise and baseline, (ii) extract all the reliable spectral signatures from the hyperspectral dataset and assign the class labels, (iii) training a neural network (NN) on to the precisely "labelled" spectral signature, and finally, examined the interoperability/reusability of already trained NN on to the newly measured dataset taken from the same LIB specimen or completely different LIB specimen for inline real-time analytics. Furthermore, we demonstrate that it is possible to quantitatively assess the capacity degradation of LIB via a capacity retention coefficient that can be calculated by comparing the LMO signatures extracted by the analytical framework (AF). The present approach is suited for real-time vibrational spectroscopy based industrial applications; multicomponent chemical reactions, chromatographic, spectroscopic mixtures, and environmental monitoring. Hyperspectral imaging ; Computer science ; Raman spectroscopy ; Electrode ; Spectral signature ; Pipeline (software) ; Pattern recognition (psychology) ; Artificial intelligence ; Convolutional neural network ; Battery (electricity) ; Noise (video) ; Remote sensing ; Chemistry ; Physics ; Geology ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Christopher Sutton | Mario Boley | Luca M. Ghiringhelli | Matthias Rupp | Jilles Vreeken | Matthias Scheffler
Source: ChemRxiv (preprint, 2019-09-09)
Field: Materials Science — Materials Chemistry

Identifying Domains of Applicability of Machine Learning Models for Materials Science We present an extension to the usual machine learning process that allows for the identification of the domain of applicability of a fitted model, i.e., the region in its domain where it performs most accurately. This approach is applied to several vastly different but commonly used materials representations (namely the n-gram approach, SOAP, and the many body tenor representation), which are practically indistinguishable based on performance using a single error statistic. Moreover, these models appear unsatisfactory for screening applications as they fail to reliably identify the ground state polymorphs. When applying our newly developed analysis for each of the models, we can identify the domain of applicability for each model according to a simple set of interpretable conditions. We show that identification of the domain of applicability in the prediction of the formation energy enables a more accurate ground-state search - a crucial step for the discovery of novel materials. Computer science ; Statistic ; Process (computing) ; Domain (mathematical analysis) ; Identification (biology) ; Representation (politics) ; Simple (philosophy) ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Extension (predicate logic) ; Energy (signal processing) ; Algorithm ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Colin A. Grambow | Lagnajit Pattanaik | William H. Green
Source: ChemRxiv (preprint, 2019-12-23)
Field: Materials Science — Materials Chemistry

Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry Reaction times, activation energies, branching ratios, yields, and many other quantitative attributes are important for precise organic syntheses and generating detailed reaction mechanisms. Often, it would be useful to be able to classify proposed reactions as fast or slow. However, quantitative chemical reaction data, especially for atom-mapped reactions, are difficult to find in existing databases. Therefore, we used automated potential energy surface exploration to generate 12,000 organic reactions involving H, C, N, and O atoms calculated at the ωB97X-D3/def2-TZVP quantum chemistry level. We report the results of geometry optimizations and frequency calculations for reactants, products, and transition states of all reactions. Additionally, we extracted atom-mapped reaction SMILES, activation energies, and enthalpies of reaction. We believe that this data will accelerate progress in automated methods for organic synthesis and reaction mechanism generation—for example, by enabling the development of novel machine learning models for quantitative reaction prediction. Transition state ; Reaction mechanism ; Chemistry ; Branching (polymer chemistry) ; Elementary reaction ; Chemical reaction ; Quantum chemistry ; Atom (system on chip) ; Quantum ; Computational chemistry ; Potential energy surface ; Reaction rate ; Computer science ; Molecule ; Catalysis ; Physics ; Organic chemistry ; Kinetics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Mohammed AlQuraishi
Source: Journal of Computational Chemistry (article, 2019-01-07)
Field: Materials Science — Materials Chemistry

Parallelized Natural Extension Reference Frame: Parallelized Conversion from Internal to Cartesian Coordinates The conversion of polymer parameterization from internal coordinates (bond lengths, angles, and torsions) to Cartesian coordinates is a fundamental task in molecular modeling, often performed using the natural extension reference frame (NeRF) algorithm. NeRF can be parallelized to process multiple polymers simultaneously, but is not parallelizable along the length of a single polymer. A mathematically equivalent algorithm, pNeRF, has been derived that is parallelizable along a polymer's length. Empirical analysis demonstrates an order-of-magnitude speed up using modern GPUs and CPUs. In machine learning-based workflows, in which partial derivatives are backpropagated through NeRF equations and neural network primitives, switching to pNeRF can reduce the fractional computational cost of coordinate conversion from over two-thirds to around 10%. An optimized TensorFlow-based implementation of pNeRF is available on GitHub at https://github.com/aqlaboratory/pnerf © 2018 Wiley Periodicals, Inc. Parallelizable manifold ; Cartesian coordinate system ; Computer science ; Reference frame ; Process (computing) ; Geographic coordinate conversion ; Workflow ; Frame (networking) ; Algorithm ; Extension (predicate logic) ; Computational science ; Computation ; Coordinate system ; Parallel computing ; Geometry ; Artificial intelligence ; Mathematics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Liang Qi
Source: Computational Materials Science (article, 2019-03-14)
Field: Materials Science — Materials Chemistry

Effects of electronic structures on mechanical properties of transition metals and alloys Materials science ; Crystal twinning ; Ductility (Earth science) ; Electronic structure ; Deformation (meteorology) ; Transition metal ; Elastic modulus ; Condensed matter physics ; Metallurgy ; Composite material ; Chemistry ; Microstructure Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Advanced Materials Characterization Techniques
Authors: Niklas W. A. Gebauer | Michael Gastegger | Kristof T. Schütt
Source: arXiv (Cornell University) (article, 2019-06-01)
Field: Materials Science — Materials Chemistry

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired properties. While graph-based models have previously been proposed, they are restricted by a lack of spatial information such that they are unable to recognize spatial isomerism and non-bonded interactions. Here, we introduce a generative neural network for 3d point sets that respects the rotational invariance of the targeted structures. We apply it to the generation of molecules and demonstrate its ability to approximate the distribution of equilibrium structures using spatial metrics as well as established measures from chemoinformatics. As our model is able to capture the complex relationship between 3d geometry and electronic properties, we bias the distribution of the generator towards molecules with a small HOMO-LUMO gap - an important property for the design of organic solar cells. Cheminformatics ; Chemical space ; Computer science ; Theoretical computer science ; Artificial intelligence ; Maxima and minima ; Graph ; Biological system ; Statistical physics ; Computational chemistry ; Mathematics ; Drug discovery ; Chemistry ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Data Visualization and Analytics