jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Flore Mekki‐Berrada | Zekun Ren | Tan Huang | Wai Kuan Wong | Fang Zheng | Jiaxun Xie | Siyu Tian | J. Senthilnath | Zackaria Mahfoud | Daniil Bash | Kedar Hippalgaonkar | Saif Khan | Tonio Buonassisi | Qianxiao Li | Xiaonan Wang
Source: ChemRxiv (preprint, 2020-07-21)
Field: Materials Science — Materials Chemistry

Two-Step Machine Learning Enables Optimized Nanoparticle Synthesis &lt;p&gt;In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with a desired absorbance spectrum. Combining a Gaussian Process based Bayesian Optimization (BO) with a Deep Neural Network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable&lt;i&gt; &lt;/i&gt;by humans, the proposed framework efficiently optimizes the nanomaterial synthesis, and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.&lt;/p&gt; Bayesian optimization ; Absorbance ; Nanomaterials ; Artificial neural network ; Computer science ; Gaussian process ; Deep learning ; Throughput ; Nanoparticle ; Artificial intelligence ; Process (computing) ; Kriging ; Microfluidics ; Machine learning ; Biological system ; Nanotechnology ; Gaussian ; Materials science ; Chemistry ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Daniel P. Russo | Xiliang Yan | Sunil Shende | Heng Huang | Bing Yan | Hao Zhu
Source: Analytical Chemistry (article, 2020-09-24)
Field: Materials Science — Materials Chemistry

Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties Digitalizing complex nanostructures into data structures suitable for machine learning modeling without losing nanostructure information has been a major challenge. Deep learning frameworks, particularly convolutional neural networks (CNNs), are especially adept at handling multidimensional and complex inputs. In this study, CNNs were applied for the modeling of nanoparticle activities exclusively from nanostructures. The nanostructures were represented by virtual molecular projections, a multidimensional digitalization of nanostructures, and used as input data to train CNNs. To this end, 77 nanoparticles with various activities and/or physicochemical property results were used for modeling. The resulting CNN model predictions show high correlations with the experimental results. An analysis of a trained CNN quantitatively showed that neurons were able to recognize distinct nanostructure features critical to activities and physicochemical properties. This "end-to-end" deep learning approach is well suited to digitalize complex nanostructures for data-driven machine learning modeling and can be broadly applied to rationally design nanoparticles with desired activities. Nanostructure ; Convolutional neural network ; Deep learning ; Nanoparticle ; Artificial intelligence ; Chemistry ; Nanotechnology ; Artificial neural network ; Computer science ; Machine learning ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Pınar Acar
Source: AIAA Journal (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Machine Learning Reinforced Crystal Plasticity Modeling Under Experimental Uncertainty This Paper addresses a two-step computational approach to building a robust modeling environment for the titanium–aluminum alloy, Ti-7Al, a candidate aerospace material owing to superior mechanical performance under high stresses. To be used in aerospace applications, the large deformation behavior of the alloy should be investigated with a high-fidelity crystal plasticity model. However, there is no universal agreement on the crystal plasticity parameters, and previous efforts are only based on deterministic techniques. Therefore, our goal is to build a crystal plasticity model for Ti-7Al, which is validated for the global (component-scale) and local (grain-level) features by considering the experimental uncertainty. In the first step, the lower and upper bounds of the crystal plasticity parameters are determined with an inverse problem that is solved to match the computations with the experimental stress-strain data. The second step validates the local features by solving an optimization problem that minimizes the difference between the simulated and experimental microstructural textures. The optimization is performed using an Artificial Neural Network (ANN-)based surrogate model that is trained within the lower and upper limits of the parameters obtained in the first step. The outcomes of the two-step approach demonstrate significant improvement over the previous deterministic solutions. Plasticity ; Materials science ; Computation ; Surrogate model ; Inverse problem ; Inverse ; Titanium alloy ; Computer science ; Algorithm ; Machine learning ; Alloy ; Mathematics ; Geometry ; Mathematical analysis ; Composite material Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Sogol Lotfi | Ziyan Zhang | Gayatri Viswanathan | Kaitlyn Fortenberry | Aria Mansouri Tehrani | Jakoah Brgoch
Source: Matter (article, 2020-06-05)
Field: Materials Science — Materials Chemistry

Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis Convex hull ; Ternary operation ; Ternary plot ; Diagram ; Space (punctuation) ; Chemical space ; Composition (language) ; Function (biology) ; Construct (python library) ; Support vector machine ; Work (physics) ; Regular polygon ; Chemistry ; Mathematics ; Algorithm ; Materials science ; Computer science ; Machine learning ; Thermodynamics ; Physics ; Geometry ; Statistics ; Drug discovery ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Anus Manzoor | Dilpuneet S. Aidhy
Source: Materialia (article, 2020-06-24)
Field: Materials Science — Materials Chemistry

Predicting vibrational entropy of fcc solids uniquely from bond chemistry using machine learning Density functional theory ; Computation ; Materials science ; Enthalpy ; Thermodynamics ; Work (physics) ; Bond length ; Alloy ; Entropy (arrow of time) ; Chemical bond ; Bond ; Statistical physics ; Stability (learning theory) ; Molecule ; Computer science ; Algorithm ; Computational chemistry ; Machine learning ; Physics ; Chemistry ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Vadim Korolev | Artem Mitrofanov | Artem A. Eliseev | Valery Tkachenko
Source: Materials Horizons (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted search for functional materials over extended chemical space New computational framework has extended an inverse materials design over all the possible stoichiometric compounds. Inverse ; Space (punctuation) ; Stoichiometry ; Materials science ; Chemical space ; Computer science ; Nanotechnology ; Theoretical computer science ; Mathematics ; Chemistry ; Bioinformatics ; Physical chemistry ; Geometry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Mengyao Zhang | Jia Li | Ling Kang | Nan Zhang | Chun Huang | Yaqin He | Menghan Hu | Xiaofeng Zhou | Jian Zhang
Source: Nanoscale (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Machine learning-guided design and development of multifunctional flexible Ag/poly (amic acid) composites using the differential evolution algorithm The development of flexible composites is of great significance in the flexible electronic field. In combination with machine learning technology, the introduction of artificial intelligence to flexible materials design, synthesis, characterization and application research will greatly promote the flexible materials research efficiency. In this study, the back propagation (BP) neural network based on the differential evolution (DE) algorithm was applied to determine the electrical properties of the flexible Ag/poly (amic acid) (PAA) composite structure and to develop flexible materials for its different applications. In the machine learning model, the concentration of PAA, the ion exchange time of AgNO<sub>3</sub>, and the concentration and reduction time of NaBH<sub>4</sub> are set as input parameters, and the product of the sheet resistance of the Ag/PAA film and the processing time are set as output information. To overcome the situation whereby the BP neural network solution process could fall into the local optimum, the initial threshold and the weight of the BP neural network and the data import model are optimized by the DE algorithm. Utilizing 1077 learning samples and 49 predictive samples, a machine learning model with very high accuracy was established and relative errors of predictions less than 1.96% were achieved. In terms of this model, the optimized fabrication conditions of the Ag/PAA composites, which are suitable for strain sensors and electrodes, were predicted. To identify the availability and applicability of the proposed algorithm, a strain gauge sensor, a triboelectric nanogenerator (TENG) and a capacitive pressure sensor array were fabricated successfully using the optimized process parameters. This work shows that machine learning can be used to quickly optimize the process and provide guidance for material and process design, which is of significance for the development of flexible materials and devices. Materials science ; Algorithm ; Composite material ; Differential (mechanical device) ; Computer science ; Engineering Conducting polymers and applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Memory and Neural Computing
Authors: Kaichun Li | Ming‐Yen Lu | Hôǹg Thái Nguyêñ | Shih‐Wei Feng | S. B. Artemkina | В.Е. Федоров | Hsiang‐Chen Wang
Source: Nanomaterials (article, 2020-06-13)
Field: Materials Science — Materials Chemistry

Intelligent Identification of MoS2 Nanostructures with Hyperspectral Imaging by 3D-CNN Increasing attention has been paid to two-dimensional (2D) materials because of their superior performance and wafer-level synthesis methods. However, the large-area characterization, precision, intelligent automation, and high-efficiency detection of nanostructures for 2D materials have not yet reached an industrial level. Therefore, we use big data analysis and deep learning methods to develop a set of visible-light hyperspectral imaging technologies successfully for the automatic identification of few-layers MoS<sub>2</sub>. For the classification algorithm, we propose deep neural network, one-dimensional (1D) convolutional neural network, and three-dimensional (3D) convolutional neural network (3D-CNN) models to explore the correlation between the accuracy of model recognition and the optical characteristics of few-layers MoS<sub>2</sub>. The experimental results show that the 3D-CNN has better generalization capability than other classification models, and this model is applicable to the feature input of the spatial and spectral domains. Such a difference consists in previous versions of the present study without specific substrate, and images of different dynamic ranges on a section of the sample may be administered via the automatic shutter aperture. Therefore, adjusting the imaging quality under the same color contrast conditions is unnecessary, and the process of the conventional image is not used to achieve the maximum field of view recognition range of ~1.92 mm<sup>2</sup>. The image resolution can reach ~100 nm and the detection time is 3 min per one image. Hyperspectral imaging ; Convolutional neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Deep learning ; Computer vision ; Automation ; Artificial neural network ; Engineering 2D Materials and Applications ; Nanoplatforms for cancer theranostics ; Ga2O3 and related materials
Authors: Chonghuan Zhang | Yehia Amar | Liwei Cao | Alexei A. Lapkin
Source: Organic Process Research & Development (article, 2020-12-03)
Field: Materials Science — Materials Chemistry

Solvent Selection for Mitsunobu Reaction Driven by an Active Learning Surrogate Model Optimization of a synthetic reaction with respect to solvent choice and operating conditions was implemented as a machine learning-based workflow. The approach is exemplified on the case study of selection of a promising solvent to maximize the yield of a Mitsunobu reaction producing isopropyl benzoate. A solvent was defined with 15 molecular descriptors, and a library of solvent descriptors was built. The descriptors were converted into a reduced dimensionality form using an Autoencoder. Experimental yields were used to train a multilayered artificial neural network (ANN) surrogate model, which was used for the optimization and design of experiments (DoE). DoE was performed in an active learning mode to reduce the number of experiments required for reaction optimization. The final surrogate model identified 1-chloropentane as a promising solvent, which resulted in an experimental yield of 93%. Yield (engineering) ; Surrogate model ; Solvent ; Autoencoder ; Artificial neural network ; Design of experiments ; Chemistry ; Computer science ; Selection (genetic algorithm) ; Artificial intelligence ; Biological system ; Machine learning ; Organic chemistry ; Materials science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Xi Chen | C. Franklin Goldsmith
Source: The Journal of Physical Chemistry A (article, 2020-01-13)
Field: Materials Science — Materials Chemistry

Accelerating Variational Transition State Theory via Artificial Neural Networks An application of atomistic machine learning for variational transition state theory is presented. The rate constants for various radical-radical reactions are computed using variable reaction coordinate transition state theory. In order to simplify the calculation process, artificial neural networks are trained on a structured grid of potential energy data. The resulting surrogate potential energy surface is used in classical phase space representations to describe the interaction between two radical species in the gas phase. When the artificial neural network is trained to potential energy alone, the number of explicit electronic structure energy calculations required to compute a rate constant decreases by at least a factor of 4. When forces are included in the training data, the reduction is more dramatic-at least an order of magnitude. Artificial neural network ; State (computer science) ; Transition (genetics) ; Statistical physics ; Computer science ; Artificial intelligence ; Physics ; Algorithm ; Chemistry Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Heat Transfer and Boiling Studies
Authors: Ryo Tamura | Makoto Watanabe | Hiroaki Mamiya | Kota Washio | Masao Yano | Katsunori Danno | Akira Kato | Tetsuya Shoji
Source: Science and Technology of Advanced Materials (article, 2020-01-31)
Field: Materials Science — Materials Chemistry

Materials informatics approach to understand aluminum alloys The relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependence of the predicted properties on explanatory variables, that is, the type of heat treatment and element composition, is searched using a Markov chain Monte Carlo method. From the dependencies, a factor to obtain the desired properties is investigated. Using targets of 5000, 6000, and 7000 series aluminum alloys, we extracted relations that are difficult to find via simple correlation analysis. Our method is also used to design an experimental plan to optimize the materials properties while promoting the understanding of target materials. Materials informatics ; Materials science ; Aluminium ; Monte Carlo method ; Informatics ; Material properties ; Series (stratigraphy) ; Computer science ; Health informatics ; Metallurgy ; Mathematics ; Composite material ; Statistics ; Engineering Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Advanced Materials Characterization Techniques
Authors: Caroline Desgranges | Jérôme Delhommelle
Source: Molecular Systems Design & Engineering (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks Combined machine learning-molecular simulations protocols for the prediction and exploration of free energy surfaces. Metal-organic framework ; Thermodynamics ; Separation (statistics) ; Process engineering ; Computer science ; Materials science ; Chemistry ; Physics ; Engineering ; Physical chemistry ; Adsorption Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Jie Yang | Alejandro Gallegos | Cheng Lian | Shengwei Deng | Honglai Liu | Jianzhong Wu
Source: Chinese Journal of Chemical Engineering (article, 2020-12-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Curvature effects on electric-double-layer capacitance Electrolyte ; Capacitance ; Curvature ; Electrode ; Materials science ; Capacitor ; Density functional theory ; Porosity ; Capacitive sensing ; Layer (electronics) ; Composite material ; Voltage ; Chemistry ; Geometry ; Computer science ; Computational chemistry ; Mathematics ; Electrical engineering Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Battery Materials and Technologies
Authors: Gareth W. Richings | Scott Habershon
Source: The Journal of Physical Chemistry A (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes We have recently shown how high-accuracy wave function grid-based propagation schemes, such as the multiconfiguration time-dependent Hartree (MCTDH) method, can be combined with machine-learning (ML) descriptions of PESs to yield an "on-the-fly" direct dynamics scheme which circumvents potential energy surface (PES) prefitting. To date, our approach has been demonstrated in the ground-state dynamics and nonadiabatic spin-allowed dynamics of several molecular systems. Expanding on this successful previous work, this Article demonstrates how our ML-based quantum dynamics scheme can be adapted to model nonadiabatic dynamics for spin-forbidden processes such as intersystem crossing (ISC), opening up new possibilities for modeling chemical dynamic phenomena driven by spin-orbit coupling. After describing modifications to diabatization schemes to enable accurate and robust treatment or electronic states of different spin-multiplicity, we demonstrate our methodology in applications to modeling ISC in SO<sub>2</sub> and thioformaldehyde, benchmarking our results against previous trajectory- and grid-based calculations. As a relatively efficient tool for modeling spin-forbidden nonadiabatic dynamics without demanding any prefitting of PESs, our overall strategy is a potentially powerful tool for modeling important photochemical systems, such as photoactivated pro-drugs and organometallic catalysts. Hartree ; Surface hopping ; Potential energy ; Vibronic coupling ; Intersystem crossing ; Statistical physics ; Wave function ; Potential energy surface ; Spin (aerodynamics) ; Quantum dynamics ; Grid ; Quantum ; Physics ; Excited state ; Quantum mechanics ; Molecule ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Di Lu | YU Wei-dong
Source: Textile Research Journal (article, 2020-08-17)
Field: Materials Science — Polymers and Plastics

Predicting the tensile strength of single wool fibers using artificial neural network and multiple linear regression models based on acoustic emission The acoustic emission (AE) technique is widely used at the present time for almost any kind of material characterization. The main aim of the present study was to predict the tensile strength of wool by using artificial neural networks and multiple linear regression analysis based on AE detection. With this aim, a number of single wool fibers were stretched to fracture and the signals at break were recorded by the AE technique. The energy, amplitude, duration, number of hits, average rectified value and root mean square value were used as input parameters to predict the strength of the wool. A feed-forward neural network with a backpropagation (BP) algorithm was successfully trained and tested using the measured data. The same input parameters were used by multiple stepwise regression models for the estimation of wool strength. The coefficients of determination of the BP neural network and stepwise regression indicate that there is a strong correlation between the measured and predicted strength of wool with an acceptable error value. The comparative analysis of the two modeling techniques shows that the neural network performs better than the stepwise regression models. Meanwhile, the relative importance of the input parameters was determined by using rank analysis. The prediction models established in the present work can be applied to AE studies of fiber bundles or fiber-reinforced composite materials. Artificial neural network ; Backpropagation ; Linear regression ; Wool ; Ultimate tensile strength ; Regression analysis ; Stepwise regression ; Mean squared error ; Regression ; Approximation error ; Root mean square ; Biological system ; Materials science ; Mathematics ; Statistics ; Computer science ; Composite material ; Engineering ; Machine learning Textile materials and evaluations ; Mechanical Behavior of Composites ; Dyeing and Modifying Textile Fibers
Authors: Ping Wang | Yuan Zhang | Entao Yao | Mi Yan | Yang Zheng | Chenglong Tang
Source: Measurement (article, 2020-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Method of measuring the mechanical properties of ferromagnetic materials based on magnetostrictive EMAT characteristic parameters Electromagnetic acoustic transducer ; Magnetostriction ; Materials science ; Ferromagnetism ; Acoustics ; Inverse magnetostrictive effect ; Magnetic field ; Transducer ; Microstructure ; Composite material ; Ultrasonic sensor ; Condensed matter physics ; Ultrasonic testing ; Physics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels