jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mladenka Pestorić | Jasna Mastilović | Žarko Kevrešan | Lato Pezo | Miona Belović | Svetlana Glogovac | Dubravka Škrobot | Nebojša Ilić | Adam Takač
Source: Food and Feed Research (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Artificial neural network model in predicting the quality of fresh tomato genotypes Sensory analysis is the best mean to precisely describe the eating quality of fresh foods. However, it is expensive and time-consuming method which cannot be used for measuring quality properties in real time. The aim of this paper was to contribute to the study of the relationship between sensory and instrumental data, and to define a proper model for predicting sensory properties of fresh tomato through the determination of the physicochemical properties. Principal Component Analysis (PCA) was applied to the experimental data to characterize and differentiate among the observed genotypes, explaining 73.52% of the total variance, using the first three principal components. Artificial neural network (ANN) model was used for the prediction of sensory properties based on the results obtained by basic chemical and instrumental determinations. The developed ANN model predicts the sensory properties with high adequacy, with the overall coefficient of determination of 0.859. Principal component analysis ; Artificial neural network ; Sensory system ; Artificial intelligence ; Variance (accounting) ; Pattern recognition (psychology) ; Quality (philosophy) ; Statistics ; Mathematics ; Computer science ; Biological system ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Postharvest Quality and Shelf Life Management
Authors: Dylan H. Ross | Ryan P. Seguin | Allison M. Krinsky | Libin Xu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-05-15)
Field: Chemistry — Spectroscopy

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites Abstract Drug metabolite identification is a bottleneck of drug metabolism studies. Ion mobility-mass spectrometry (IM-MS) enables the measurement of collision cross section (CCS), a unique physical property related to an ion’s gas-phase size and shape, which can be used to increase the confidence in the identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we present the production of a large-scale database of drug and drug metabolite CCS values, assembled using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors. This novel prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers for the first time. Metabolite ; Bottleneck ; Drug discovery ; Identification (biology) ; Throughput ; Collision ; Computer science ; Cross-validation ; Drug ; Chemistry ; Biological system ; Computational biology ; Machine learning ; Biology ; Pharmacology ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Soroush Soltani | Taha Roodbar Shojaei | Nasrin Khanian | Thomas Shean Yaw Choong | Nilofar Asim | Umer Rashid
Source: Chemical Engineering & Technology (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Porosity Estimation of Mesoporous TiO<sub>2</sub>‐ZnO Nanocrystalline by Artificial Neural Network Modeling Abstract Artificial neural network was introduced for modeling and verifying the relative significance of operational variables to attain the best possible average pore diameter ( D p ) of the mesoporous TiO 2 ‐ZnO nanocrystalline synthesized by microwave‐assisted hydrothermal method. To optimize the system, the autonomous variables calcination temperature and concentrations of D ‐glucose, polyethylene glycol, and Zn‐Ti were employed as input factors, D p was designated as output. D p was achieved from the implementation of the experimental model of the variables by the Barrett‐Joyner‐Halenda method. The network was trained by IBP, BBP, LM, QP, and GA algorithms as a model. The BBP‐4‐7‐1 model provided the optimum numerical properties among four other algorithms. Furthermore, the calcination temperature acted as the most efficient variable whilst D ‐glucose concentration showed the least effect. By validating the microwave‐assisted hydrothermal conditions, in situ polymerization of poly(sodium 4‐styrene sulfonate) was introduced to activate mesopore walls by –SO 3 H functional groups used for ester production by transesterification of used cooking oil, resulting in a methyl ester content of 97.4 % and excellent reusability for ten sequential reactions without any further regeneration. Mesoporous material ; Calcination ; Nanocrystalline material ; Materials science ; Chemical engineering ; Polymerization ; Porosity ; Hydrothermal circulation ; Polyethylene glycol ; Artificial neural network ; Hydrothermal synthesis ; Transesterification ; Nanotechnology ; Chemistry ; Computer science ; Organic chemistry ; Composite material ; Catalysis ; Machine learning ; Polymer Mesoporous Materials and Catalysis ; Water Quality Monitoring and Analysis ; Catalytic Processes in Materials Science
Authors: S. Mohammadzadeh | E. Lejeune
Source: arXiv (Cornell University) (preprint, 2021-07-24)
Field: Materials Science — Materials Chemistry

Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based Metamodels Using simulation to predict the mechanical behavior of heterogeneous materials has applications ranging from topology optimization to multi-scale structural analysis. However, full-fidelity simulation techniques such as Finite Element Analysis can be prohibitively computationally expensive when they are used to explore the massive input parameter space of heterogeneous materials. Therefore, there has been significant recent interest in machine learning-based models that, once trained, can predict mechanical behavior at a fraction of the computational cost. Over the past several years, research in this area has been focused mainly on predicting single Quantities of Interest (QoIs). However, there has recently been an increased interest in a more challenging problem: predicting full-field QoI (e.g., displacement/strain fields, damage fields) for mechanical problems. Due to the added complexity of full-field information, network architectures that perform well on single QoI problems may perform poorly in the full-field QoI problem setting. The work presented in this paper is twofold. First, we made a significant extension to the Mechanical MNIST dataset designed to enable the investigation of full field QoI prediction. Specifically, we added Finite Element simulation results of quasi-static brittle fracture in a heterogeneous material captured with the phase-field method. Second, we established strong baseline performance for predicting full-field QoI with MultiRes-WNet architecture. In addition to presenting the results in this paper, we have released our model implementation and the Mechanical MNIST Crack Path dataset under open-source licenses. We anticipate that future researchers will directly use our model architecture on related datasets and potentially design models that exceed the baseline performance for predicting full-field QoI established in this paper. Computer science ; MNIST database ; Field (mathematics) ; Finite element method ; Path (computing) ; High fidelity ; Machine learning ; Fidelity ; Artificial intelligence ; Algorithm ; Computational science ; Deep learning ; Engineering ; Structural engineering ; Mathematics Machine Learning in Materials Science ; Infrastructure Maintenance and Monitoring ; Software Engineering Research
Authors: Xiaoyang Wang | Yinan Wang | LinFeng Zhang | Dai Fuzhi | Handong Wang
Source: arXiv (Cornell University) (preprint, 2021-11-08)
Field: Materials Science — Materials Chemistry

A Tungsten Deep Potential with High Accuracy and Generalization Ability based on a Newly Designed Three-body Embedding Formalism. Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD)simulations reveal the atomisttic scale mechanisms, so they are crucial for the understanding ofthe macroscopic property deterioration of tungsten under harsh and complex service environment.The interatomic potential used in the MD simulations is required to accurately describe a widespectrum of relevant defect properties, which is by far challenging to the existing interatomicpotentials. In this paper, we propose a new three-body embedding descriptor and hybridize it intothe Deep-Potential (DP) framework, an end-to-end deep learning interatomic potential model.Trained with the dataset generated by a concurrent learning method, the potential model fortungsten, named by DP-HYB, is able to accurately predict a wide range of properties includingelastic constants, the formation energies of free surfaces, grain boundaries, point defects and defectclusters, stacking fault energies, the core structure of screw dislocation, the energy barrier and thetransition path of the screw dislocation migration. Since most of the properties are not explicitlyincluded in the training dataset, the strong generalizability of the DP-HYB model indicates thatit is a good candidate for the atomistic simulations of tungsten property deterioration, especiallythose involving the mechanical property changing under the harsh service environment. Stacking-fault energy ; Tungsten ; Molecular dynamics ; Interatomic potential ; Materials science ; Embedding ; Artificial intelligence ; Generalizability theory ; Computer science ; Statistical physics ; Stacking ; Atomic units ; Dislocation ; Algorithm ; Physics ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Majid Fathpour | Afshin Ebrahimi | Aliyeh Ghamkhari | Ali Shahbazi | Elmira Abbasi
Source: Iranian Journal of Science and Technology Transactions of Civil Engineering (article, 2021-10-29)
Field: Chemistry — Inorganic Chemistry

Comparison of Modeling and Optimization of Methanol to Propylene (MTP) Over High Silica H-ZSM-5 NANO Catalyst Using Black-Box Modeling (ANN) and Meta-heuristic Optimizers (GA-PSO) Particle swarm optimization ; Artificial neural network ; Radial basis function ; Multilayer perceptron ; Genetic algorithm ; Black box ; Computer science ; Biological system ; Algorithm ; Materials science ; Artificial intelligence ; Machine learning Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Mesoporous Materials and Catalysis
Authors: Donald Loveland | Shusen Liu | Bhavya Kailkhura | Anna M. Hiszpanski | Yong Han
Source: arXiv (Cornell University) (preprint, 2021-06-25)
Field: Materials Science — Materials Chemistry

Reliable Graph Neural Network Explanations Through Adversarial Training Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection. While this has been deemed successful, many post-hoc explanation methods have been shown to fail in capturing a model's learned representation. Due to this problem, it is worthwhile to consider how one might train a model so that it is more amenable to post-hoc analysis. Given the success of adversarial training in the computer vision domain to train models with more reliable representations, we propose a similar training paradigm for GNNs and analyze the respective impact on a model's explanations. In instances without ground truth labels, we also determine how well an explanation method is utilizing a model's learned representation through a new metric and demonstrate adversarial training can help better extract domain-relevant insights in chemistry. Adversarial system ; Computer science ; Artificial intelligence ; Machine learning ; Introspection ; Post hoc ; Representation (politics) ; Metric (unit) ; Artificial neural network ; Deep neural networks ; Domain (mathematical analysis) ; Graph ; Training set ; Training (meteorology) ; Theoretical computer science ; Mathematics ; Cognitive psychology ; Psychology Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Kuo Yuan | Cheulhee Jung | Yuan Chen | Hung‐Che Kuo | Oliver Zhao | Trung D. Nguyen | James R. Rybarski | Soonwoo Hong | Yuan‐I Chen | Dennis Wylie | John A. Hawkins | Jada N. Walker | Samuel W. J. Shields | Jennifer S. Brodbelt | Jeffrey T. Petty | Ilya J. Finkelstein | Hsin‐Chih Yeh
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-12-05)
Field: Materials Science — Materials Chemistry

Massively Parallel Selection of NanoCluster Beacons NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed the polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here we report a high-throughput selection method that takes advantage of repurposed next-generation-sequencing (NGS) chips to screen the activation fluorescence of ∼40,000 activator sequences. We find the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow and red POTs, respectively. Based on these findings, we propose a “zipper bag model” that explains how these hotspots lead to the creation of distinct silver cluster chromophores and contribute to the difference in chromophore chemical yields. Combining high-throughput screening with machine learning algorithms, we establish a pipeline to rationally design bright and multicolor NCBs. Activator (genetics) ; Molecular beacon ; Beacon ; Massively parallel ; Computer science ; Throughput ; Fluorescence ; Nucleobase ; Chromophore ; DNA ; Computational biology ; Chemistry ; Nanotechnology ; Materials science ; Biology ; Physics ; Gene ; Parallel computing ; Wireless ; Computer network ; Photochemistry ; Telecommunications ; Biochemistry Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Fergany Badry
Source: OakTrust (Texas A&M University Libraries) (article, 2021-01-08)
Field: Materials Science — Materials Chemistry

A Hybrid Physics-Based and Data-Driven Approach for Predicting the Effective Thermal Conductivity of Heterogeneous Solids Thermal conductivity is one of the most important physical properties of materials. It plays a significant role in operation, performance and efficiency of the nuclear reactors. This study introduces a novel model for the effective thermal conductivity of polycrystalline solids based on the thin-interface description of grain boundaries (GBs). In contrast to existing models, the new model treats a GB as an autonomous “phase” with its own thermal conductivity. The Kapitza resistance/conductance of a thin interface is then derived in terms of the interface thermal conductivity and width. The predictions of the new model deviate from the corresponding ones from existing models by 1-100% as the grain size approaches the GB width. The development and implementation of two quantitative mesoscale models for the effective thermal conductivity of two important types of nuclear fuels are undertaken. These models account for the effects of temperature, underlying microstructure, and interface thermal resistance for calculating the effective thermal conductivity. High-fidelity finite-element simulations were conducted to validate the predictions of the developed models. These simulations proved the higher accuracy of the developed models. Lastly, to reduce the required computational power, advanced machine learning algorithms were integrated with the validated mesoscale models. This approach is novel and significantly saved the running time and computational cost. The advantages and limitations of the developed models are summarized, and some future directions are highlighted. Thermal conductivity ; Statistical physics ; Physics ; Computer science ; Materials science ; Thermodynamics Machine Learning in Materials Science ; Thermal properties of materials ; Phase Equilibria and Thermodynamics
Authors: Zara Moleinia | David F. Bahr
Source: Materials (article, 2021-01-18)
Field: Materials Science — Materials Chemistry

Multi-Scale Analyses and Modeling of Metallic Nano-Layers The current work centers on multi-scale approaches to simulate and predict metallic nano-layers' thermomechanical responses in crystal plasticity large deformation finite element platforms. The study is divided into two major scales: nano- and homogenized levels where Cu/Nb nano-layers are designated as case studies. At the nano-scale, a size-dependent constitutive model based on entropic kinetics is developed. A deep-learning adaptive boosting technique named single layer calibration is established to acquire associated constitutive parameters through a single process applicable to a broad range of setups entirely different from those of the calibration. The model is validated through experimental data with solid agreement followed by the behavioral predictions of multiple cases regarding size, loading pattern, layer type, and geometrical combination effects for which the performances are discussed. At the homogenized scale, founded on statistical analyses of microcanonical ensembles, a homogenized crystal plasticity-based constitutive model is developed with the aim of expediting while retaining the accuracy of computational processes. Accordingly, effective constitutive functionals are realized where the associated constants are obtained via metaheuristic genetic algorithms. The model is favorably verified with nano-scale data while accelerating the computational processes by several orders of magnitude. Ultimately, a temperature-dependent homogenized constitutive model is developed where the effective constitutive functionals along with the associated constants are determined. The model is validated by experimental data with which multiple demonstrations of temperature effects are assessed and analyzed. Constitutive equation ; Materials science ; Calibration ; Nano- ; Plasticity ; Scale (ratio) ; Experimental data ; Finite element method ; Statistical physics ; Biological system ; Thermodynamics ; Mathematics ; Composite material ; Statistics ; Physics Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; Metallurgy and Material Forming
Authors: S. R. Weerakoon | S. Somaratne
Source: Asian Journal of Agriculture (article, 2021-03-21)
Field: Chemistry — Analytical Chemistry

Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM) Abstract. Weerakoon SR, Somaratne S. 2021. Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM). Asian J Agric 5: 35-43. Weedy rice (WR) (Oryza sativa f. spontanea) has become a major threat in rice cultivation. Discrimination of WR from cultivated rice is difficult since agro-morphology of WR and cultivated rice are overlapping. Molecular markers are useful and can be an informative tool for estimating genetic diversity and relationships in closely related WR eco-types. Self-Organizing Maps (SOM) is an interesting and promising classification tool employing an innovative and data-driven classification method based on unsupervised artificial neural networks. The present study focused on exploring the potential use of SOM to classify WR populations of different eco-climatic zones in Sri Lanka using agro-morphological and molecular data. Separate SOMs for each set of variables, agro-morphological and molecular data were developed. The best SOM was chosen based on the error performance. Findings of SOM analyses showed that certain morphological characters (seedling height, leaf blade width, leaf blade length, culm strength, panicle shattering, seed coat color and leaf angle) and certain molecular characters detected from SSR primers (RM 11, RM 21, RM 14, and RM 280) are important in separation of different WR eco-types satisfactorily. SOM clustering of cultivated, wild, and WR eco-types indicated specific patterns of grouping with respect to climatic conditions of the country. WR eco-types in dry zone and wet zone of the country are closely related to Oryza nivara and O. rufipogon respectively. Sri lanka ; Oryza sativa ; Panicle ; Weedy rice ; Biology ; Genetic diversity ; Seedling ; Agronomy ; Botany ; Geography ; Population ; Gene Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Hang Hu | Jyothsna Padmakumar Bindu | Julia Laskin
Source: ChemRxiv (preprint, 2021-08-09)
Field: Chemistry — Spectroscopy

Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep networks. Herein, we introduce a self-supervised clustering approach based on contrastive learning, which shows an excellent performance in clustering of small MSI data. We train a deep convolutional neural network (CNN) using MSI data from a single experiment without manual annotations to effectively learn high-level spatial features from ion images and classify them based on molecular colocalizations. We demonstrate that contrastive learning generates ion image representations that form well-resolved clusters. Subsequent self-labeling is used to fine-tune both the CNN encoder and linear classifier based on confidently classified ion images. This new approach enables autonomous and high-throughput identification of co-localized species in MSI data, which will dramatically expand the application of spatial lipidomics, metabolomics, and proteomics in biological research. Computer science ; Cluster analysis ; Artificial intelligence ; Convolutional neural network ; Classifier (UML) ; Deep learning ; Pattern recognition (psychology) ; Mass spectrometry imaging ; Identification (biology) ; Annotation ; Mass spectrometry ; Chemistry ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications