jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Surya Prakash Tiwari | Wei Shi | Samir Budhathoki | James S. Baker | David Hopkinson | Janice A. Steckel
Source: ChemRxiv (preprint, 2023-05-12)
Field: Materials Science — Materials Chemistry

Creation of Polymer Datasets with Targeted Backbones for Screening of Gas Permeability and Selectivity A simple approach was developed to computationally construct a polymer by composing simplified molecular-input line-entry system (SMILES) strings of a polymer backbone and a molecular fragment. This method was used to create 14 polymer datasets by combining seven polymer backbones and two large molecular datasets (ZINC and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, polyethylene oxide (PEO), poly-allyl glycidyl ether, and polyphosphazene. The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study used machine learning (ML) models to screen the polymers for CO2/CH4 and CO2/N2 gas separation using membranes and several polymers of interest were identified. Polymer ; Selectivity ; Polydimethylsiloxane ; Materials science ; Peptoid ; Polymer chemistry ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Membrane Separation and Gas Transport ; Fuel Cells and Related Materials
Authors: Joakim Linja | Joonas Hämäläinen | Antti Pihlajamäki | P. Nieminen | Sami Malola | Hannu Häkkinen | Tommi Kärkkäinen
Source: arXiv (Cornell University) (preprint, 2023-02-28)
Field: Materials Science — Materials Chemistry

Knowledge Discovery from Atomic Structures using Feature Importances Molecular-level understanding of the interactions between the constituents of an atomic structure is essential for designing novel materials in various applications. This need goes beyond the basic knowledge of the number and types of atoms, their chemical composition, and the character of the chemical interactions. The bigger picture takes place on the quantum level which can be addressed by using the Density-functional theory (DFT). Use of DFT, however, is a computationally taxing process, and its results do not readily provide easily interpretable insight into the atomic interactions which would be useful information in material design. An alternative way to address atomic interactions is to use an interpretable machine learning approach, where a predictive DFT surrogate is constructed and analyzed. The purpose of this paper is to propose such a procedure using a modification of the recently published interpretable distance-based regression method. Our tests with a representative benchmark set of molecules and a complex hybrid nanoparticle confirm the viability and usefulness of the proposed approach. Benchmark (surveying) ; Computer science ; Quantum chemical ; Set (abstract data type) ; Character (mathematics) ; Process (computing) ; Feature (linguistics) ; Density functional theory ; Artificial intelligence ; Machine learning ; Molecule ; Mathematics ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: V.K. Gupta | Ajit Sapre
Source: Journal of Modern Polymer Chemistry and Materials (article, 2023-12-30)
Field: Materials Science — Biomaterials

Advances in High Performance Polymers and Composites Materials: Development Opportunities The development of high-performance polymers continues to grow in both academia and industry due to their numerous benefits for society and diverse commercial potential. Further, polymer materials with varied functional properties can be modified through blends and composites to enhance their performance. The unique functional, thermal, and mechanical characteristics, along with the ease of processing, have created new opportunities for product applications in various sectors, including packaging, agriculture, energy, automobiles, infrastructure, retail, aerospace, defense, and other growing industries. This review primarily focuses on the scientific and technological advances in high-performance materials such as polyolefins and elastomers, as well as their composites. Furthermore, it addresses the environmental concerns arising from plastic mismanagement, emphasizing sustainability aspects, including the circular economy and the development of biodegradable, bio-compostable and bio-based polymers. The review also discusses new directions in polymer science and engineering, such as life cycle management, flexible manufacturing, and the integration of artificial intelligence, machine learning, and deep learning to accelerate sustainable development. Sustainability ; Aerospace ; Circular economy ; Elastomer ; New product development ; Materials science ; Manufacturing engineering ; Nanotechnology ; Business ; Engineering ; Composite material ; Marketing ; Aerospace engineering biodegradable polymer synthesis and properties ; Chemistry and Chemical Engineering ; Nanotechnology research and applications
Authors: Jose Martinez-Trespalacios | John R. Castro-Suárez
Source: Journal of Southwest Jiaotong University (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

APPLICATION OF FOURIER TRANSFORM SPECTROSCOPY AND MACHINE LEARNING TO DETERMINE GREEN ETHYLENE CONTENT IN SAMPLES OF ETHYLENE-PROPYLENE IMPACT COPOLYMERS Impact copolymers are synthesized using ethylene and propylene monomers in various proportions. The mechanical properties of these polymers are directly influenced by the ethylene content because an increase in ethylene results in significant enhancements in the copolymer properties. To precisely quantify the ethylene content, ATR-FTIR was employed, leveraging calibration methodologies and prediction models based on machine learning (ML). Principal component regression (PCR), artificial neural networks (ANN), support vector regression (SVR), and k-nearest neighbor (kNN) were used. Green ethylene in concentrations ranging from 0.5% to 53% was used. R 2 , root mean square error of calibration (NRMSE), and root mean square error of prediction (RMSEP) were used as parameters for judging the best ML model. This study demonstrated that focusing on bands between 690 and 1325 cm -1 enables effective classification and prediction of green ethylene concentrations. Even when all measured bands within this range are reduced to a space of only two principal components, a remarkable 97% of the variance is explained. The results suggest that mid-infrared spectroscopy could be a useful tool for quantitative analysis of green ethylene when machine learning algorithms are used. Keywords: Green Ethylene, k-Nearest Neighbor, Support Vector Regression, Artificial Neural Network, Fourier Transform Infrared Spectroscopy DOI： https://doi.org/10.35741/issn.0258-2724.58.6.15 Principal component analysis ; Ethylene ; Copolymer ; Fourier transform infrared spectroscopy ; Mean squared error ; Calibration ; Support vector machine ; Spectroscopy ; Biological system ; Materials science ; Fourier transform ; Partial least squares regression ; Regression analysis ; Polymer ; Mathematics ; Artificial intelligence ; Computer science ; Statistics ; Chemistry ; Chemical engineering ; Physics ; Organic chemistry ; Engineering ; Composite material Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Jiaxin Xu | Tengfei Luo
Source: Research Square (preprint, 2023-12-28)
Field: Materials Science — Materials Chemistry

Unlocking Enhanced Thermal Conductivity in Polymer Blends through Active Learning Abstract Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has emerged as a vital field for efficiently designing and developing polymeric materials. However, the focus of polymer informatics has predominantly centered on single-component polymers, leaving the vast chemical space of polymer blends relatively unexplored. This study employs a high-throughput molecular dynamics (MD) simulation approach combined with active learning (AL) to uncover polymer blends with enhanced thermal conductivity (TC) compared to the constituent single-component polymers. Initially, TC values for about 600 amorphous single-component polymers and approximately 200 amorphous polymer blends with varying blending ratios are determined through MD simulations. The optimal representation method for polymer blends is also identified, which involves a weighted sum approach that extends existing polymer representation from single-component polymers to polymer blends. An AL framework, combining MD simulation and ML models, is employed to explore the TC of an unlabeled dataset comprising approximately 550,000 potential polymer blends. The AL framework proves highly effective in accelerating the discovery of high-performance polymer blends for thermal transport. Additionally, we delve into the relationship between TC, radius of gyration (Rg), and hydrogen bonding, highlighting the roles of inter- and intra-chain interactions in thermal transport in amorphous polymer blends. A significant positive association between TC and Rg improvement and an indirect contribution from H-bond interaction to TC enhancement are revealed through a log-linear model and odd ratio calculation based on the polymer blend dataset, emphasizing the impact of increasing Rg and H-bond interactions on enhancing polymer blend TC. Thermal conductivity ; Materials science ; Conductivity ; Polymer ; Thermal ; Active learning (machine learning) ; Polymer science ; Chemical engineering ; Composite material ; Computer science ; Artificial intelligence ; Thermodynamics ; Physics ; Chemistry ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design
Authors: Jiawei Guo | Tyler Sours | Sam Holton | Chenghan Sun | Ambarish Kulkarni
Source: ChemRxiv (preprint, 2023-04-24)
Field: Materials Science — Materials Chemistry

Quantifying the Limits of Methane Activation in Cu-exchanged Zeolites using Reactive and Interpretable Machine Learning based Potentials Natural gas remains an essential energy source for the industrial and residential sectors. However, selective valorization of methane (the main component of natural gas) into more mobile liquid energy carriers such as methanol remains challenging. Inspired by pMMO enzymes, many recent studies have examined Cu-exchanged zeolites as promising catalysts, specifically through [CuOCu]2+ sites. These efforts, in part, have been motivated by the possibility of finding an elusive “Goldilocks” active site or topology that can outperform known catalysts while also maintaining selectivity towards methanol. As large-scale experiments with 1000s of material variations are impossible, theory will likely play an important role. Although computational screening studies are now routine for metals and alloys, similar studies for zeolites are not as straightforward due to the diversity of local chemical environments, and the aforementioned studies are not trivial using the traditional density functional theory (DFT)-based approach. Therefore, the overarching goal of this study is to leverage large-scale DFT calculations to develop a reactive machine learning-based potential (rMLP) capable of systematically sampling the stability and reactivity of all [CuOCu]2+ sites within a representative set of zeolites. Specifically, using methane activation as a prototypical example of an industrially relevant zeolite-catalyzed reaction, we have developed a novel multistage active learning algorithm that preferentially samples the potential energy surface of the system near the transition state of methane activation. We show that the resulting rMLP replaces the expensive DFT-based NEB calculations without any appreciable loss in accuracy (within 0.07 eV of the DFT computed energy barriers) – we evaluate C-H bond activation energies for 5,400 distinct sites across 52 zeolites and obtain 3,356 valid sites suitable for methane activation. By replacing the expensive DFT-based NEB calculations with rMLPs, we now report an exhaustive high-throughput screening study of thousands of [CuOCu]2+ sites in zeolites, comparing the maximum rates of methane activation across 52 zeolite topologies and more than 3,000 sites. To the best of our knowledge, this work represents the first example of using reactive MLPs to identify the transition state geometries and screen the catalytic performance of thousands of zeolite-based active sites at DFT accuracies. Methane ; Density functional theory ; Catalysis ; Leverage (statistics) ; Chemistry ; Active site ; Methanol ; Zeolite ; Biochemical engineering ; Natural gas ; Computer science ; Computational chemistry ; Machine learning ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science
Authors: Lin Yao | Wentao Guo | Zhen Wang | Shang Xiang | Wentan Liu | Guolin Ke
Source: arXiv (Cornell University) (preprint, 2023-09-27)
Field: Materials Science — Materials Chemistry

Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis Single-step retrosynthesis (SSR) in organic chemistry is increasingly benefiting from deep learning (DL) techniques in computer-aided synthesis design. While template-free DL models are flexible and promising for retrosynthesis prediction, they often ignore vital 2D molecular information and struggle with atom alignment for node generation, resulting in lower performance compared to the template-based and semi-template-based methods. To address these issues, we introduce Node-Aligned Graph-to-Graph (NAG2G), a transformer-based template-free DL model. NAG2G combines 2D molecular graphs and 3D conformations to retain comprehensive molecular details and incorporates product-reactant atom mapping through node alignment which determines the order of the node-by-node graph outputs process in an auto-regressive manner. Through rigorous benchmarking and detailed case studies, we have demonstrated that NAG2G stands out with its remarkable predictive accuracy on the expansive datasets of USPTO-50k and USPTO-FULL. Moreover, the model's practical utility is underscored by its successful prediction of synthesis pathways for multiple drug candidate molecules. This not only proves NAG2G's robustness but also its potential to revolutionize the prediction of complex chemical synthesis processes for future synthetic route design tasks. Retrosynthetic analysis ; Benchmarking ; Computer science ; Graph ; Artificial intelligence ; Node (physics) ; Data mining ; Theoretical computer science ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Andi Wijaya | Julian Wagner | Bernhard Sartory | Roland Brunner
Source: Research Square (preprint, 2023-08-18)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Driven Material Design for Porous Materials Microstructure ; Workflow ; Artificial intelligence ; Porosity ; Computer science ; Materials science ; Machine learning ; Composite material ; Database Machine Learning in Materials Science ; Injection Molding Process and Properties ; Electron and X-Ray Spectroscopy Techniques
Authors: Sergei Manzhos | Manabu Ihara
Source: arXiv (Cornell University) (preprint, 2023-02-11)
Field: Materials Science — Materials Chemistry

Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization Representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates (corresponding of different orders of coupling) are useful in quantum dynamics and other applications, especially where integration is needed. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Comm. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces. Artificial neural network ; Representation (politics) ; Coupling (piping) ; Nonlinear system ; Gaussian ; Computer science ; Energy (signal processing) ; Multivariate statistics ; Activation function ; Quantum ; Process (computing) ; Algorithm ; Applied mathematics ; Artificial intelligence ; Mathematics ; Topology (electrical circuits) ; Biological system ; Physics ; Machine learning ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Mass Spectrometry Techniques and Applications
Authors: Ryan Jacobs | Takuya Yamamoto | G.R. Odette | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2023-09-05)
Field: Materials Science — Metals and Alloys

Predictions and Uncertainty Estimates of Reactor Pressure Vessel Steel Embrittlement Using Machine Learning An essential aspect of extending safe operation of the active nuclear reactors is understanding and predicting the embrittlement that occurs in the steels that make up the Reactor pressure vessel (RPV). In this work we integrate state of the art machine learning methods using ensembles of neural networks with unprecedented data collection and integration to develop a new model for RPV steel embrittlement. The new model has multiple improvements over previous machine learning and hand-tuned efforts, including greater accuracy (e.g., at high-fluence relevant for extending the life of present reactors), wider domain of applicability (e.g., including a wide-range of compositions), uncertainty quantification, and online accessibility for easy use by the community. These improvements provide a model with significant new capabilities, including the ability to easily and accurately explore compositions, flux, and fluence effects on RPV steel embrittlement for the first time. Furthermore, our detailed comparisons show our approach improves on the leading American Society for Testing and Materials (ASTM) E900-15 standard model for RPV embrittlement on every metric we assessed, demonstrating the efficacy of machine learning approaches for this type of highly demanding materials property prediction. Embrittlement ; Reactor pressure vessel ; Metric (unit) ; Neutron flux ; Nuclear engineering ; Materials science ; Pressure vessel ; Artificial neural network ; Machine learning ; Fluence ; Artificial intelligence ; Computer science ; Metallurgy ; Engineering ; Irradiation ; Nuclear physics ; Composite material ; Neutron Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Fatigue and fracture mechanics
Authors: Julian Lißner | Felix Fritzen
Source: arXiv (Cornell University) (preprint, 2023-02-24)
Field: Materials Science — Materials Chemistry

Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks Beyond the generally deployed features for microstructure property prediction this study aims to improve the machine learned prediction by developing novel feature descriptors. Therefore, Bayesian infused data mining is conducted to acquire samples containing characteristics inexplicable to the current feature set, and suitable feature descriptors to describe these characteristics are proposed. The iterative development of feature descriptors resulted in 37 novel features, being able to reduce the prediction error by roughly one third. To further improve the predictive model, convolutional neural networks (Conv Nets) are deployed to generate auxiliary features in a supervised machine learning manner. The Conv Nets were able to outperform the feature based approach. A key ingredient for that is a newly proposed data augmentation scheme and the development of so-called deep inception modules. A combination of the feature based approach and the convolutional neural network leads to a hybrid neural network: A parallel deployment of the both neural network archetypes in a single model achieved a relative rooted mean squared error below 1%, more than halving the error compared to prior models operating on the same data. The hybrid neural network was found powerful enough to be extended to predict variable material parameters, from a low to high phase contrast, while allowing for arbitrary microstructure geometry at the same time. Computer science ; Artificial intelligence ; Convolutional neural network ; Artificial neural network ; Feature (linguistics) ; Machine learning ; Pattern recognition (psychology) ; Bayesian probability ; Data mining Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Mineral Processing and Grinding
Authors: Lukas Fuchs | Tom Kirstein | Christoph Mahr | Orkun Furat | Valentin Baric | Andreas Rosenauer | Lutz Maedler | Volker Schmidt
Source: arXiv (Cornell University) (preprint, 2023-10-27)
Field: Materials Science — Materials Chemistry

Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data The structural characterization of hetero-aggregates in 3D is of great interest, e.g., for deriving process-structure or structure-property relationships. However, since 3D imaging techniques are often difficult to perform as well as time and cost intensive, a characterization of hetero-aggregates based on 2D image data is desirable, but often non-trivial. To overcome the issues of characterizing 3D structures from 2D measurements, a method is presented that relies on machine learning combined with methods of spatial stochastic modeling, where the latter are utilized for the generation of synthetic training data. This kind of training data has the advantage that time-consuming experiments for the synthesis of differently structured materials followed by their 3D imaging can be avoided. More precisely, a parametric stochastic 3D model is presented, from which a wide spectrum of virtual hetero-aggregates can be generated. Additionally, the virtual structures are passed to a physics-based simulation tool in order to generate virtual scanning transmission electron microscopy (STEM) images. The preset parameters of the 3D model together with the simulated STEM images serve as a database for the training of convolutional neural networks, which can be used to determine the parameters of the underlying 3D model and, consequently, to predict 3D structures of hetero-aggregates from 2D STEM images. Furthermore, an error analysis is performed to evaluate the prediction power of the trained neural networks with respect to structural descriptors, e.g. the hetero-coordination number. Computer science ; Parametric statistics ; Characterization (materials science) ; Convolutional neural network ; Process (computing) ; Artificial intelligence ; Artificial neural network ; Synthetic data ; Property (philosophy) ; Pattern recognition (psychology) ; Parametric model ; Machine learning ; Algorithm ; Materials science ; Mathematics ; Nanotechnology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Hydrocarbon exploration and reservoir analysis