jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Igor Smirnov | Alexey Kutyrev | Nikolay Kiktev
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Neural network for identifying apple fruits on the crown of a tree To identify the fruits on the crowns of trees and count their number, a software and hardware complex (PAC) based on a technical vision system and a recurrent neural network of deep learning has been developed. The created neural network and class allocation algorithms allow the software and hardware complex to function stably in the conditions of industrial horticultural plantings, regardless of the size and interference of foliage, to determine the color of the fruit surface, to identify the presence of diseases and defects of fruits. The developed package provides digital monitoring of both photographic materials and video streams in online mode. Artificial neural network ; Software ; Computer science ; Interference (communication) ; Tree (set theory) ; Function (biology) ; Crown (dentistry) ; Artificial intelligence ; Class (philosophy) ; Pattern recognition (psychology) ; Machine learning ; Biology ; Mathematics ; Telecommunications ; Operating system Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Horticultural and Viticultural Research
Authors: Pin-Wen Guan
Source: Scripta Materialia (article, 2021-09-17)
Field: Materials Science — Materials Chemistry

Differentiable thermodynamic modeling A new framework of thermodynamic modeling is proposed by introducing the concept of differentiable programming, where all the thermodynamic observables including both thermochemical quantities and phase equilibria can be differentiated with respect to the underlying model parameters, thus allowing the models learned by gradient-based optimization. It is shown that thermodynamic modeling and deep learning can be seamlessly integrated and unified within this framework. A preliminary successful application is demonstrated for the Cu-Rh system. It is expected that thermodynamic modeling in a deep learning style can increase prediction power of models, and provide more effective guidance for design, synthesis and optimization of multi-component materials with complex chemistry via learning various types of data. Differentiable function ; Component (thermodynamics) ; Observable ; Computer science ; Automatic differentiation ; Thermodynamic system ; Statistical physics ; Materials science ; Biological system ; Thermodynamics ; Mathematics ; Physics ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Marwah M. M. Madkhali | Conor D. Rankine | Thomas J. Penfold
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Enhancing the analysis of disorder in X-ray absorption spectra: application of deep neural networks to T-jump-X-ray probe experiments Many chemical and biological reactions, including ligand exchange processes, require thermal energy for the reactants to overcome a transition barrier and reach the product state. Temperature-jump (T-jump) spectroscopy uses a near-infrared (NIR) pulse to rapidly heat a sample, offering an approach for triggering these processes and directly accessing thermally-activated pathways. However, thermal activation inherently increases the disorder of the system under study and, as a consequence, can make quantitative interpretations of structural changes challenging. In this Article, we optimise a deep neural network (DNN) for the instantaneous prediction of Co K-edge X-ray absorption near-edge structure (XANES) spectra. We apply our DNN to analyse T-jump pump/X-ray probe data pertaining to the ligand exchange processes and solvation dynamics of Co2+ in chlorinated aqueous solution. Our analysis is greatly facilitated by machine learning, as our DNN is able to predict quickly and cost-effectively the XANES spectra of thousands of geometric configurations sampled from ab initio molecular dynamics (MD) using nothing more than the local geometric environment around the X-ray absorption site. We identify directly the structural changes following the T-jump, which are dominated by sample heating and a commensurate increase in the Debye-Waller factor. Jump ; Spectral line ; Absorption (acoustics) ; Artificial neural network ; X-ray ; Absorption spectroscopy ; Energy (signal processing) ; Thermal ; Power (physics) ; Physics ; Materials science ; Computational physics ; Molecular physics ; Statistical physics ; Atomic physics ; Chemical physics ; Optics ; Computer science ; Artificial intelligence ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Yuqing Cheng | Handong Wang | Shuaichuang Wang | Xingyu Gao | Qiong Li | Jun Fang | H. Song | Weidong Chu | Gongmu Zhang | Haifeng Song | Haifeng Liu
Source: AIP Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep-learning potential method to simulate shear viscosity of liquid aluminum at high temperature and high pressure by molecular dynamics The shear viscosity of matter and efficient simulating methods in a wide range of temperatures and densities are desirable. In this study, we present the deep-learning many-body potential (the deep potential) method to reduce the computational cost of simulations for the viscosity of liquid aluminum at high temperature and high pressure with accurate results. Viscosities for densities of 2.35 g/cm3, 2.7 g/cm3, 3.5 g/cm3, and 4.27 g/cm3 and temperatures from melting points to about 50 000 K are calculated. The results agree well with the experiment data at a pressure near 1 bar and are consistent with the simulation of first-principles at high pressure and high temperature. We reveal the behavior of the shear viscosity of liquid Al at a range where the current experimental results do not exist. Based on the available experimental data and newly generated simulation data, we propose a modified Enskog–Dymond theory, which can analytically calculate the viscosity of Al at this range. This research is helpful for numerous potential applications. Viscosity ; Thermodynamics ; Aluminium ; Molecular dynamics ; Bar (unit) ; Shear (geology) ; Materials science ; Range (aeronautics) ; High pressure ; Atmospheric temperature range ; Shear viscosity ; Melting temperature ; Mechanics ; Chemistry ; Physics ; Composite material ; Computational chemistry Machine Learning in Materials Science ; High-pressure geophysics and materials ; Material Dynamics and Properties
Authors: Kyohei Noda | Yasuhiko Igarashi | Hiroaki Imai | Yuya Oaki
Source: Chemical Communications (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Yield-prediction models for efficient exfoliation of soft layered materials into nanosheets Yield-prediction models were studied for efficient exfoliation of soft layered materials stacked via van der Waals interactions with the assistance of machine learning on small experimental data. High-yield exfoliation of graphite and layered organic polymer was achieved under the conditions guided by the models in a limited number of experiments. Exfoliation joint ; Yield (engineering) ; Materials science ; Nanotechnology ; Process engineering ; Composite material ; Graphene ; Engineering Graphene research and applications ; Ferroelectric and Piezoelectric Materials ; MXene and MAX Phase Materials
Authors: Ludwig Schneider | Juan Pablo
Source: Macromolecules (article, 2021-10-28)
Field: Materials Science — Materials Chemistry

Combining Particle-Based Simulations and Machine Learning to Understand Defect Kinetics in Thin Films of Symmetric Diblock Copolymers The self-assembly of soft matter provides a practical and scalable route toward the production of nanostructured materials, with minimal need for direct intervention at nanoscopic length scales. Symmetric diblock copolymers, which can self-assemble into a lamellar phase, are a prototype for this class of materials. In this work, we introduce a machine learning model that is trained by intermediate time-scale simulations of a soft, coarse-grained model. The aim of the model is to simulate defect kinetics in the lamellar morphology, as the material relaxes toward equilibrium. To do so, we exploit the physical characteristics of overdamped dynamics and formulate the problem of time evolution as a Markov chain. The trained artificial neural network (ANN) predicts a time-independent transition probability from one time step to the next. As a result, we arrive at a method that can be repeatedly applied to generate long-time trajectories. Predicting defect kinetics in this manner provides hitherto unavailable insights into the late-time dynamics of block copolymer relaxation. The neural network is purposely designed to be independent of input size, which enables training on small systems, and enabling predictions over large scales. As a demonstration of these capabilities, in this work, we leverage the ANN to obtain information about the statistics of defect motion and lifetimes over a long-range ordering process. Statistical physics ; Computer science ; Materials science ; Leverage (statistics) ; Soft matter ; Lamellar structure ; Nanoscopic scale ; Markov chain ; Molecular dynamics ; Copolymer ; Nanotechnology ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Polymer ; Colloid Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Theoretical and Computational Physics
Authors: Farid Behboodi‐Sadabad | S. Li | Wenxi Lei | Y. Liu | Timo Sommer | Pascal Friederich | Caroline Sobek | Phillip B. Messersmith | Pavel A. Levkin
Source: Materials Today Bio (article, 2021-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-throughput screening of multifunctional nanocoatings based on combinations of polyphenols and catecholamines Biomimetic surface coatings based on plant polyphenols and catecholamines have been used broadly in a variety of applications. However, the lack of a rational cost-effective platform for screening these coatings and their properties limits the true potential of these functional materials to be unleashed. Here, we investigated the oxidation behavior and coating formation ability of a library consisting of 45 phenolic compounds and catecholamines. UV-vis spectroscopy demonstrated significant acceleration of oxidation and polymerization under UV irradiation. We discovered that several binary mixtures resulted in non-additive behavior (synergistic or antagonistic effect) yielding much thicker or thinner coatings than individual compounds measured by ellipsometry. To investigate the properties of coatings derived from new combinations, we used a miniaturized high-throughput strategy to screen 2,532 spots coated with single, binary, and ternary combinations of coating precursors in one run. We evaluated the use of machine learning models to learn the relation between the chemical structure of the precursors and the thickness of the nanocoatings. Formation and stability of nanocoatings were investigated in a high-throughput manner via discontinuous dewetting. 30 stable combinations (hits) were used to tune the surface wettability and to form water droplet microarray and spot size gradients of water droplets on the coated surface. No toxicity was observed against eukaryotic HeLa cells and <i>Pseudomonas aeruginosa</i> (strain PA30) bacteria after 24 h incubation at 37 °C. The strategy introduced here for high-throughput screening of nanocoatings derived from combinations of coating precursors enables the discovery of new functional materials for various applications in science and technology in a cost-effective miniaturized manner. Coating ; Materials science ; Wetting ; Polymerization ; Ternary operation ; Dewetting ; Nanotechnology ; Chemical engineering ; Polymer ; Thin film ; Composite material Gold and Silver Nanoparticles Synthesis and Applications ; Polymer Surface Interaction Studies ; Surfactants and Colloidal Systems
Authors: Philip A. E. Murgatroyd | Kieran Routledge | Samantha Durdy | Michael W. Gaultois | T. Wesley Surta | Matthew S. Dyer | John B. Claridge | Stanislav N. Savvin | D. Pelloquin | Sylvie Hébert | Jonathan Alaria
Source: Advanced Functional Materials (article, 2021-02-22)
Field: Materials Science — Materials Chemistry

Chemically Controllable Magnetic Transition Temperature and Magneto‐Elastic Coupling in MnZnSb Compounds Abstract Magneto‐caloric materials offer the possibility to design environmentally friendlier thermal management devices compared to the widely used gas‐based systems. The challenges to develop this solid‐state based technology lie in the difficulty of finding materials presenting a large magneto‐caloric effect over a broad temperature span together with suitable secondary application parameters such as low heat capacity and high thermal conductivity. A series of compounds derived from the PbFCl structure is investigated using a combination of computational and experimental methods focusing on the change of cell volume in magnetic and non‐magnetic ground states. Scaling analysis of the magnetic properties determines that they are second order phase transition ferromagnets and that the magnetic entropy change is driven by the coupling of magneto‐elastic strain in the square‐net through the magnetic transition determined from neutron and synchrotron X‐ray diffraction. The primary and secondary application related properties are measured experimentally, and the c/a parameter is identified as an accurate proxy to control the magnetic transition. Chemical substitution on the square‐net affords tuning of the Curie temperature over a broad temperature span between 252 and 322 K. A predictive machine learning model for the c/a parameter is developed to guide future exploratory synthesis. Materials science ; Curie temperature ; Ferromagnetism ; Phase transition ; Synchrotron radiation ; Condensed matter physics ; Thermal conductivity ; Synchrotron ; Thermodynamics ; Optics ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science
Authors: Xiaojuan Tian | Mingguang Chen
Source: Scientific Reports (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Descriptor selection for predicting interfacial thermal resistance by machine learning methods Interfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications. Machine learning ; Artificial intelligence ; Computer science ; Univariate ; Model selection ; Selection (genetic algorithm) ; Kriging ; Decision tree ; Feature selection ; Kernel (algebra) ; Data mining ; Mathematics ; Multivariate statistics Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Ramin Nikzad‐Langerodi | Erik Andries
Source: Journal of Chemometrics (article, 2021-11-01)
Field: Chemistry — Analytical Chemistry

A chemometrician's guide to transfer learning Domain adaptation (DA) and Transfer Learning (TL) are terms coined by the machine learning community, particular computer vision. However, the chemometrics community has been working on similar problems (with chemical and spectroscopic contexts) for much longer and these techniques go under the moniker of calibration transfer and maintenance (CTM). Both the machine learning and chemometrics communities often encounter the same problem: their prediction models have a tendency to rely too much on the distribution of the data on which they have been trained. In practice, we are constantly updating a predictive model on data that evolves over time. Chemometrics ; Intersection (aeronautics) ; Computer science ; Transfer of learning ; Competence (human resources) ; Artificial intelligence ; Domain (mathematical analysis) ; Cheminformatics ; Adaptation (eye) ; Applicability domain ; Mathematics education ; Data science ; Chemistry ; Machine learning ; Mathematics ; Engineering ; Psychology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Yan Xiang | Yuhang Tang | Hongyi Liu | Guang Lin | Huai Sun
Source: The Journal of Physical Chemistry A (article, 2021-05-17)
Field: Materials Science — Materials Chemistry

Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of Molecules This work presents a Gaussian process regression (GPR) model on top of a novel graph representation of chemical molecules that predicts thermodynamic properties of pure substances in single, double, and triple phases. A transferable molecular graph representation is proposed as the input for a marginalized graph kernel, which is the major component of the covariance function in our GPR models. Radial basis function kernels of temperature and pressure are also incorporated into the covariance function when necessary. We predicted three types of representative properties of pure substances in single, double, and triple phases, i.e., critical temperature, vapor–liquid equilibrium (VLE) density, and pressure–temperature density. The accuracy of the models is nearly identical to the precision of the experimental measurements. Moreover, the reliability of our predictions can be quantified on a per-sample basis using the posterior uncertainty of the GPR model. We compare our model against Morgan fingerprints and a graph neural network to further demonstrate the advantage of the proposed method. Covariance ; Kriging ; Covariance function ; Graph ; Kernel (algebra) ; Gaussian ; Gaussian process ; Computer science ; Statistical physics ; Mathematics ; Algorithm ; Applied mathematics ; Statistics ; Chemistry ; Discrete mathematics ; Physics ; Theoretical computer science ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yingying Sheng | Yifu Zhu | María Luisa Cerón | Yufu Yi | Peng Liu | Peng Wang | Ting Xue | María Belén Camarada | Yangping Wen
Source: Journal of Electroanalytical Chemistry (article, 2021-07-12)
Field: Materials Science — Materials Chemistry

A stable nanosilver decorated phosphorene nanozyme with phosphorus-doped porous carbon microsphere for intelligent sensing of 8-hydroxy-2′-deoxyguanosine Chemistry ; Phosphoric acid ; Chemical engineering ; Specific surface area ; Nanotechnology ; Nuclear chemistry ; Inorganic chemistry ; Materials science ; Organic chemistry ; Catalysis Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Shiyu Liu | Shutao Wang | Chunhai Hu | Xiaoyang Qin | Junzhu Wang | Deming Kong
Source: Measurement (article, 2021-10-08)
Field: Chemistry — Analytical Chemistry

Development of a new NIR-machine learning approach for simultaneous detection of diesel various properties Diesel fuel ; Cetane number ; Process engineering ; Support vector machine ; Boiling point ; Near-infrared spectroscopy ; Mean squared error ; Coefficient of determination ; Computer science ; Artificial intelligence ; Automotive engineering ; Machine learning ; Engineering ; Mathematics ; Biodiesel ; Statistics ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Yathavan Subramanian | R. Veena | Muhammed Ali | Avinash Kumar | Ramesh Kumar Gubediran | Anitha Dhanasekaran | Dhanasekaran Gurusamy | Kalpana Muniandi
Source: Materials Today Proceedings (article, 2021-07-15)
Field: Materials Science — Materials Chemistry

Artificial intelligence technique based performance estimation of solid oxide fuel cells Cathode ; Anode ; Materials science ; Power density ; Electrode ; Electrolyte ; Current density ; Oxide ; Non-blocking I/O ; Solid oxide fuel cell ; Nuclear engineering ; Chemical engineering ; Computer science ; Analytical Chemistry (journal) ; Power (physics) ; Electrical engineering ; Metallurgy ; Chemistry ; Engineering ; Catalysis ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Gas Sensing Nanomaterials and Sensors
Authors: Joshua A. Vita | Dallas R. Trinkle
Source: Computational Materials Science (article, 2021-08-19)
Field: Materials Science — Materials Chemistry

Exploring the necessary complexity of interatomic potentials Interpretability ; Interatomic potential ; Leverage (statistics) ; Computer science ; Metadynamics ; Machine learning ; Computational complexity theory ; Spline (mechanical) ; Artificial intelligence ; Algorithm ; Statistical physics ; Molecular dynamics ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography