jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junrong Lin | Mahmudul Hasan | Pinar Acarb | José Blanchet | Vahid Tarokh
Source: arXiv (Cornell University) (preprint, 2023-04-11)
Field: Materials Science — Materials Chemistry

Neural Network Accelerated Process Design of Polycrystalline Microstructures Computational experiments are exploited in finding a well-designed processing path to optimize material structures for desired properties. This requires understanding the interplay between the processing-(micro)structure-property linkages using a multi-scale approach that connects the macro-scale (process parameters) to meso (homogenized properties) and micro (crystallographic texture) scales. Due to the nature of the problem's multi-scale modeling setup, possible processing path choices could grow exponentially as the decision tree becomes deeper, and the traditional simulators' speed reaches a critical computational threshold. To lessen the computational burden for predicting microstructural evolution under given loading conditions, we develop a neural network (NN)-based method with physics-infused constraints. The NN aims to learn the evolution of microstructures under each elementary process. Our method is effective and robust in finding optimal processing paths. In this study, our NN-based method is applied to maximize the homogenized stiffness of a Copper microstructure, and it is found to be 686 times faster while achieving 0.053% error in the resulting homogenized stiffness compared to the traditional finite element simulator on a 10-process experiment. Process (computing) ; Artificial neural network ; Computer science ; Path (computing) ; Microstructure ; Finite element method ; Stiffness ; Scale (ratio) ; Macroscopic scale ; Algorithm ; Mathematical optimization ; Materials science ; Artificial intelligence ; Structural engineering ; Engineering ; Mathematics ; Physics Machine Learning in Materials Science ; Metallurgy and Material Forming ; Metal Forming Simulation Techniques
Authors: Haoxin Mai | Tu C. Le | David A. Winkler | Rachel A. Caruso
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Rapid Discovery of Narrow Bandgap Oxide Photocatalysts Assisted by Machine Learning Band gap ; Materials science ; Photocatalysis ; Optoelectronics ; Oxide ; Nanotechnology ; Chemistry ; Metallurgy Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Vanja Malisic | Milada Pezo | Aleksandra Jelić | Aleksandra Patarić | Slaviša Putić
Source: Hemijska industrija (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Prediction of thermal and mechanical properties of acrylate-based composites using artificial neural network modeling Poly(methyl methacrylate) (PMMA) has a broad spectrum of uses, especially in medical applications. The role of fine-grained alumina particles of PMMA composites was investigated in this study. The composites were based on PMMA modified with dimethyl itaconate (DMI) as a matrix and alumina particles (Al2O3) and alumina doped with iron (Al2O3-Fe) modified with 3-aminopropyl-trimethoxysilane (AM) and flax oil fatty acid methyl esters (biodiesel) as reinforcements. Three particle sizes were measured (~0.4, ~0.6 and ~1.2 ?m). The highest thermal conductivity values were measured for the composite 5 wt.% Al2O3-Fe-AM. With the addition of 3 wt.% Al2O3-AM to the PMMA/DMI matrix, mechanical properties were improved (tensile strength, strain, and modulus of elasticity). An artificial neural network model based on the Broyden-Fletcher-Goldfarb-Shanno iterative algorithm was investigated for prediction of thermal conductivity and mechanical properties of the composites showing satisfactory results. This is relevant for applications for optimization of dental materials to produce dentures, which were exposed to variations in temperature during the application. Materials science ; Composite material ; Ultimate tensile strength ; Thermal conductivity ; Acrylate ; Composite number ; Polymer ; Copolymer Polymer Nanocomposites and Properties ; Dental materials and restorations ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Diba Behnoudfar | Cory M. Simon | Joshua Schrier
Source: ChemRxiv (preprint, 2023-08-21)
Field: Materials Science — Materials Chemistry

Data-driven imputation of miscibility of aqueous solutions via graph-regularized logistic matrix factorization Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas previous machine learning approaches to ATPS prediction used physicochemical properties of each solute as a descriptor, in this work, we show how to impute missing miscibility outcomes directly from an incomplete collection of pairwise miscibility experiments. We use graph-regularized logistic matrix factorization (GR-LMF) to learn a latent vector of each solution from (i) the observed entries in the pairwise miscibility matrix and (ii) a graph (nodes: solutes, edges: shared relationships) indicating the general category of the solute (i.e., polymer, surfactant, salt, protein). For an experimental dataset of the pairwise miscibility of 68 solutions from Peacock et al. [ACS Appl. Mater. Interfaces 2021, 13, 11449--11460], we find that GR-LMF more accurately predicts missing (im)miscibility outcomes of pairs of solutions than ordinary logistic matrix factorization and random forest classifiers that use physicochemical features of the solutes. GR-LMF obviates the need for features of the solutions/solutes to impute missing miscibility outcomes, but it cannot predict the miscibility of a new solution without some observations of its miscibility with other solutions in the training data set. Miscibility ; Pairwise comparison ; Aqueous solution ; Thermodynamics ; Chemistry ; Mathematics ; Materials science ; Statistics ; Polymer ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Marjolein de Jager | Frank Smallenburg | Laura Filion
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-09-12)
Field: Materials Science — Materials Chemistry

In search of a precursor for crystal nucleation of hard and charged colloids Data package accompanying the publication with the above title in the Journal of Chemical Physics (2023). This package provides all the relevant information for the numerical study of crystal nucleation of hard and charged colloids. We strived to answer the question: are there hidden local structures present in metastable fluid that foreshadow the location of the imminent formation of a crystal nucleus? To this end, we obtained numerous spontaneous nucleation events of both hard and charged colloids using computer simulations, and tracked the onset of crystal nucleation on a local level. We used both standard and unsupervised machine learning methods for identifying local structures, and demonstrated that all signs of crystallinity emerge simultaneously, and abruptly, from the very start of the nucleation process. In brief, this package contains the data required to reproduce the graphs, as well as the simulation source code and analysis scripts required to process the raw output. In each directory there are README.txt files that describe the content. The main directory contains three directories: the directory [Figures], which contains the published graphs, and the directories [HS] and [HCYukawa], which contain everything on the systems of, respectively, hard spheres and hard-core Yukawa particles. In these last two directories you will find the directories [Data], [Mathematica], [Python], and/or [Code]. The directory [Data] contains all the processed data (.dat format) for that system. Each subdirectory in [Data] contains its own C codes (and bash scripts) used for generating the raw simulation data. The directory [Python] contains all Jupyter notebooks, which were mostly used for the principal component analysis (PCA). The directory [Mathematica] contains all the Mathematica notebooks used for processing the (raw) data and visualizing the results. Lastly, the directory [Code], when present, contains additional C codes used for analyzing the raw data. Nucleation ; Colloid ; Materials science ; Colloidal crystal ; Crystal (programming language) ; Chemical physics ; Nanotechnology ; Chemical engineering ; Chemistry ; Computer science ; Engineering ; Organic chemistry Material Dynamics and Properties ; Minerals Flotation and Separation Techniques ; nanoparticles nucleation surface interactions
Authors: Kai Sundmacher | Karsten Rätze | Wieland Kortuz | Sabine Kirschtowski | Michael Jokiel | Christof Hamel
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Optimal Experimental Design for the Identification of a Reaction Kinetic Model for the Hydroaminomethylation of 1-Decene in a Thermomorphic Multiphase System Kinetic energy ; Identification (biology) ; Chemistry ; Computer science ; Applied mathematics ; Mathematics ; Physics ; Classical mechanics Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Prof. Vijayaramaraju | K. Dhanesh | G.Lakshmi Varun | G.Shiva Kumar | N.Venkata Siva Reddy
Source: International Journal of Research Publication and Reviews (article, 2023-04-27)
Field: Chemistry — Analytical Chemistry

Rice Grain Quality Analysis Using CNN, Dense Net, Mobile Net Rice is a staple food crop for millions of people worldwide, and the quality of the rice grains is a critical factor in determining its market value and consumer acceptance.Traditional methods of grading and classifying rice grains are time-consuming and subjective, leading to inconsistencies and errors in the final product.In recent years, deep learning (DL) techniques have shown great promise in automating the process of rice grain quality analysis.In this study, we developed a DL-based approach for rice grain quality analysis using a large dataset of rice grain images.The DL model was trained to classify rice grains based on various quality parameters such as size, shape, colour, texture, and defects.The results showed that the DL model could accurately classify rice grains with a high degree.Furthermore, the DL-based analysis provided insights into the underlying physical and chemical properties of the rice grains, which can be used to optimize production and processing methods. Net (polyhedron) ; Computer science ; Environmental science ; Mathematics Spectroscopy and Chemometric Analyses
Authors: Abraham, B. Moses | Piqué, Oriol | Khan, Mohd Aamir | Viñes Solana, Francesc | Illas i Riera, Francesc | Singh, Jayant K.
Source: Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO2 adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (εd), surface metal electronegativity (χM), and valence electron number of metal atoms (MV) as key descriptors for CO2 activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO2 activation and their posterior usage. Electronegativity ; Transition metal ; Valence (chemistry) ; Carbide ; Nitride ; Metal ; Valence electron ; MXenes MXene and MAX Phase Materials ; CO2 Reduction Techniques and Catalysts ; Machine Learning in Materials Science
Authors: Shuma Tanaka | Kazuyoshi Ogasawara
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Prediction of Emission Level Energy of Mn4+ in Oxides Based on the Local Structure Using First-Principles Calculations and Machine Learning The white LED is an important next-generation light source. Most of the high color rendering white LEDs that have become popular in recent years consist of blue LEDs combined with green and red phosphors. For example, Eu 2+ :Sr x Ca 1 -xAlSiN 3 (SCASN) and Mn 4+ :K 2 SiF 6 (KSF) are used as red phosphors. However, considering the production cost and the stability against high temperature and high humidity, novel red phosphors obtained by adding Mn 4+ to oxide crystals are still desired. To realize a Mn-doped oxide red phosphor for white LEDs, it is indispensable to clarify the relationship between the multiplet energies and the local structures. Semi-empirical methods based on experimental data are generally used to analyze luminescent materials, but these methods cannot be applied to unknown or hypothetical materials. In this work, we analyzed the relationship between the local structure and the energy levels of hypothetical Mn 4+ -doped oxides by performing first-principles electronic structure calculations using the discrete-variational multi-electron (DVME) method. The local structures around Mn 4+ were systematically changed by changing the bond distances and angles and the multiplet energies corresponding to each local structure were calculated non-empirically. Then the relationship between the energy and the local structure was visually represented by creating energy-structure maps. Using these systematic theoretical values as the training data, a machine learning model for the prediction of the first-principles calculation results based only on the structural parameters was also created. The results of this study would provide guidelines for the development of novel phosphors based on oxide crystals doped with Mn 4+ . Energy (signal processing) ; Materials science ; Computational physics ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Carlo Adornetto | Gianluigi Greco
Source:  (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

GIDnets: Generative Neural Networks for Solving Inverse Design Problems via Latent Space Exploration In a number of different fields, including Engeneering, Chemistry and Physics, the design of technological tools and device structures is increasingly supported by deep-learning based methods, which provide suggestions on crucial architectural choices based on the properties that these tools and structures should exhibit. The paper proposes a novel architecture, named GIDnet, to address this inverse design problem, which is based on exploring a suitably defined latent space associated with the possible designs. Among its distinguishing features, GIDnet is capable of identifying the most appropriate starting point for the exploration and of likely converging into a point corresponding to a design that is a feasible one. Results of a thorough experimental activity evidence that GIDnet outperforms earlier approaches in the literature. Generative grammar ; Computer science ; Point (geometry) ; Generative Design ; Space (punctuation) ; Architecture ; Artificial intelligence ; Artificial neural network ; Inverse ; Machine learning ; Theoretical computer science ; Mathematics ; Engineering Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Zibin Bi
Source: Academic Journal of Materials & Chemistry (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Component Analysis and Identification of Glass Products Based on Support Vector Machine Comprehensive Classification Model It is of great significance to study the ancient Silk Road to analyze and identify the types and components of glass products. In this paper, support vector machine (SVM) method is used to study the statistical rule and internal relation between different chemical components of ancient glass products and how to classify ancient glass products. Firstly, the high-potassium glass and lead-barium glass were further divided into subclasses, and then the K-means cluster analysis was carried out. After many fitting repeats, the optimal number of classes was obtained as 5, and the chemical composition characteristics of the classified types were quantitatively analyzed and their ranges were divided. Finally, the five glass types were named as PbO- BaO- SiO2, PbO (~)- BaO- SiO2, K2O- SiO2- Al2O3, PbO- BaO (~)- SiO2- CuO and K2O- SiO2 (~).Then the classification criteria of each category are given on the basis of K-means clustering, and then combined with the support vector machine model, the unknown categories of glass artifacts are classified by machine learning, and finally the final results are obtained by fitting the five ancient glass artifact subcategories based on multiple chemical compositions. And the result probability is predicted from the 5 subclasses, and the maximum probability is taken as the final predicted category. Support vector machine ; Artifact (error) ; Cluster analysis ; Identification (biology) ; Artificial intelligence ; Similarity (geometry) ; Statistical analysis ; Basis (linear algebra) ; Computer science ; Component (thermodynamics) ; Pattern recognition (psychology) ; Mathematics ; Statistics ; Thermodynamics ; Physics Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Water Quality Monitoring and Analysis
Authors: Jiaqing Jiang
Source: arXiv (Cornell University) (preprint, 2023-09-18)
Field: Materials Science — Materials Chemistry

Local Hamiltonian Problem with succinct ground state is MA-Complete Finding the ground energy of a quantum system is a fundamental problem in condensed matter physics and quantum chemistry. Existing classical algorithms for tackling this problem often assume that the ground state has a succinct classical description, i.e. a poly-size classical circuit for computing the amplitude. Notable examples of succinct states encompass matrix product states, contractible projected entangled pair states, and states that can be represented by classical neural networks. We study the complexity of the local Hamiltonian problem with succinct ground state. We prove this problem is MA-Complete. The Hamiltonian we consider is general and might not be stoquastic. The MA verification protocol is based on the fixed node quantum Monte Carlo method, particularly the variant of the continuous-time Markov chain introduced by Bravyi et.al. [BCGL22]. Based on our work, we also introduce a notion of strong guided states, and conjecture that the local Hamiltonian problem with strong guided state is MA-Complete, which will be in contrast with the QCMA-Complete result of the local Hamiltonian problem with standard guided states [WFC23,GLG22]. Hamiltonian (control theory) ; Ground state ; Adiabatic quantum computation ; Quantum ; Conjecture ; Markov chain ; Contractible space ; Mathematics ; Physics ; Quantum mechanics ; Statistical physics ; Quantum computer ; Discrete mathematics ; Pure mathematics ; Mathematical optimization Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Markov Chains and Monte Carlo Methods
Authors: Davide Avagliano | Marta Skreta | Sebastian Arellano-Rubach | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2023-11-14)
Field: Materials Science — Materials Chemistry

DELFI: A computer oracle for recommending density functionals for excited states calculations Density functional theory (DFT) is known as the workhorse of computational quantum chemistry. One of its main limitations, if not the main one, is that choosing the right functional to employ is a non-trivial task left for human experts. The choice is particularly hard for excited states calculations, when using the time-dependent formulation of DFT (TD-DFT). This is not only due to the approximations and limitations of the method, but also because the photophysical properties of a molecule are defined by a manifold of states that all need to be properly described in a balanced manner to obtain an accurate photochemical picture. This includes not only the relative energy of the states, but also capturing the correct character, order and intensity of the transitions. In this work, we developed a scoring system to quantitatively define the accuracy of an excited states calculation by simultaneously considering at the same time all these properties of a manifold of states. The scoring system is generalizable to any level of theory, we here applied it to a large dataset of organic molecules, calculating 38 scores for as many common functionals of different type and rung, against a higher accuracy method for a total of more than 820.000 single point calculations. The results of these calculations are collected in a database that we released and made it open, providing 4 million datapoints to be used by the community for future applications. We used the scores we extrapolated to train a graph attention neural network that is used to predict the 38 scores for molecules represented as 2D graphs. We call this oracle DELFI (Data-driven EvaLuation of Functionals by Inference), which can be used to quickly screen and predict the ranking of functionals to calculate optical properties of organic molecules. A corresponding web application allows to easily run DELFI and analyze the results, alleviating the hurdle of choosing the right functional for TD-DFT calculations. Excited state ; Density functional theory ; Oracle ; Computer science ; Manifold (fluid mechanics) ; Point (geometry) ; Algorithm ; Statistical physics ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Various Chemistry Research Topics