jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joseph Kern | Shruti Venkatram | Manali Banerjee | Blair Brettmann | Rampi Ramprasad
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Solvent selection for polymers enabled by generalized chemical fingerprinting and machine learning We present machine learning models trained on experimental data to predict room-temperature solubility for any polymer-solvent pair. The new models are a significant advancement over past data-driven work, in terms of protocol, validity, and versatility. A generalizable fingerprinting method is used for the polymers and solvents, making it possible, in principle, to handle any polymer-solvent combination. Our data-driven approach achieves high accuracy when either both the polymer and solvent or just the polymer has been seen during the training phase. Model performance is modest though when a solvent (in a newly queried polymer-solvent pair) is not part of the training set. This is likely because the number of unique solvents in our data set is small (much smaller than the number of polymers). Nevertheless, as the data set increases in size, especially as the solvent set becomes more diverse, the overall predictive performance is expected to improve. Selection (genetic algorithm) ; Polymer ; Solvent ; Computer science ; Chemistry ; Biochemical engineering ; Combinatorial chemistry ; Artificial intelligence ; Nanotechnology ; Biological system ; Materials science ; Organic chemistry ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Timothée Devergne | Théo Magrino | Fabio Pietrucci | A. Marco Saitta
Source: Journal of Chemical Theory and Computation (article, 2022-08-05)
Field: Materials Science — Materials Chemistry

Combining Machine Learning Approaches and Accurate <i>Ab Initio</i> Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution The study of the thermodynamics, kinetics, and microscopic mechanisms of chemical reactions in solution requires the use of advanced free-energy methods for predictions to be quantitative. This task is however a formidable one for atomistic simulation methods, as the cost of quantum-based <i>ab initio</i> approaches, to obtain statistically meaningful samplings of the relevant chemical spaces and networks, becomes exceedingly heavy. In this work, we critically assess the optimal structure and minimal size of an <i>ab initio</i> training set able to lead to accurate free-energy profiles sampled with neural network potentials. The results allow one to propose an <i>ab initio</i> protocol where the <i>ad hoc</i> inclusion of a machine-learning (ML)-based task can significantly increase the computational efficiency, while keeping the <i>ab initio</i> accuracy and, at the same time, avoiding some of the notorious extrapolation risks in typical atomistic ML approaches. We focus on two representative, and computationally challenging, reaction steps of the classic Strecker-cyanohydrin mechanism for glycine synthesis in water solution, where the main precursors are formaldehyde and hydrogen cyanide. We demonstrate that indistinguishable <i>ab initio</i> quality results are obtained, thanks to the ML subprotocol, at about 1 order of magnitude less of computational load. Ab initio ; Computer science ; Extrapolation ; Computational chemistry ; Potential energy surface ; Chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Zakiya Shireen | Hansani Weeratunge | Adrian Menzel | Andrew W. Phillips | Ronald G. Larson | Kate Smith‐Miles | Elnaz Hajizadeh
Source: npj Computational Materials (article, 2022-11-03)
Field: Materials Science — Materials Chemistry

A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer Abstract This work presents a framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The framework combines bottom-up and top-down approaches of coarse-grained model parameters by integrating machine learning (ML) with optimization algorithms. In the bottom-up approach, bonded interactions of the CG model are optimized using deep neural networks (DNN), where atomistic bonded distributions are matched. In the top-down approach, optimization of nonbonded parameters is accomplished by reproducing the temperature-dependent experimental density. We demonstrate that developed framework addresses the thermodynamic consistency and transferability issues associated with the classical coarse-graining approaches. The efficiency and transferability of the CG model is demonstrated through accurate predictions of chain statistics, the limiting behavior of the glass transition temperature, diffusion, and stress relaxation, where none were included in the parametrization process. The accuracy of the predicted properties are evaluated in context of molecular theories and available experimental data. Granularity ; Parametrization (atmospheric modeling) ; Computer science ; Context (archaeology) ; Consistency (knowledge bases) ; Relaxation (psychology) ; Transferability ; Isotropy ; Limiting ; Artificial neural network ; Artificial intelligence ; Algorithm ; Statistical physics ; Machine learning ; Physics ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Joshua Ojih | Uche Onyekpe | Alejandro Rodriguez | Jianjun Hu | Chengxiao Peng | Ming Hu
Source: ACS Applied Materials & Interfaces (article, 2022-09-15)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Discovery of Promising Thermal Energy Storage Materials with High Heat Capacity Thermal energy storage offers numerous benefits by reducing energy consumption and promoting the use of renewable energy sources. Thermal energy storage materials have been investigated for many decades with the aim of improving the overall efficiency of energy systems. However, finding solid materials that meet the requirement of high heat capacity has been a grand challenge for material scientists. Herewith, by training various machine learning models on 3377 high-quality data from full density functional theory (DFT) calculations, we efficiently search for potential materials with high heat capacity. We build four traditional machine learning models and two graph neural network models. Cross-comparison of the prediction performance and model accuracy was conducted among different models. The deeperGATGNN model exhibits high prediction accuracy and is used for predicting the heat capacity of 32,026 structures screened from the open quantum material database. We gain deep insight into the correlation between heat capacity and structure descriptors such as space group, prototype, lattice volume, atomic weight, etc. Twenty-two structures were predicted to possess high heat capacity, and the results were further validated with DFT calculations. We also identified one special structure, namely, MnIn<sub>2</sub>Se<sub>4</sub>, with space group no. 227 (<i>Fd</i>3̅<i>m</i>), that exhibits extremely high heat capacity, even higher than that of the Dulong-Petit limit at room temperature. This study paves the way for accelerating the discovery of novel thermal energy storage materials by combining machine learning with minimal DFT inquiry. Thermal energy storage ; Heat capacity ; Materials science ; Energy storage ; Machine learning ; Computer science ; Artificial intelligence ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Ayush Pratap | Neha Sardana
Source: Materials Today Proceedings (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-based image processing in materials science and engineering: A review Computer science ; Cluster analysis ; Interpretation (philosophy) ; Artificial intelligence ; Machine learning ; Image processing ; Science and engineering ; Data science ; Data collection ; Data processing ; Image (mathematics) ; Engineering ; Database Machine Learning in Materials Science ; Mineral Processing and Grinding ; Computational Drug Discovery Methods
Authors: I. A. Balyakin | A. A. Yuryev | В. В. Филиппов | Б. Р. Гельчинский
Source: Computational Materials Science (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

Viscosity of liquid gallium: Neural network potential molecular dynamics and experimental study Gallium ; Viscosity ; Molecular dynamics ; Dynamics (music) ; Materials science ; Thermodynamics ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Metallurgy ; Composite material Machine Learning in Materials Science ; Theoretical and Computational Physics ; Material Dynamics and Properties
Authors: Jingxian Huang | Zhen Li | Wei Zhang | Zhuoyuan Lv | Shuying Dong | Yan Feng | Rongxia Liu | Yan Zhao
Source: Food Chemistry (article, 2022-10-06)
Field: Chemistry — Analytical Chemistry

Explainable machine learning-assisted origin identification: Chemical profiling of five lotus (Nelumbo nucifera Gaertn.) parts Lotus ; Phytochemical ; Nelumbo nucifera ; Metabolomics ; Identification (biology) ; Profiling (computer programming) ; Tracing ; Computational biology ; Artificial intelligence ; Computer science ; Biology ; Machine learning ; Biological system ; Chemistry ; Botany ; Bioinformatics Chromatography in Natural Products ; Metabolomics and Mass Spectrometry Studies ; Traditional Chinese Medicine Analysis
Authors: Kristen Patnode | Bakhtiyor Rasulev | Andriy Voronov
Source: ACS Applied Materials & Interfaces (article, 2022-02-04)
Field: Materials Science — Biomaterials

Synergistic Behavior of Plant Proteins and Biobased Latexes in Bioplastic Food Packaging Materials: Experimental and Machine Learning Study Plant-based proteins are attractive components which may serve as sustainable alternatives to current petrochemical products. Both soy protein and major corn protein, zein, are of interest in food packaging applications due to their sustainability, biodegradation properties, and inherent physicochemical properties. This study discusses the development of bioplastic materials, where it explores the effects of combining zein, soy protein, and plasticizing latexes derived from plant oil-based monomers (POBMs) on properties of resulting bioplastic films. By looking for synergistic effects of soy protein's inherent film formation ability and zein's higher strength, we prepare strong yet flexible soy-zein films as materials, called proteoposites. Incorporation of natural additive POBM-latexes helps to plasticize and hydrophobize the bioplastic films and thus to improve mechanical and barrier properties. Variation of the POBM-latexes' particle size further aims to enhance the performance of resulting bioplastic films. As a result, modified soy-zein proteoposite films with improved moisture resistance, enhanced mechanical behavior, and greater barrier properties were developed. Machine learning-based computational models were utilized in order to find main structural factors affecting the bioplastic's properties and develop a quantitative structure-property relationship model between the physicochemical properties of the film components and the resulted bioplastics' properties and performance. The developed model effectively predicts experimental outcomes with >85% (<i>R</i><sup>2</sup>: 0.85) accuracy. The newly synthesized proteoposites confirmed the machine learning model predictions. As a result, proteoposite films made of two plant proteins and modified with POBM-latexes can be considered as an attractive and viable replacement for petrochemical food packaging products. Bioplastic ; Materials science ; Soy protein ; Biodegradation ; Food packaging ; Petrochemical ; Composite material ; Organic chemistry ; Food science ; Waste management ; Chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution
Authors: Xinkai Xu | Dipesh Aggarwal | Karthik Shankar
Source: Nanomaterials (review, 2022-02-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder-Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions. Plasmon ; Inverse ; Property (philosophy) ; Current (fluid) ; Nanostructure ; Inverse problem ; Materials science ; Computer science ; Nanotechnology ; Artificial intelligence ; Engineering ; Electrical engineering ; Mathematics ; Optoelectronics ; Mathematical analysis ; Geometry Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications
Authors: A.E. Hughés | David A. Winkler | James Carr | Peter Lee | Sam Yang | Majid Laleh | Yongjun Tan
Source: Corrosion and Materials Degradation (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning Machine learning (ML) is providing a new design paradigm for many areas of technology, including corrosion inhibition. However, ML models require relatively large and diverse training sets to be most effective. This paper provides an overview of developments in corrosion inhibitor research, focussing on how corrosion performance data can be incorporated into machine learning and how large sets of inhibitor performance data that are suitable for training robust ML models can be developed through various corrosion inhibition testing approaches, especially high-throughput performance testing. It examines different types of environments where corrosion by-products and electrolytes operate, with a view to understanding how conventional inhibitor testing methods may be better designed, chosen, and applied to obtain the most useful performance data for inhibitors. The authors explore the role of modern characterisation techniques in defining corrosion chemistry in occluded structures (e.g., lap joints) and examine how corrosion inhibition databases generated by these techniques can be exemplified by recent developments. Finally, the authors briefly discuss how the effects of specific structures, alloy microstructures, leaching structures, and kinetics in paint films may be incorporated into machine learning strategies. Corrosion ; Corrosion inhibitor ; Computer science ; Leaching (pedology) ; Artificial intelligence ; Machine learning ; Materials science ; Metallurgy ; Environmental science Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Qi‐Jun Hong
Source: Computational Materials Science (article, 2022-08-13)
Field: Materials Science — Materials Chemistry

Melting temperature prediction via first principles and deep learning Computer science ; Chemical space ; Metastability ; Deep learning ; Algorithm ; Artificial neural network ; Computational science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Anwar Shafe | Collin D. Wick | Andrew J. Peters | Xiyuan Liu | Guoqiang Li
Source: Polymer (article, 2022-01-23)
Field: Materials Science — Polymers and Plastics

Effect of atomistic fingerprints on thermomechanical properties of epoxy-diamine thermoset shape memory polymers Thermosetting polymer ; Shape-memory polymer ; Polymer ; Materials science ; Epoxy ; Shape-memory alloy ; Computer science ; Smart polymer ; Molecular dynamics ; Composite material ; Computational chemistry ; Chemistry Polymer composites and self-healing ; Photochromic and Fluorescence Chemistry ; Photopolymerization techniques and applications
Authors: Hanna Türk | Elisabetta Landini | Christian Künkel | Johannes T. Margraf | Karsten Reuter
Source: Chemistry of Materials (article, 2022-10-19)
Field: Materials Science — Materials Chemistry

Assessing Deep Generative Models in Chemical Composition Space The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table since even a single-crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on reinforcement learning, variational autoencoders, and generative adversarial networks for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2 million main-group Elpasolites, the precision, coverage, and diversity of the generated materials are rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed. Generative grammar ; Chemical space ; Space (punctuation) ; Computer science ; Artificial intelligence ; Range (aeronautics) ; Hyperparameter ; Generative Design ; Generative model ; Machine learning ; Composition (language) ; Reinforcement learning ; Theoretical computer science ; Materials science ; Biology ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Sergey A. Stel’makh | Evgenii M. Shcherban’ | Alexey N. Beskopylny | Levon R. Mailyan | Besarion Meskhi | Irina Razveeva | Alexey Kozhakin | Nikita Beskopylny
Source: Materials (article, 2022-09-28)
Field: Materials Science — Materials Chemistry

Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods Currently, one of the topical areas of application of artificial intelligence methods in industrial production is neural networks, which allow for predicting the performance properties of products and structures that depend on the characteristics of the initial components and process parameters. The purpose of the study was to develop and train a neural network and an ensemble model to predict the mechanical properties of lightweight fiber-reinforced concrete using the accumulated empirical database and data from construction industry enterprises, and to improve production processes in the construction industry. The study applied deep learning and an ensemble of regression trees. The empirical base is the result of testing a series of experimental compositions of fiber-reinforced concrete. The predicted properties are cubic compressive strength, prismatic compressive strength, flexural tensile strength, and axial tensile strength. The quantitative picture of the accuracy of the applied methods for strength characteristics varies for the deep neural network method from 0.15 to 0.73 (MAE), from 0.17 to 0.89 (RMSE), and from 0.98% to 6.62% (MAPE), and for the ensemble of regression trees, from 0.11 to 0.62 (MAE), from 0.15 to 0.80 (RMSE), and from 1.30% to 3.4% (MAPE). Both methods have shown high efficiency in relation to such a hard-to-predict material as concrete, which is so heterogeneous in structure and depends on many factors. The value of the developed models lies in the possibility of obtaining additional useful information in the process of preparing highly functional lightweight fiber-reinforced concrete without additional experiments. Artificial neural network ; Ultimate tensile strength ; Compressive strength ; Flexural strength ; Mean absolute percentage error ; Fiber ; Materials science ; Empirical modelling ; Mean squared error ; Regression analysis ; Computer science ; Ensemble learning ; Structural engineering ; Artificial intelligence ; Machine learning ; Composite material ; Mathematics ; Engineering ; Statistics ; Simulation Structural mechanics and materials ; Innovative concrete reinforcement materials ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Ruichao Zhu | Jiafu Wang | Jinming Jiang | Cuilian Xu | Che Liu | Yuxiang Jia | Sai Sui | Zhongtao Zhang | Tonghao Liu | Zuntian Chu | Tie Jun Cui | Shaobo Qu
Source: Photonics Research (article, 2022-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning-empowered multispectral metafilm with reduced radar cross section, low infrared emissivity, and visible transparency For camouflage applications, the performance requirements for metamaterials in different electromagnetic spectra are usually contradictory, which makes it difficult to develop satisfactory design schemes with multispectral compatibility. Fortunately, empowered by machine learning, metamaterial design is no longer limited to directly solving Maxwell’s equations. The design schemes and experiences of metamaterials can be analyzed, summarized, and learned by computers, which will significantly improve the design efficiency for the sake of practical engineering applications. Here, we resort to the machine learning to solve the multispectral compatibility problem of metamaterials and demonstrate the design of a new metafilm with multiple mechanisms that can realize small microwave scattering, low infrared emissivity, and visible transparency simultaneously using a multilayer backpropagation neural network. The rapid evolution of structural design is realized by establishing a mapping between spectral curves and structural parameters. By training the network with different materials, the designed network is more adaptable. Through simulations and experimental verifications, the designed architecture has good accuracy and robustness. This paper provides a facile method for fast designs of multispectral metafilms that can find wide applications in satellite solar panels, aircraft windows, and others. Metamaterial ; Multispectral image ; Computer science ; Camouflage ; Emissivity ; Robustness (evolution) ; Artificial neural network ; Backpropagation ; Radar ; Electronic engineering ; Optics ; Artificial intelligence ; Telecommunications ; Engineering ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications