jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Takashi Kojima | Takashi Washio | Satoshi Hara | Masataka KOISHI
Source: Scientific Reports (article, 2020-10-22)
Field: Materials Science — Materials Chemistry

Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber Molecular dynamics (MD) simulation is used to analyze the mechanical properties of polymerized and nanoscale filled rubber. Unfortunately, the computation time for a simulation can require several months' computing power, because the interactions of thousands of filler particles must be calculated. To alleviate this problem, we introduce a surrogate convolutional neural network model to achieve faster and more accurate predictions. The major difficulty when employing machine-learning-based surrogate models is the shortage of training data, contributing to the huge simulation costs. To derive a highly accurate surrogate model using only a small amount of training data, we increase the number of training instances by dividing the large-scale simulation results into 3D images of middle-scale filler morphologies and corresponding regional stresses. The images include fringe regions to reflect the influence of the filler constituents outside the core regions. The resultant surrogate model provides higher prediction accuracy than that trained only by images of the entire region. Afterwards, we extract the fillers that dominate the mechanical properties using the surrogate model and we confirm their validity using MD. Surrogate model ; Economic shortage ; Computer science ; Filler (materials) ; Convolutional neural network ; Natural rubber ; Scale (ratio) ; Computation ; Artificial intelligence ; Artificial neural network ; Machine learning ; Materials science ; Algorithm ; Composite material Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Rheology and Fluid Dynamics Studies
Authors: Charles D. Griego | Lingyan Zhao | Karthikeyan Saravanan | John A. Keith
Source: AIChE Journal (article, 2020-09-03)
Field: Materials Science — Materials Chemistry

Machine learning corrected alchemical perturbation density functional theory for catalysis applications Abstract Alchemical perturbation density functional theory (APDFT) has promise for enabling computational screening of hypothetical catalyst sites. Here, we analyze errors in first order APDFT calculation schemes for binding energies of CH x , NH x , OH x , and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then train three different support vector regression machine learning models that correct systematic APDFT prediction errors for each of the three classes of carbon, nitrogen, and oxygen based adsorbates. While uncorrected first order APDFT alone approximates accurate adsorbate binding energies on up to 36 hypothetical alloys based on a single Kohn–Sham DFT calculation on a 3 × 3 unit cell for Pt(111), the machine learning‐corrected APDFT extends this number to more than 20,000 and provides a recipe for developing other machine learning‐based APDFT models. Density functional theory ; Perturbation theory (quantum mechanics) ; Perturbation (astronomy) ; Support vector machine ; Chemistry ; Algorithm ; Computer science ; Statistical physics ; Physics ; Computational chemistry ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jonathan Fine | Judy Kuan‐Yu Liu | Armen G. Beck | Kawthar Z. Alzarieni | Xin Ma | Victoria M. Boulos | Hilkka I. Kenttämaa | Gaurav Chopra
Source: Chemical Science (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Graph-based machine learning interprets and predicts diagnostic isomer-selective ion–molecule reactions in tandem mass spectrometry Diagnostic ion-molecule reactions employed in tandem mass spectrometry experiments can frequently be used to differentiate between isomeric compounds unlike the popular collision-activated dissociation methodology. Selected neutral reagents, such as 2-methoxypropene (MOP), are introduced into an ion trap mass spectrometer where they react with protonated analytes to yield product ions that are diagnostic for the functional groups present in the analytes. However, the understanding and interpretation of the mass spectra obtained can be challenging and time-consuming. Here, we introduce the first bootstrapped decision tree model trained on 36 known ion-molecule reactions with MOP. It uses the graph-based connectivity of analytes' functional groups as input to predict whether the protonated analyte will undergo a diagnostic reaction with MOP. A Cohen kappa statistic of 0.70 was achieved with a blind test set, suggesting substantial inter-model reliability on limited training data. Prospective diagnostic product predictions were experimentally tested for 13 previously unpublished analytes. We introduce chemical reactivity flowcharts to facilitate chemical interpretation of the decisions made by the machine learning method that will be useful to understand and interpret the mass spectra for chemical reactivity. Mass spectrometry ; Molecule ; Tandem mass spectrometry ; Reactivity (psychology) ; Chemistry ; Graph ; Ion ; Flowchart ; Tandem ; Computer science ; Computational chemistry ; Organic chemistry ; Chromatography ; Materials science ; Theoretical computer science ; Programming language Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies
Authors: Joshua L. Lansford | Dionisios G. Vlachos
Source: ACS Nano (article, 2020-11-16)
Field: Materials Science — Materials Chemistry

Spectroscopic Probe Molecule Selection Using Quantum Theory, First-Principles Calculations, and Machine Learning Probe molecule vibrational spectra have a long history of being used to characterize materials including metals, oxides, metal-organic frameworks, and even human proteins. Furthermore, recent advances in machine learning have enabled computationally generated spectra to aid in detailed characterization of complex surfaces with probe molecules. Despite widespread use of probe molecules, the science of probe molecule selection is underdeveloped. Here, we develop physical concepts, including orbital interaction energy and the energy overlap integral, to explain and predict the ability of probe molecules to discriminate structural descriptors. We resolve the crystal orbital overlap population (COOP) to specific molecular orbitals and quantify their bonding character, which directly influences vibrational frequencies. Using only a single adsorbate calculation from density function theory (DFT), we compute the interaction energy of individual adsorbate molecular orbitals with adsorption site atomic orbitals across many different sites. Combining the molecular orbital resolved COOP and changes in orbital interaction energy enables probe molecule selection for improved discrimination of various sites. We demonstrate these concepts by comparing the predicted effectiveness of carbon monoxide (CO), nitric oxide (NO), and ethylene (C<sub>2</sub>H<sub>4</sub>) to probe Pt adsorption sites. Finally, using a previously developed machine learning framework, we show that models trained on hundreds of thousands of C<sub>2</sub>H<sub>4</sub> spectra, computed from DFT, which regress surface binding-type and generalized coordination number, outperform those trained using CO and NO spectra. A python package, pDOS_overlap, for implementing the electron density-based analysis on any combination of adsorbates and materials, is also made available. Density functional theory ; Atomic orbital ; Molecular orbital ; Orbital overlap ; Chemical physics ; Molecule ; Chemistry ; Computational chemistry ; Molecular physics ; Materials science ; Physics ; Electron ; Quantum mechanics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Computational Drug Discovery Methods
Authors: Gary Whelan | David L. McDowell
Source: Integrating materials and manufacturing innovation (article, 2020-11-18)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Uncertainty Quantification for Modeling Structure–Property Linkages for Fatigue Critical Engineering Alloys Using an ICME Workflow Uncertainty quantification ; Workflow ; Computer science ; Property (philosophy) ; Process (computing) ; Engineering design process ; Work (physics) ; Gaussian process ; New product development ; Industrial engineering ; Systems engineering ; Machine learning ; Mechanical engineering ; Gaussian ; Engineering Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Manufacturing Process and Optimization
Authors: Colin A. Grambow | Lagnajit Pattanaik | William H. Green
Source: ChemRxiv (preprint, 2020-03-31)
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 ; Atom (system on chip) ; Chemical reaction ; Quantum chemistry ; Quantum ; Computational chemistry ; Potential energy surface ; Quantum chemical ; Organic reaction ; Reaction rate ; Computer science ; Molecule ; Physics ; Catalysis ; Organic chemistry ; Kinetics ; Quantum mechanics ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Guilherme Banin | Roniérik Pioli Vieira | Liliane Maria Ferrareso Lona
Source: Chemical Engineering Journal (article, 2020-09-17)
Field: Chemistry — Organic Chemistry

Artificial neural networks towards average properties targets in styrene ARGET-ATRP Dispersity ; Polystyrene ; Artificial neural network ; Polymer ; Monomer ; Styrene ; Materials science ; Computer science ; Polymerization ; Nanotechnology ; Copolymer ; Polymer chemistry ; Artificial intelligence Advanced Polymer Synthesis and Characterization ; Fuel Cells and Related Materials ; Machine Learning in Materials Science
Authors: Jinkui Wu | Shihui Wang | Li Zhou | Xu Ji | Yiyang Dai | Yagu Dang | Markus Kraft
Source: Industrial & Engineering Chemistry Research (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Deep-Learning Architecture in QSPR Modeling for the Prediction of Energy Conversion Efficiency of Solar Cells The efficient and effective design of chemical processes and products heavily relies on the accurate prediction of essential properties. In this work, a deep-learning architecture integrating a bidirectional long short-term memory (Bi-LSTM) network, an attention mechanism, and a back-propagation neural network (BPNN) is developed for the prediction of energy conversion efficiency of organic solar cells. Inspired by the success of artificial intelligence in natural language processing, we first developed a novel strategy for molecular signature encoding and information embedding in order to depict the compositional structures of molecules. Then, an advanced recurrent neural network named Bi-LSTM is employed to process the molecular information, while the BPNN is applied to correlate energy conversion efficiency. During this procedure, the attention mechanism is used to identify molecular constituents that are important to the property of interest. To evaluate the performance of the proposed approach, the energy conversion efficiencies of more than 20,000 organic photovoltaics are used to train and test the model. Result comparisons with several other modeling approaches indicate that the proposed method is competitive in prediction accuracy and possesses good transferability to small data sets. Additionally, the proposed method is capable of identifying decisive molecular constituents, providing instructive information for the reverse design of organic solar cells. Computer science ; Artificial intelligence ; Artificial neural network ; Deep learning ; Process (computing) ; Backpropagation ; Efficient energy use ; Embedding ; Solar energy ; Machine learning ; Biological system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Economic and Technological Innovation
Authors: Brian DeCost | Jason Hattrick‐Simpers | Zachary Trautt | A. Gilad Kusne | Eva M. Campo | Martin A. Green
Source: Machine Learning Science and Technology (preprint, 2020-06-05)
Field: Materials Science — Materials Chemistry

Scientific AI in materials science: a path to a sustainable and scalable paradigm Abstract Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI (SciAI) to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical ( e.g. development of robust, physically meaningful multiscale material representations) to social ( e.g. promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward. Leverage (statistics) ; Scalability ; Workforce ; Computer science ; Data science ; Key (lock) ; Engineering ethics ; Management science ; Knowledge management ; Artificial intelligence ; Engineering ; Political science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: A. K. Gupta | Chih-Hsien Hsu | Chao‐Sung Lai
Source: IEEE photonics journal (article, 2020-08-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancement of the Au/ZnO-NA Plasmonic SERS Signal Using Principal Component Analysis as a Machine Learning Approach In this work, we modeled a novel approach to enhance surface-enhanced Raman scattering (SERS) signals using principal component analysis (PCA) as a machine learning approach. Zinc oxide nanoarrays (ZnO-NAs) were synthesized using a hydrothermal method followed by zinc oxide nucleation on ITO glass substrates via an oxidation furnace at 500 °C. The surface morphology was improved by short rapid thermal annealing (S-RTA) after deposition of a gold layer via a thermal evaporator to avoid chemical contamination of the sensing surface, which is a suitable plasmonic platform for the generation of “hot spots” for SERS enhancement with fewer defects. The proposed Au/ZnO-NA SERS sensor exhibited an enhancement factor (EF) of 1.15 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> via the R6G Raman probe and excellent uniformity over the entire surface. The PCA algorithm was used to extract useful features and information from the SERS signal. The algorithm was implemented with MATLAB software (R2019a) by the multivariable analytical tool to find an enhanced signal (~3 times higher) with high uniformity, which has great potential and is applicable to a wide range of probe molecules suitable in medical, safety, and environmental applications. Raman scattering ; Materials science ; Principal component analysis ; Raman spectroscopy ; Optoelectronics ; Analytical Chemistry (journal) ; Computer science ; Optics ; Artificial intelligence ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection
Authors: Mookala Premasudha | Srinivasulu Reddy Bhumi Reddy | Yeon‐Ju Lee | Bharat B. Panigrahi | Kwon‐Koo Cho | Subba Reddy Nagireddy Gari
Source: Journal of Applied Polymer Science (article, 2020-10-04)
Field: Materials Science — Biomaterials

Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon <scp>VII</scp> starch) nanofiber diameter Abstract Present work was aimed to develop an artificial neural networks (ANN) model to predict the polysaccharide‐based biopolymer (Hylon VII starch) nanofiber diameter and classification of its quality (good, fair, and poor) as a function of polymer concentration, spinning distance, feed rate, and applied voltage during the electrospinning process. The relationship between diameter and its quality with process parameters is complex and nonlinear. The backpropagation algorithm was used to train the ANN model and achieved the classification accuracy, precision, and recall of 93.9%, 95.2%, and 95.2%, respectively. The average errors of the predicted fiber diameter for training and unseen testing data were found to be 0.05% and 2.6%, respectively. A stand‐alone ANN software was designed to extract information on the electrospinning system from a small experimental database. It was successful in establishing the relationship between electrospinning process parameters and fiber quality and diameter. The yield of smaller diameter with good quality was favored by lower feed rate, lower polymer solution concentration, and higher applied voltage. Electrospinning ; Nanofiber ; Materials science ; Starch ; Artificial neural network ; Backpropagation ; Polymer ; Fiber ; Composite material ; Artificial intelligence ; Biological system ; Chemical engineering ; Computer science ; Chemistry ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Natural Fiber Reinforced Composites
Authors: David Montes de Oca Zapiain | Matthew Guziewski | Shawn P. Coleman | Rémi Dingreville
Source: The Journal of Physical Chemistry C (article, 2020-11-03)
Field: Materials Science — Materials Chemistry

Characterizing the Tensile Strength of Metastable Grain Boundaries in Silicon Carbide Using Machine Learning The local atomic structure, local chemistry, and stoichiometry of grain boundaries control in part the strength and fracture toughness of silicon carbide components. The predictions of the structure and properties of these grain boundaries are generally limited to their ground-state configurations. We investigated the tensile strength behavior of metastable grain boundaries in silicon carbide using high-throughput atomistic simulations combined with machine learning techniques. We analyzed and compared the ∑5 ⟨100⟩{120} and ∑9 ⟨110⟩{122} tilt grain boundary metastable configurations to identify structural and chemical attributes that dominate their tensile strength. We characterized these metastable grain boundaries using a set of microscopic descriptors representing the local grain boundary atomic structure and the local grain boundary stoichiometry and chemical-bound types. We used a boosted regression tree surrogate model for the successful prediction of metastable grain boundary strength as a function of these descriptors. Our results show that the tensile strength of generic (i.e., any random grain boundary from the entire grain boundary population), metastable grain boundaries is primarily dominated by the grain boundary excess free volume, closely followed by the type of structure composing the boundary and the amount of C–C bonds. The 5% strongest metastable grain boundaries have particular characteristics with a low amount of free volume and the highest density of C–C bonds. Our results reveal that the 5% strongest and weakest metastable grain boundaries are most sensitive to the local stoichiometry, regardless of the local atomic structure composing the grain boundary as compared to any other generic metastable grain boundaries. We show that the strongest and weakest metastable grain boundary configurations can be identified as specific regions in a low-dimensional-representation space of their microscopic descriptors. Taken together, these findings showcase the effectiveness and validity of using a low-dimensional representation of the grain boundary structure and machine-learned surrogate models to rapidly assess metastable grain boundary strength without the need to perform actual tensile simulations. Grain boundary ; Materials science ; Grain boundary strengthening ; Metastability ; Ultimate tensile strength ; Grain boundary diffusion coefficient ; Carbide ; Crystallography ; Condensed matter physics ; Metallurgy ; Microstructure ; Chemistry ; Physics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: James L. McDonagh | William C. Swope | R. L. Anderson | Michael A. Johnston | David J. Bray
Source: Polymer International (article, 2020-05-31)
Field: Materials Science — Materials Chemistry

What can digitisation do for formulated product innovation and development? Abstract Digitisation offers significant opportunities for the formulated product industry to transform the way it works. Recent developments in the fields of artificial intelligence, machine learning, robotics and high performance computing present disruptive and transformative methods to accelerate the R&amp;D process. These methods include physics‐based chemical simulation, data driven models and hybrid approaches, each of which has been shown to provide new physical and chemical insights that can augment experimental R&amp;D. However, successfully exploiting these technologies in R&amp;D organisations at scale is challenging due to the high level of domain specialisation required and the need for novel models for the formulation domain. In this paper we give an overview of some of the digital technology demonstrators we have developed for formulated product R&amp;D. These demonstrators focus on computational modelling methods. In particular we discuss how we tackled the issue of domain specialisation and the challenges we encountered in creating accurate models for formulation problems. © 2020 Society of Chemical Industry Transformative learning ; Domain (mathematical analysis) ; Computer science ; New product development ; Artificial intelligence ; Product (mathematics) ; Process (computing) ; Focus (optics) ; Robotics ; Data science ; Scale (ratio) ; Industrial engineering ; Engineering ; Robot ; Management ; Mathematics ; Physics Machine Learning in Materials Science ; Process Optimization and Integration ; Scientific Computing and Data Management