jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julio Coll
Source:  (preprint, 2021-09-06)
Field: Chemistry — Organic Chemistry

Star-shaped Triazine-derivatives: would they crossbind SARS-CoV-2 spike helices? This work describes synthesizable water-soluble Triazine-derivatives computationally crossbinding the S spike helices of Severe Acute Respiratory Syndrome coronavirus (SARS)-CoV-2. The "spring-loaded switch-folding” (S-SLSF) α-helices included in the S homotrimer top-to-bottom cavity and implicated in viral-host membrane fusion were targeted by star-shaped Trihydroxyl-Triphenyl-Triazines (TTT) leads at subnanomolar binding-scores. Exploration of in silico leads among millions of molecular candidates, included several similar searches, core-replacement, fragment extensions, or convolutional neural network deep-screening combined with hundreds of water-soluble lead-derivatives identified by manual iterations and commercially available building-blocks for chemical synthesis. The lead-derivatives are briefly discussed for in vitro validation and possibilities of fusion inhibition substituting mutations. In silico ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Triazine ; Chemistry ; Computational biology ; Coronavirus disease 2019 (COVID-19) ; Helix (gastropod) ; Coronavirus ; Biophysics ; Combinatorial chemistry ; Stereochemistry ; Biology ; Biochemistry ; Medicine ; Gene Synthesis and Characterization of Heterocyclic Compounds ; Click Chemistry and Applications ; Bacteriophages and microbial interactions
Authors: Conlain Kelly | Surya R. Kalidindi
Source: Computational Materials Science (article, 2021-03-07)
Field: Materials Science — Materials Chemistry

Recurrent localization networks applied to the Lippmann-Schwinger equation Computer science ; Generalizability theory ; Range (aeronautics) ; Artificial neural network ; Optimization problem ; Artificial intelligence ; Convolutional neural network ; Deep learning ; Field (mathematics) ; Interpretation (philosophy) ; Equation solving ; Applied mathematics ; Mathematical optimization ; Theoretical computer science ; Algorithm ; Mathematics ; Partial differential equation ; Mathematical analysis Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Magnetic Properties and Applications
Authors: Sam Vimal | Achyuth Ramachandran | Anirudh Selvam | S. Karthick
Source: FME Transaction (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites As composites are materials whose properties can essentially be customized to suit the necessities of the engineering application on hand, they are being widely used in many applications for radically different purposes. In order to ensure quality in production process of composite products, a solid understanding of the process involved during its manufacturing is essential to ensure the product is free from both internal and external defects. To that aim, a study was conducted to model Thrust force and Torque on drilling of Glass-Hemp-Flax reinforced polymer composite by fabricating and maching the composite as per Taguchi's L 27 Orthogonal Array. The process parameters considered for modeling are drill diameter, spindle speed and feed rate. Using the process control parameters as inputs and thrust force and torque to be predicted as outputs, artificial neural networks (ANNs) were created to model the effects of the inputs and their interactions. The predictions obtained from the neural networks were compared with the values obtained from experimentation. Excellent agreement was found between the two sets of values, establishing grounds for more extensive use of neural networks in modelling of machining parameters. Thrust ; Artificial neural network ; Taguchi methods ; Torque ; Mechanical engineering ; Drilling ; Drill ; Machining ; Composite number ; Glass fiber ; Process (computing) ; Materials science ; Python (programming language) ; Engineering ; Composite material ; Computer science ; Artificial intelligence Natural Fiber Reinforced Composites ; Advanced machining processes and optimization ; Mechanical Engineering and Vibrations Research
Authors: Andrew E. Sifain | Betsy M. Rice | Samuel H. Yalkowsky | Brian C. Barnes
Source: Journal of Molecular Graphics and Modelling (article, 2021-01-29)
Field: Materials Science — Materials Chemistry

Machine learning transition temperatures from 2D structure Computer science ; Representation (politics) ; Chemical space ; Boosting (machine learning) ; A priori and a posteriori ; Machine learning ; Entropy (arrow of time) ; Artificial intelligence ; Data mining ; Chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Miguel Yescas | Brendan Le Gloannec | François Roch
Source: Journal of Nuclear Materials (article, 2021-10-21)
Field: Materials Science — Metals and Alloys

Prediction of mechanical properties of PWR vessel steel heads containing residual carbon macrosegregation using Artificial Neural Networks Charpy impact test ; Ultimate tensile strength ; Toughness ; Forging ; Residual stress ; Residual ; Artificial neural network ; Fracture toughness ; Materials science ; Variable (mathematics) ; Reactor pressure vessel ; Composite material ; Mechanical engineering ; Computer science ; Nuclear engineering ; Engineering ; Metallurgy ; Artificial intelligence ; Mathematics ; Algorithm Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Fatigue and fracture mechanics
Authors: Elli Leppänen | Anja Aarva | Sami Sainio | A. Miguel | Tomi Laurila
Source: Journal of Physics Condensed Matter (article, 2021-08-03)
Field: Chemistry — Electrochemistry

Connection between the physicochemical characteristics of amorphous carbon thin films and their electrochemical properties Connecting a material's surface chemistry with its electrocatalytic performance is one of the major questions in analytical electrochemistry. This is especially important in many sensor applications where analytes from complex media need to be measured. Unfortunately, today this connection is still largely missing except perhaps for the most simple ideal model systems. Here we present an approach that can be used to obtain insights about this missing connection and apply it to the case of carbon nanomaterials. In this paper we show that by combining advanced computational techniques augmented by machine learning methods with x-ray absorption spectroscopy (XAS) and electrochemical measurements, it is possible to obtain a deeper understanding of the correlation between local surface chemistry and electrochemical performance. As a test case we show how by computationally assessing the growth of amorphous carbon (a-C) thin films at the atomic level, we can create computational structural motifs that may in turn be used to deconvolute the XAS data from the real samples resulting in local chemical information. Then, by carrying out electrochemical measurements on the same samples from which x-ray spectra were measured and that were further characterized computationally, it is possible to gain insight into the interplay between the local surface chemistry and electrochemical performance. To demonstrate this methodology, we proceed as follows: after assessing the basic electrochemical properties of a-C films, we investigate the effect of short HNO<sub>3</sub>treatment on the sensitivity of these electrodes towards an inner sphere redox probe dopamine to gain knowledge about the influence of altered surface chemistry to observed electrochemical performance. These results pave the way towards a more general assessment of electrocatalysis in different systems and provide the first steps towards data driven tailoring of electrode surfaces to gain optimal performance in a given application. Connection (principal bundle) ; Materials science ; Electrochemistry ; Amorphous solid ; Carbon fibers ; Carbon film ; Chemical engineering ; Amorphous carbon ; Thin film ; Nanotechnology ; Composite material ; Electrode ; Chemistry ; Organic chemistry ; Engineering ; Mechanical engineering ; Physical chemistry ; Composite number Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Electrochemical sensors and biosensors
Authors: Makena Dettmann | L. S. R. Cavalcante | Corina A. Magdaleno | Karina Masalkovaitė | Daniel Vong | Jordan T. Dull | Barry P. Rand | Luke L. Daemen | Nir Goldman | Roland Faller | Adam J. Moulé
Source: Journal of Chemical Theory and Computation (article, 2021-11-24)
Field: Materials Science — Materials Chemistry

Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal-organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy-efficiency tradeoffs have not been examined in depth. In this study, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction. Vibration ; Density functional theory ; Chebyshev polynomials ; Computer science ; Rubrene ; Chebyshev filter ; Computational physics ; Inelastic neutron scattering ; Materials science ; Inelastic scattering ; Scattering ; Algorithm ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Optics ; Quantum mechanics ; Optoelectronics ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Denghui Lu | Wanrun Jiang | Yixiao Chen | Linfeng Zhang | Weile Jia | Handong Wang | Mohan Chen
Source:  (preprint, 2021-07-05)
Field: Materials Science — Materials Chemistry

DP Train, then DP Compress: Model Compression in Deep Potential Molecular Dynamics. Machine learning based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from a lower efficiency as compared to typical empirical force fields due to more sophisticated computations involved. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning based PES model. This scheme, we call DP Compress, is an efficient post-processing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP- based molecular dynamics simulations by an order of magnitude faster, and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available at this https URL. Computer science ; Computation ; Molecular dynamics ; Deep learning ; Compression (physics) ; Boosting (machine learning) ; Computational science ; Data compression ; Algorithm ; Computer engineering ; Artificial intelligence ; Materials science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Hiroshi Ohno
Source: Soft Computing (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Training data augmentation using generative models with statistical guarantees for materials informatics Computer science ; Benchmark (surveying) ; Estimator ; Artificial neural network ; Kernel (algebra) ; Machine learning ; Materials informatics ; Kernel density estimation ; Artificial intelligence ; Algorithm ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Ritaban Dutta | Cherry Chen | D. Renshaw | Daniel Liang
Source: Scientific Reports (article, 2021-08-12)
Field: Materials Science — Materials Chemistry

Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation. Computer science ; Boltzmann machine ; Artificial intelligence ; Machine learning ; Computer vision ; Deep learning Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing
Authors: Masaki Teranishi
Source: Mechanics Research Communications (article, 2021-11-30)
Field: Materials Science — Materials Chemistry

Neural network constitutive model for uniaxial cyclic plasticity based on return mapping algorithm Constitutive equation ; Artificial neural network ; Plasticity ; Monotonic function ; Algorithm ; Network model ; Hardening (computing) ; Computer science ; Generalization ; Structural engineering ; Materials science ; Artificial intelligence ; Layer (electronics) ; Mathematics ; Engineering ; Finite element method ; Mathematical analysis ; Composite material Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics ; Microstructure and Mechanical Properties of Steels
Authors: I Y Prayogi | Sandra Sandra | Yusuf Hendrawan
Source: IOP Conference Series Earth and Environmental Science (article, 2021-11-01)
Field: Chemistry — Analytical Chemistry

Image classification of different clove (Syzygium aromaticum) quality using deep learning method with convolutional neural network algorithm Abstract The objective of this study is to classify the quality of dried clove flowers using deep learning method with Convolutional Neural Network (CNN) algorithm, and also to perform the sensitivity analysis of CNN hyperparameters to obtain best model for clove quality classification process. The quality of clove as raw material in this study was determined according to SNI 3392-1994 by PT. Perkebunan Nusantara XII Pancusari Plantation, Malang, East Java, Indonesia. In total 1,600 images of dried clove flower were divided into 4 qualities. Each clove quality has 225 training data, 75 validation data, and 100 test data. The first step of this study is to build CNN model architecture as first model. The result of that model gives 65.25% reading accuracy. The second step is to analyze CNN sensitivity or CNN hyperparameter on the first model. The best value of CNN hyperparameter in each step then to be used in the next stage. Finally, after CNN hyperparameter carried out the reading accuracy of the test data is improved to 87.75%. Hyperparameter ; Convolutional neural network ; Artificial intelligence ; Computer science ; Syzygium ; Pattern recognition (psychology) ; Artificial neural network ; Algorithm ; Machine learning ; Traditional medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Stefan Hiemer | Stefano Zapperi
Source: Materials Theory (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

From mechanism-based to data-driven approaches in materials science Abstract A time-honored approach in theoretical materials science revolves around the search for basic mechanisms that should incorporate key feature of the phenomenon under investigation. Recent years have witnessed an explosion across areas of science of a data-driven approach fueled by recent advances in machine learning. Here we provide a brief perspective on the strengths and weaknesses of mechanism based and data-driven approaches in the context of the mechanics of materials. We discuss recent literature on dislocation dynamics, atomistic plasticity in glasses focusing on the empirical discovery of governing equations through artificial intelligence. We conclude highlighting the main open issues and suggesting possible improvements and future trajectories in the fields. Mechanism (biology) ; Context (archaeology) ; Data science ; Computer science ; Perspective (graphical) ; Phenomenon ; Strengths and weaknesses ; Management science ; Cognitive science ; Artificial intelligence ; Epistemology ; Engineering ; Psychology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Dakota Folmsbee | David Ryan Koes | Geoffrey Hutchison
Source: The Journal of Physical Chemistry A (article, 2021-02-25)
Field: Materials Science — Materials Chemistry

Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization While many machine learning (ML) methods, particularly deep neural networks, have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. The methods were trained on the existing ANI-1 training set, calculated using the ωB97X/6-31G(d) single points at nonequilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and a new libmolgrid-based convolutional neural net, the Colorful CNN, show good performance. Maxima and minima ; Artificial neural network ; Anharmonicity ; Convolutional neural network ; Potential energy ; Range (aeronautics) ; Work (physics) ; Quantum ; Focus (optics) ; Energy minimization ; Density functional theory ; Computer science ; Statistical physics ; Physics ; Artificial intelligence ; Algorithm ; Mathematics ; Materials science ; Mathematical analysis ; Quantum mechanics ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Soo Jin Kim | Jae‐Seung Jeong | Ho Won Jang | Hyunjung Yi | Hoichang Yang | Hyunsu Ju | Jung Ah Lim
Source: Advanced Materials (article, 2021-07-01)
Field: Materials Science — Polymers and Plastics

Neurofiber Transistors: Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning (Adv. Mater. 26/2021) Dendritic neural-network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are developed by Hyunsu Ju, Jung Ah Lim, and co-workers in article number 2100475. These neurofiber transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enable a multichannel synaptic junction of a neural network via simple physical contact of gate-electrode microfibers, as the dendritic connections of a biological neuron fiber. This work provides insight into the design of the materials and architecture of organic neuromorphic devices with reliable artificial synaptic functionality for artificial neural network learning. Materials science ; Neuromorphic engineering ; Microfiber ; Artificial neural network ; Transistor ; Electrode ; Nanotechnology ; Synapse ; Physical neural network ; Computer science ; Optoelectronics ; Artificial intelligence ; Electrical engineering ; Neuroscience ; Recurrent neural network ; Voltage ; Engineering Conducting polymers and applications ; Advanced Memory and Neural Computing ; Analytical Chemistry and Sensors
Authors: Tri Nguyen | Yichen Guo | Shuyu Qin | Kylie S. Frew | Ruijuan Xu | Joshua Agar
Source: npj Computational Materials (article, 2021-10-08)
Field: Materials Science — Materials Chemistry

Symmetry-aware recursive image similarity exploration for materials microscopy Abstract In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package ( https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer ) of this interactive tool for researchers to use with their data. Computer science ; Metadata ; Similarity (geometry) ; Information retrieval ; Image (mathematics) ; Artificial intelligence ; World Wide Web Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Electron Microscopy Techniques and Applications