jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A Venkatesh. | M. Saravanan
Source: 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) (article, 2022-10-20)
Field: Chemistry — Analytical Chemistry

An Efficient Method for Predicting Linear Regression with Polynomial Regression Aim: This research study will identify the crop yield production by predicting efficient performance with higher accuracy using various machine learning algorithms. Materials and Methods: The dataset used for this paper consists of 1112 rows of data for the classification of crop yield. The data are labelled as “temperature”, “humidity”, “rainfall”, “production”, which classifies the data or the crop yeild data into these types, 20 number of dataset have been used for Simple Linear Regression (SLR) taken as group-1 and is compared with Polynomial Regression (PR) algorithm taken as group-2 with 80 percentage of g value to predict crop yield data with confidence value of 95% and significant value of 0.05. The result predicted for better crop yield with novel simple linear regression algorithm used and shown accuracy of 95.40%. This study found the prediction of crop yield using the SLR is good significantly better than the PR algorithm. Polynomial regression ; Regression ; Linear regression ; Proper linear model ; Regression analysis ; Computer science ; Bayesian multivariate linear regression ; Statistics ; Polynomial ; Regression dilution ; Mathematics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Fault Detection and Control Systems
Authors: Fei Wang | Zhiyuan Bi | Lifeng Ding | Qingyuan Yang
Source: Chinese Journal of Chemical Engineering (article, 2022-04-10)
Field: Chemistry — Inorganic Chemistry

Large-scale computational screening of metal–organic frameworks for D2/H2 separation Separation (statistics) ; Metal-organic framework ; Scale (ratio) ; Environmental science ; Chemistry ; Computer science ; Organic chemistry ; Machine learning ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; Covalent Organic Framework Applications
Authors: Shengchao Liu | Meng Qu | Zuobai Zhang | Huiyu Cai | Jian Tang
Source: arXiv (Cornell University) (preprint, 2022-02-22)
Field: Materials Science — Materials Chemistry

Structured Multi-task Learning for Molecular Property Prediction Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity. In this paper, we study multi-task learning for molecular property prediction in a novel setting, where a relation graph between tasks is available. We first construct a dataset (ChEMBL-STRING) including around 400 tasks as well as a task relation graph. Then to better utilize such relation graph, we propose a method called SGNN-EBM to systematically investigate the structured task modeling from two perspectives. (1) In the \emph{latent} space, we model the task representations by applying a state graph neural network (SGNN) on the relation graph. (2) In the \emph{output} space, we employ structured prediction with the energy-based model (EBM), which can be efficiently trained through noise-contrastive estimation (NCE) approach. Empirical results justify the effectiveness of SGNN-EBM. Code is available on https://github.com/chao1224/SGNN-EBM. Computer science ; Graph ; Machine learning ; Artificial intelligence ; Complement (music) ; Relation (database) ; Task (project management) ; Theoretical computer science ; Property (philosophy) ; Latent variable ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ciril Reiner‐Rozman | Bernhard Pichler | Vivien Madi | Petra Weißenböck | Thomas Hegedüs | Patrik Aspermair | Johannes Bintinger
Source: Sensors (article, 2022-07-19)
Field: Materials Science — Polymers and Plastics

Optimization of Printed Polyaniline Composites for Gas Sensing Applications Polyaniline (PANI) films are promising candidates for electronic nose-based IoT applications, but device performances are influenced by fabrication parameters and ambient conditions. Affinities of different PANI composites to analytes for gas sensing applications remain elusive. In this study, we investigate the material properties in detail for two different dopant systems: F4TCNQ and carbon black. Using a reproducibility-driven approach, we investigate different dopant concentrations in regard to their sensitivity and specificity towards five relevant markers for breath cancer diagnosis. We benchmark the system using ammonia measurements and evaluate limits of detection. Furthermore, we provide statistical analysis on reproducibility and pave the way towards machine learning discrimination via principal component analysis. The influence of relative humidity on sensor hysteresis is also investigated. We find that F4TCNQ-doped PANI films show improved reproducibility compared to carbon black-doped films. We establish and quantify a tradeoff between sensitivity, reproducibility, and environmental stability by the choice of dopant and concentrations ratios. Reproducibility ; Dopant ; Polyaniline ; Electronic nose ; Materials science ; Carbon black ; Conductive polymer ; Fabrication ; Relative humidity ; Carbon fibers ; Doping ; Composite material ; Nanotechnology ; Polymer ; Chemistry ; Optoelectronics ; Composite number ; Polymerization ; Chromatography Conducting polymers and applications ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Yuelin Wang | Kai Yi | Xinliang Liu | Yu Guang Wang | Shi Jin
Source: arXiv (Cornell University) (preprint, 2022-06-11)
Field: Materials Science — Materials Chemistry

ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next. We model such process by an interacting particle system with attractive and repulsive forces and the Allen-Cahn force arising in the modeling of phase transition. The dynamics of the system is a reaction-diffusion process which can separate particles without blowing up. This induces an Allen-Cahn message passing (ACMP) for graph neural networks where the numerical iteration for the particle system solution constitutes the message passing propagation. ACMP which has a simple implementation with a neural ODE solver can propel the network depth up to one hundred of layers with theoretically proven strictly positive lower bound of the Dirichlet energy. It thus provides a deep model of GNNs circumventing the common GNN problem of oversmoothing. GNNs with ACMP achieve state of the art performance for real-world node classification tasks on both homophilic and heterophilic datasets. Codes are available at https://github.com/ykiiiiii/ACMP. Message passing ; Computer science ; Ode ; Artificial neural network ; Solver ; Phase transition ; Graph ; Node (physics) ; Algorithm ; Theoretical computer science ; Artificial intelligence ; Applied mathematics ; Mathematics ; Physics ; Distributed computing Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Stochastic Gradient Optimization Techniques
Authors: Zhiyang Zheng | Tengfei Xu | Dominik Legut | Ruifeng Zhang
Source: Computational Materials Science (article, 2022-09-13)
Field: Materials Science — Materials Chemistry

High-throughput informed machine learning models for ultrastrong B-N solids Computation ; Computer science ; Algorithm ; Matrix (chemical analysis) ; Interpretability ; Inverse ; Machine learning ; Mathematics ; Materials science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Boron and Carbon Nanomaterials Research
Authors: Jim James | Nathan Pruyne | Tiberiu Stan | Marcus Schwarting | Jiwon Yeom | Seungbum Hong | Peter W. Voorhees | Ben Blaiszik | Ian Foster
Source: Computational Materials Science (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

Segmentation of tomography datasets using 3D convolutional neural networks Convolutional neural network ; Computer science ; Artificial intelligence ; Voxel ; Pattern recognition (psychology) ; Segmentation ; Hyperparameter ; Deep learning ; Transformation (genetics) ; Visualization ; Chemistry Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties
Authors: Raziyeh Pourdarbani | Sajad Sabzi | Mohammad Hossein Rohban | Ginés Garcı́a-Mateos | José Miguel Molina‐Martínez | Jitendra Paliwal | Juan Ignacio Arribas
Source: Journal of Near Infrared Spectroscopy (article, 2022-05-29)
Field: Chemistry — Analytical Chemistry

Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants Chemical fertilizers are widely applied in agriculture to achieve high yield, enhance produce quality and build resistance to diseases; in our case the plant being tomato ( Solanum lycopersicum L. var. Royal). However, the acidity, size and taste of tomato fruits could change with excess nitrogen (N) application. The present study aims at the early detection of nitrogen-rich tomato leaves using hyperspectral imaging techniques in the visible and near infrared (Vis-NIR) spectrum, in order to improve plant nutrition composition at an early growth stage. A 30% over-dose of nitrogen was applied to half of the tomato pots. Five leaves were randomly collected from each pot for 3 days (classes D0, D1, D2 and D3), and images were captured with a hyperspectral camera. A metaheuristic approach of artificial neural networks and the firefly algorithm (ANN-FA) was used to determine the most discriminative wavelengths. Afterwards, a combination of ANN and particle swarm optimization (ANN-PSO) was used to classify tomato leaves into the four classes. The training/classification process was repeated 200 times, and results indicated that the proposed approach was able to detect the excess of nitrogen even at the first day (D1), with a precision of 92.9%. Considering all the classes, the average correct classification rate was 92.6%, while the best execution achieved 95.5% accuracy. Thus, the method showed a high performance for practical uses. Hyperspectral imaging ; Discriminative model ; Nitrogen ; Firefly algorithm ; Mathematics ; Horticulture ; Artificial intelligence ; Algorithm ; Computer science ; Pattern recognition (psychology) ; Particle swarm optimization ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Asako YOSHINARI | Yuma Iwasaki | Masato Kotsugi | Shunsuke Sato | Naoka Nagamura
Source: Science and Technology of Advanced Materials Methods (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Skill-Agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning Reflection high-energy electron diffraction (RHEED) data are important for the in-situ characterization of surface conditions during physical vapor deposition. Surface superstructures obtained by adsorbing exotic atoms onto a clean silicon surface, which exhibit various physical properties, were identified using RHEED. However, this information is too abundant for quantitative analysis; therefore, scientists rely on their expertise to interpret RHEED patterns to assess surface structures and evaluate film thickness, and a large amount of information remains unused. In this study, we adapted machine learning for a RHEED pattern dataset of a clean Si(111) surface during indium deposition in molecular-beam epitaxy growth to use the entire RHEED pattern image information and investigated appropriate machine leaning analysis methods. First, we aimed to determine RHEED pattern similarities in the dataset. Then, five structural phases, 7 × 7 (clean surface), √3×√3, √31×√31, 4 × 1, and 4 × 1 (Room Temperature), were automatically detected by hierarchical clustering using Ward’s method. Next, we aimed to extract the information for each surface superstructure from the dataset. Using non-negative matrix factorization, we successfully estimated the optimal forming conditions for each surface superstructure separately more accurately than the conventional methods. Our proposed strategies could be widely applied to surface structural analysis. Reflection high-energy electron diffraction ; Electron diffraction ; Superstructure ; Reflection (computer programming) ; Surface (topology) ; Deposition (geology) ; Materials science ; Diffraction ; Computer science ; Optics ; Geology ; Physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Haoyuan Chen
Source: Frontiers in Energy Research (article, 2022-10-28)
Field: Chemistry — Inorganic Chemistry

Recent advances in computational study and design of MOF catalysts for CO2 conversion Catalytic conversion of the greenhouse gas CO 2 into value-added chemicals and fuels is highly beneficial to the environment, the economy, and the global energy supply. Metal–organic frameworks (MOFs) are promising catalysts for this purpose due to their uniquely high structural and chemical tunability. In the catalyst discovery process, computational chemistry has emerged as an essential tool as it can not only aid in the interpretation of experimental observations but also provide atomistic-level insights into the catalytic mechanism. This Mini Review summarizes recent computational studies on MOF-catalyzed CO 2 conversion through different types of reactions, discusses about the usage of various computational methods in those works, and provides a brief perspective of future works in this field. Catalysis ; Biochemical engineering ; Nanotechnology ; Computational simulation ; Process engineering ; Materials science ; Computer science ; Chemistry ; Engineering ; Organic chemistry ; Computational science Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science
Authors: Timothy A. Goetjen | Magali Ferrandon | A. Jeremy Kropf | Jessica V. Lamb | Massimiliano Delferro | Joseph T. Hupp | Omar K. Farha
Source: The Journal of Physical Chemistry C (article, 2022-11-28)
Field: Chemistry — Inorganic Chemistry

Active-Site Determination and Mechanistic Insights in a MOF-Supported Polymerization Catalyst The structural elucidation of catalyst active sites in heterogeneous catalysts when supported by traditional metal oxides remains a challenge despite the advanced characterization techniques that have been developed. The catalyst-deposition-site nonuniformity in metal oxides inhibits clear structural characterization through bulk spectroscopic methods and rules out the use of single-crystal X-ray diffraction. However, for metal–organic framework (MOF)-supported catalysts, the crystallinity and uniform structures of the underlying support enhance our ability to identify the precatalyst and catalytically active sites and open the door to using single-crystal X-ray diffraction coupled with spectroscopy under reaction conditions. The use of in situ X-ray absorption spectroscopy identifies the catalytically active site in diethylaluminum chloride (DEAC)-pretreated Cr-SIM-NU-1000 to be a Cr-ethyl when used for ethylene polymerization. Further kinetic experiments elucidate the effects of ethylene pressure, temperature, catalyst loading, and cocatalyst loading, furthering mechanistic knowledge and helping to deconvolute the structure−function relationship. Catalysis ; Crystallinity ; Polymerization ; Ethylene ; Active site ; Characterization (materials science) ; Materials science ; Metal ; Metal-organic framework ; Spectroscopy ; Chemical engineering ; Chemistry ; Single crystal ; Inorganic chemistry ; Organic chemistry ; Crystallography ; Nanotechnology ; Polymer Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Chandrabhan Verma | Chaudhery Mustansar Hussain | M.A. Quraishi | Kyong Yop Rhee
Source: Reviews in Chemical Engineering (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Metals and metal oxides polymer frameworks as advanced anticorrosive materials: design, performance, and future direction Abstract Metals (Ms) and metal oxides (MOs) possess a strong tendency to coordinate and combine with organic polymers to form respective metal–polymer frameworks (MPFs) and metal oxide polymer frameworks (MOPFs). MPFs and MOPFs can be regarded as composites of organic polymers. MPFs and MOPFs are widely used for industrial and biological applications including as anticorrosive materials in the aqueous phase as well as in the coating conditions. The presence of the Ms and MOs in the polymer coatings improves the corrosion inhibition potential of MPFs and MOPFs by improving their self-healing properties. The Ms and MOs fill the micropores and cracks through which corrosive species such as water, oxygen, and corrosive ions and salts can diffuse and destroy the coating structures. Therefore, the Ms and MOs enhance the durability as well as the effectiveness of the polymer coatings. The present review article is intended to describe the corrosion inhibition potential of some MPFs and MOPFs of some most frequently utilized transition metal elements such as Ti, Si, Zn, Ce, Ag, and Au. The mechanism of corrosion inhibition of MPFs and MOPFs is also described in the presence and absence of metal and metal oxides. Corrosion ; Materials science ; Coating ; Polymer ; Metal ; Oxide ; Metal-organic framework ; Chemical engineering ; Metal ions in aqueous solution ; Aqueous solution ; Durability ; Metallurgy ; Inorganic chemistry ; Composite material ; Chemistry ; Organic chemistry ; Adsorption Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Conducting polymers and applications
Authors: Rasmus Tranås | Ole Martin Løvvik | Kristian Berland
Source: Electronic Materials (article, 2022-01-05)
Field: Materials Science — Materials Chemistry

Attaining Low Lattice Thermal Conductivity in Half-Heusler Sublattice Solid Solutions: Which Substitution Site Is Most Effective? Low thermal conductivity is an important materials property for thermoelectricity. The lattice thermal conductivity (LTC) can be reduced by introducing sublattice disorder through partial isovalent substitution. Yet, large-scale screening of materials has seldom taken this opportunity into account. The present study aims to investigate the effect of partial sublattice substitution on the LTC. The study relies on the temperature-dependent effective potential method based on forces obtained from density functional theory. Solid solutions are simulated within a virtual crystal approximation, and the effect of grain-boundary scattering is also included. This is done to systematically probe the effect of sublattice substitution on the LTC of 122 half-Heusler compounds. It is found that substitution on the three different crystallographic sites leads to a reduction of the LTC that varies significantly both between the sites and between the different compounds. Nevertheless, some common criteria are identified as most efficient for reduction of the LTC: The mass contrast should be large within the parent compound, and substitution should be performed on the heaviest atoms. It is also found that the combined effect of sublattice substitution and grain-boundary scattering can lead to a drastic reduction of the LTC. The lowest LTC of the current set of half-Heusler compounds is around 2 W/Km at 300 K for two of the parent compounds. Four additional compounds can reach similarly low LTC with the combined effect of sublattice disorder and grain boundaries. Two of these four compounds have an intrinsic LTC above ∼15 W/Km, underlining that materials with high intrinsic LTC could still be viable for thermoelectric applications. Grain boundary ; Substitution (logic) ; Materials science ; Substitution effect ; Solid solution ; Scattering ; Thermal conductivity ; Crystal structure ; Crystallography ; Condensed matter physics ; Thermoelectric effect ; Density functional theory ; Lattice (music) ; Chemistry ; Thermodynamics ; Computational chemistry ; Microstructure ; Metallurgy ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Selin Bac | Abhilash Patra | Kareesa J. Kron | Shaama Mallikarjun Sharada
Source: The Journal of Physical Chemistry A (review, 2022-10-25)
Field: Materials Science — Materials Chemistry

Recent Advances toward Efficient Calculation of Higher Nuclear Derivatives in Quantum Chemistry In this paper, we provide an overview of state-of-the-art techniques that are being developed for efficient calculation of second and higher nuclear derivatives of quantum mechanical (QM) energy. Calculations of nuclear Hessians and anharmonic terms incur high costs and memory and scale poorly with system size. Three emerging classes of methods─machine learning (ML), automatic differentiation (AD), and matrix completion (MC)─have demonstrated promise in overcoming these challenges. We illustrate studies that employ unsupervised ML methods to reduce the need for multiple Hessian calculations in dynamics simulations and those that utilize supervised ML to construct approximate potential energy surfaces and estimate Hessians and anharmonic terms at reduced cost. By extension, if electronic structure operations could be written in a manner similar to functions underlying ML methods, rapid differentiation or AD routines can be employed to inexpensively calculate higher arbitrary-order derivatives. While ML approaches are typically black-box, we describe methods such as compressed sensing (CS) and MC, which explicitly leverage problem-specific mathematical properties of higher derivatives such as sparsity and low-rank, to complete higher derivative information using only a small, incomplete sample. The three classes of methods facilitate reliable predictions of observables ranging from infrared spectra to thermal conductivity and constitute a promising way forward in accurately capturing otherwise intractable higher-order responses of QM energy to nuclear perturbations. Hessian matrix ; Computer science ; Anharmonicity ; Leverage (statistics) ; Quantum ; Rank (graph theory) ; Statistical physics ; Applied mathematics ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Spectroscopy and Quantum Chemical Studies