jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yingzong Liang | Mingwei Chen | Yanan Wang | Huaxian Jia | Tenglong Lu | Fankai Xie | Sheng Meng | Miao Liu
Source: arXiv (Cornell University) (preprint, 2021-08-01)
Field: Materials Science — Materials Chemistry

A universal model for the formation energy prediction of inorganic compounds Harnessing the recent advance in data science and materials science, it is feasible today to build predictive models for materials properties. In this study, we employ the data of high-throughput quantum mechanics calculations based on 170,714 inorganic crystalline compounds to train a machine learning model for formation energy prediction. Different from the previous work, our model reaches a fairly good predictive ability (R2=0.982 and MAE=0.07 eVatom-1, DenseNet model) and meanwhile can be universally applied to the large phase space of inorganic materials. The improvement comes from several effective structure-dependent descriptors that are proposed to take the information of electronegativity and structure into account. This model can provide a useful tool to search for new materials in a vast phase space in a fast and cost-effective manner. Electronegativity ; Computer science ; Phase space ; Throughput ; Work (physics) ; Space (punctuation) ; Chemical space ; Energy (signal processing) ; Phase (matter) ; Machine learning ; Artificial intelligence ; Biological system ; Chemistry ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Heitor Luiz Ornaghi | Roberta Motta Neves | Francisco Maciel Monticeli
Source: Preprints.org (preprint, 2021-05-24)
Field: Materials Science — Polymers and Plastics

Application of the Artificial Neural Network (ANN) Approach for Prediction of the Kinetic Parameters of Lignocellulosic Fibers Lignocellulosic fibers are widely applied as composite reinforcement due to their properties. The thermal degradation behavior determines the maximum temperature in which the fiber can be applied without significant mass loss. It is possible to determine these temperatures using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost, and time. Methods to predict thermogravimetric curves can be very useful in the materials science field and in this sense mathematical approaches are powerful tools if well employed. For this reason, in the present study, curaua TG curves were obtained at three different heating rates (5, 10, 20, and 40 &amp;deg;C.min-1) and Vyazovkin kinetic parameters were obtained. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM). Curves at any heating rate between the minimum and maximum experimental heating rates were obtained with high reliability. Finally, Vyazovkin kinetic parameters were tested again with the new curves showing similar kinetic parameters from the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications. Thermogravimetric analysis ; Kinetic energy ; Artificial neural network ; Fiber ; Biological system ; Thermal ; Curve fitting ; Materials science ; Computer science ; Thermodynamics ; Composite material ; Artificial intelligence ; Chemical engineering ; Machine learning ; Physics ; Engineering Natural Fiber Reinforced Composites ; Thermal and Kinetic Analysis ; Tribology and Wear Analysis
Authors: Stephen Lam | Qing‐Jie Li | R. G. Ballinger | Charles H. Forsberg | Ju Li
Source: ChemRxiv (preprint, 2021-01-14)
Field: Materials Science — Materials Chemistry

Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potentials &lt;p&gt;Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3¾F&lt;sub&gt;2&lt;/sub&gt; (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of desirable heat-transfer and neutron-absorption characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIP) provide a fast and accurate method for performing molecular dynamics of molten salts. For LiF, these potentials are able to accurately model dimer interactions, crystalline solids under deformation, semi-crystalline LiF near the melting point and liquid LiF at high temperatures. For Flibe, NNIPs accurately predicts the structures and dynamics at normal operating conditions, high temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long timescales (e.g., nanosecond) and large system sizes (e.g., 10&lt;sup&gt;5&lt;/sup&gt; atoms), while maintaining ab initio accuracy and providing more than three orders of magnitude of computational speedup for calculating structure and transport properties.&lt;/p&gt; Molecular dynamics ; Lithium (medication) ; Melting point ; Materials science ; Chemical physics ; Atom (system on chip) ; Absorption (acoustics) ; Interatomic potential ; Chemistry ; Computational chemistry Graphite, nuclear technology, radiation studies ; Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Lakshmi Mandal | Nanda Dulal Jana
Source: 2021 IEEE 18th India Council International Conference (INDICON) (article, 2021-12-19)
Field: Chemistry — Analytical Chemistry

Automatic Kernel Selection of Support Vector Machine for Drug/Non-Drug Compounds Classification The traditional approach of drug design and discovery is a complex process which is highly time-consuming and costly. Recently, researchers have been utilizing computational resources to speed up drug design and discovery process, known as Computer-Aided Drug Design (CADD). In CADD, the primary aim is to identify drug compounds, which neutralize the target molecule from a large biochemical dataset. Therefore, identification of drug compounds can be formulated as a classification problem. Support Vector Machine (SVM) is one of the most widely used machine learning algorithms for classification problems. However, kernel selection in SVM is a crucial task for classification problem. In this paper, we proposed a mechanism to select kernel function automatically for SVM (Auto-SVM) for drug/non-drug compounds classification. The perceptron learning algorithm is used for the linear-separability analysis of the biochemical datasets which infers whether the SVM can be integrated by a linear or non-linear kernel function. An SVM kernel function is selected based on statistical analysis. Different biochemical datasets are considered for experimental purposes. The proposed methodology demonstrates high classification performance, compared with the state-of-the-art ML algorithms such as back-propagation neural network (BPNN), naive Bayes (NB) and k-nearest neighbor (k-NN) with respect to accuracy, precision, recall and F1-score. Support vector machine ; Computer science ; Artificial intelligence ; Machine learning ; Kernel (algebra) ; Radial basis function kernel ; Naive Bayes classifier ; Pattern recognition (psychology) ; Multilayer perceptron ; Feature selection ; Artificial neural network ; Kernel method ; Perceptron ; Data mining ; Mathematics Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Mir Saeid Hesarian | Jafar Tavoosi
Source: Journal of the Textile Institute (article, 2021-12-29)
Field: Materials Science — Polymers and Plastics

Smooth appearance analysis of the finished cotton fabric affected by structural parameters based on Hopfield neural network Cotton as the most important natural fibre is used to produce apparel in clothing industry. The appearance of the cotton garment is an important factor for consumers. Therefore, in textile industry, the cotton fabric is treated with anti-wrinkle finishing material. In this process, the cross-linking is the main parameter that produces the resiliency in the chemical structure of the fiber and gives the crease retention to the cellulosic fabrics. According to this introduction, is the crosslinking unique parameter in the wrinkle resistance appearance of treated cotton fabric or others important parameters related to the fabric structure lead to the wrinkle resistance? Reply of this question is the first novelty of this article. For this purpose, we intend to use a novel tool in computational intelligence to model the wrinkle appearance of the treated and non-treated cotton fabric due to the structural parameters of the fabric. Therefore, the effect of the fiber diameter, yarn twist, fabric thickness and bending length parameters are evaluated experimentally and theoretically on the wrinkle grade of the fabric. In experiments, the mentioned parameters are measured for various provided fabrics. Then wrinkle degree of the samples is ratted according to the light line method. In theoretical study, a new Hopfield neural network model is presented as one of the novelties of this article. Four mentioned parameters are considered as the inputs and the wrinkle degree is obtained as the output of the developed Hopfield NN model. This model approximated a nonlinear function between the output and all four inputs. Simulation and experimental results show the effect of four mentioned inputs on the wrinkle degree of the non-treated cotton samples. Simulation of the treated wrinkle grade shows that the fiber diameter, yarn twist, fabric thickness and bending length parameters affect the wrinkle grade and the crosslinking is not any effect on cotton wrinkle grade. Modeling results show that the proposed Hopfield neural network has high learning capability, fast convergence and accuracy (greater than 98%) and finally negligible error value (smaller than of 1%), so it can be reasonably used in clothing industry. Wrinkle ; Textile ; Fabric structure ; Yarn ; Composite material ; Fiber ; Bending ; Artificial neural network ; Materials science ; Woven fabric ; Structural engineering ; Computer science ; Artificial intelligence ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Dyeing and Modifying Textile Fibers
Authors: Younes Chiba | Yacine Marif | Noureddine Henini | Abdelhalim Tlemçani
Source: International Journal of Energy Optimization and Engineering (article, 2021-09-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling of Magnetic Refrigeration Device by Using Artificial Neural Networks Approach The aim of this work is to use multi-layered perceptron artificial neural networks and multiple linear regressions models to predict the efficiency of the magnetic refrigeration cycle device operating near room temperature. For this purpose, the experimental data collection was used in order to predict coefficient of performance and temperature span for active magnetic refrigeration device. In addition, the operating parameters of active magnetic refrigerator cycle are used for solid magnetocaloric material under application 1.5 T magnetic fields. The obtained results including temperature span and coefficient of performance are presented and discussed. Magnetic refrigeration ; Refrigeration ; Coefficient of performance ; Artificial neural network ; Multilayer perceptron ; Work (physics) ; Magnetic field ; Materials science ; Computer science ; Mechanical engineering ; Artificial intelligence ; Engineering ; Refrigerant ; Physics Magnetic and transport properties of perovskites and related materials ; Gas Sensing Nanomaterials and Sensors ; Advanced Thermoelectric Materials and Devices
Authors: Mehmet Cagri Kaymak | Ali Rahnamoun | Kurt A. O’Hearn | Adri C. T. van Duin | Kenneth M. Merz | Hasan Metin Aktulga
Source: ChemRxiv (preprint, 2021-07-06)
Field: Materials Science — Materials Chemistry

JAX-ReaxFF: A Gradient Based Framework for Extremely Fast Optimization of Reactive Force Fields Molecular dynamics (MD) simulations ease the study of the chemistry of interest. While classical models that governs the interaction of the atoms lack reactivity, the quantum mechanics based methods increase the computational cost drastically. ReaxFF fills the gap between these two ends of the spectrum by allowing bond breaking and dynamic charges. To achieve realistic simulations using ReaxFF, the model parameters need to be carefully tuned based on the training data created using more accurate but expensive methods. The current optimization methods focus on black-box optimization methods such as genetic algorithms (GAs), Monte-Carlo methods and covariance matrix adaptation evolutionary strategy (CMA-ES). Due to the stochastic behavior of these methods, the training requires hundreds of thousands of error evaluations for complex training tasks and each error evaluation usually involves energy minimization of the many molecules in the training data. In this work, we propose a novel approach which takes advantage of the modern tools developed for machine learning to improve the training efficiency of the force field development for ReaxFF. By calculating the gradients of the loss function using JAX library developed by Google, we are able to use well studied local optimization methods such as the limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) and Sequential Least Squares Programming (SLSQP) method. To further decrease the training time, we skip the energy minimization of the molecules during the local optimization since the parameters of the model are not likely to change drastically during a local search. JAX allows us to easily parallelize the error evolution by compiling the code for CPUs, GPUs or TPUs. With the help of the modern accelerators and the gradient information, we are able to decrease the training time from days to minutes. ReaxFF ; Computer science ; Force field (fiction) ; Molecular dynamics ; Minification ; Energy minimization ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Computational chemistry ; Mathematics ; Chemistry Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Parallel Computing and Optimization Techniques
Authors: Ben Symons | Michael Bane | Paul L. A. Popelier
Source: ChemRxiv (preprint, 2021-06-22)
Field: Materials Science — Materials Chemistry

DL_FFLUX: a parallel, quantum chemical topology force field DL_FFLUX performs molecular dynamics for flexible molecules endowed with polarisable QTAIM atomic multipole moments, predicted by the machine learning method Gaussian Process Regression. Newly optimised and parallelised using domain-decomposition MPI, DL_FFLUX now operates in reasonable time frames. DL_FFLUX is delivered as an add-on to the widely distributed molecular dynamics code DL_POLY 4.08. For the systems studied here (10**3-10**5 atoms), DL_FFLUX adds minimal computational cost to the standard DL_POLY package. The parallel DL_FFLUX preserves the quality of the scaling of the MPI implementation in standard DL_POLY. For the first time it is feasible to use the full capability of DL_FFLUX to study systems that are large enough to be of real world interest. For example, a fully flexible, high-rank polarised (up to and including quadrupole moments) 1 ns simulation of a system of 10,125 atoms (3,375 water molecules) takes 30 hours (wall time) on 18 cores. Multipole expansion ; Molecular dynamics ; Fast multipole method ; Quantum ; Computer science ; Force field (fiction) ; Field (mathematics) ; Scaling ; Computational science ; Topology (electrical circuits) ; Algorithm ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Mathematics ; Combinatorics ; Artificial intelligence Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Mahati Manda
Source: arXiv (Cornell University) (preprint, 2021-10-12)
Field: Chemistry — Inorganic Chemistry

Predicting the Efficiency of CO$_2$ Sequestering by Metal Organic Frameworks Through Machine Learning Analysis of Structural and Electronic Properties Due the alarming rate of climate change, the implementation of efficient CO$_2$ capture has become crucial. This project aims to create an algorithm that predicts the uptake of CO$_2$ adsorbing Metal-Organic Frameworks (MOFs) by using Machine Learning. These values will in turn gauge the efficiency of these MOFs and provide scientists who are looking to maximize the uptake a way to know whether or not the MOF is worth synthesizing. This algorithm will save resources such as time and equipment as scientists will be able to disregard hypothetical MOFs with low efficiencies. In addition, this paper will also highlight the most important features within the data set. This research will contribute to enable the rapid synthesis of CO$_2$ adsorbing MOFs. Metal-organic framework ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Antti Pihlajamäki | Joakim Linja | Joonas Hämäläinen | P. Nieminen | Sami Malola | Tommi Kärkkäinen | Hannu Häkkinen
Source: ESANN 2021 proceedings (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces Machine learning (ML) force fields are one of the most common applications of ML in nanoscience. However, commonly these methods are trained on potential energies of atomic systems and force vectors are omitted. Here we present a ML framework, which tackles the greatest difficulty on using forces in ML: accurate prediction of force direction. We use the idea of Minimal Learning Machine to device a method which can adapt to the orientation of an atomic environment to estimate the directions of force vectors. The method was tested with linear alkane molecules. Orientation (vector space) ; Computer science ; Artificial intelligence ; Geometry ; Mathematics Machine Learning in Materials Science ; Machine Learning and ELM ; Mineral Processing and Grinding
Authors: Mohammad Javad Tavakkoli | Elham Yasari | Nafiseh Farhadian
Source: SHILAP Revista de lepidopterología (article, 2021-05-12)
Field: Materials Science — Materials Chemistry

Prediction of the hydrogen adsorption isotherm on nickel decorated carbon nanotubes by applying artificial neural network modeling The design and production of new materials to safely store hydrogen are challenging in hydrogen storage technology. Porous carbon materials such as carbon nanotubes (CNTs) are novel candidates for this aim. Predicting the hydrogen adsorption isotherm on these new materials can be done very effectively. Artificial neural network modeling (ANN) is a helpful tool for this aim. In this study, a feed-forward ANN with one hidden layer was constructed and tested to model the equilibrium data of hydrogen adsorption onto Ni-decorated CNTs. CNT properties like surface area, pore volume, and experimental conditions are used as inputs to predict the corresponding hydrogen uptake in equilibrium conditions. The constructed ANN was found to be precise in modeling the hydrogen adsorption isotherms for all inputs during the training process. The trained network successfully simulates the hydrogen adsorption isotherm for new inputs, which are kept unaware of the ANN during the training process. This shows the power of the created ANN model to determine adsorption isotherms for any operating conditions under the studied constraints. Adsorption ; Hydrogen ; Carbon nanotube ; Artificial neural network ; Materials science ; Hydrogen storage ; Nickel ; Carbon fibers ; Process engineering ; Chemical engineering ; Computer science ; Nanotechnology ; Chemistry ; Composite material ; Artificial intelligence ; Engineering ; Organic chemistry ; Metallurgy ; Composite number Hydrogen Storage and Materials ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Jianan Tang | Xiao Geng | Dongsheng Li | Yunfeng Shi | Jianhua Tong | Hai Xiao | Fei Peng
Source: Research Square (preprint, 2021-02-12)
Field: Materials Science — Materials Chemistry

Machine Learning-based Microstructure Prediction During Laser Sintering of Alumina: A Conditional Generative Approach Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning (ML) algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks (GANs) with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the shapes, sizes, and spatial configuration of the grains and the pores, match the experimental SEM micrographs very well. We further examine the statistical features of the micrograph, which were predicted by RCWCAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson-Mehl-Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve. Microstructure ; Micrograph ; Materials science ; Laser ; Function (biology) ; Artificial intelligence ; Generative grammar ; Phase (matter) ; Algorithm ; Selective laser sintering ; Computer science ; Sintering ; Composite material ; Optics ; Physics ; Scanning electron microscope Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced ceramic materials synthesis