jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Murat Tamov | О. В. Руденко | Sergey V. Usanov
Source: Vestnik MGSU (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Neural network prediction of web-crushing strength of i-shaped reinforced concrete beams Introduction. Under the combined action of shear force and bending moment web-shear cracking takes place in the thin webs of reinforced concrete beams. The strength of the concrete struts between the cracks depends on the size of the web, concrete strength, parameters of the transverse and longitudinal reinforcement, and the shear span-to-depth ratio. Plane stress constitutive models for reinforced concrete are difficult to implement. For this reason, building codes employ empirical formulas for web-crushing strength which were obtained from the analysis of the existing experimental data. Using machine learning tools — artificial neural networks (ANN) — can serve as a solution that allows to take into account the impact of structural and loading parameters more accurately. The analyzed experimental base included the test results of 77 beams. The input layer of the ANN consisted of 7 independent variables, the output — of 1 dependent, and both linear and nonlinear functions were considered as activation functions. Materials and methods. The article studies I-shaped concrete beams with basalt fiber reinforced polymer and steel transverse reinforcement tested by authors. To ensure the statistical significance of independent variables in all indicators, the database of other authors was also considered. Neural networks were developed using STATISTICA software package. In the first stage, the input and output variables were normalized. The accuracy of the ANN model prediction was compared with the accuracy of regression models. In the last stage, the calculations were performed without normalizing the variables. Results. Artificial neural networks prediction has high accuracy. The relative error of prediction was 28.6 % for the regression method and 10.9 % for the ANN. For calculations without preliminary normalization relative error of prediction was 6.6 %. Conclusions. The results of research and other similar studies suggest ANN to be a promising tool for solving intractable problems of structural engineering. Artificial neural network ; Structural engineering ; Cracking ; Reinforcement ; Shear (geology) ; Test data ; Computer science ; Shear strength (soil) ; Bending ; Flexural strength ; Materials science ; Engineering ; Composite material ; Machine learning ; Geology Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Microbial Applications in Construction Materials
Authors: Xiangxu He | Xiaohan Cui | C. T. Chan
Source: PubMed (preprint, 2022-02-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Constrained tandem neural network assisted inverse design of metasurfaces for microwave absorption Designing microwave absorbers with customized spectrums is an attractive topic in both scientific and engineering communities. However, due to the massive number of design parameters involved, the design process is typically time-consuming and computationally expensive. To address this challenge, machine learning has emerged as a powerful tool for optimizing design parameters. In this work, we present an analytical model for an absorber composed of a multi-layered metasurface and propose a novel inverse design method based on a constrained tandem neural network. The network can provide structural and material parameters optimized for a given absorption spectrum, without requiring professional knowledge. Furthermore, additional physical attributes, such as absorber thickness, can be optimized when soft constraints are applied. As an illustrative example, we use the neural network to design broadband microwave absorbers with a thickness close to the causality limit imposed by the Kramers-Kronig relation. Our approach provides new insights into the reverse engineering of physical devices. Microwave ; Broadband ; Artificial neural network ; Computer science ; Tandem ; Engineering design process ; Inverse ; Absorption (acoustics) ; Process (computing) ; Network planning and design ; Limit (mathematics) ; Inverse problem ; Electronic engineering ; Computer engineering ; Artificial intelligence ; Mechanical engineering ; Engineering ; Telecommunications ; Optics ; Physics ; Mathematics ; Aerospace engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: E. A. Kosenko | А. В. Остроух | N. I. Baurova
Source: All the materials Encyclopedic Reference Book (article, 2022-02-01)
Field: Materials Science — General Materials Science

Application prospect of neuronetwork simulation for evaluation of structures and properties of polymer composite materials with hybrid matrices Artificial neural networks are applied in various fields: from economics and social studies to medicine and robotics. The neural networks allow one to solve various problems: prediction of events, associative search of information, check of product quality and many others. The recognition of images by the neural networks is probably the most popular task. The recognition of images and symbols allows one considerably to decrease labor-intensiveness and increase accuracy of various operation processes. The problems of an increase in the effectiveness and accuracy for evaluation of structures and properties of polymer composite materials (PCM) with different hybrid matrices by classification of data, using the neural networks, are discussed. The results of learning a neural network model for classification of PCM structures with different hybrid matrices are presented. After addition learning the proposed neuronetwork model can be used for evaluation of mechanical properties of PCM with different hybrid matrices and prediction of them for product design. Artificial neural network ; Artificial intelligence ; Computer science ; Product (mathematics) ; Quality (philosophy) ; Associative property ; Machine learning ; Task (project management) ; Engineering ; Mathematics ; Systems engineering Material Properties and Applications ; Industrial Engineering and Technologies
Authors: Brook Wander | Kirby Broderick | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2022-08-26)
Field: Materials Science — Materials Chemistry

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab-initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 stable/ metastable binary, transition metal intermetallics as possible catalyst candidates. On this subset of materials, we are able to predict the adsorption energy of key descriptors, *CO and *OH, with near-DFT accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity towards C2+ oxygenates from an existing microkinetic model, we identified 144 candidate materials. For 10 promising candidates, DFT calculations reveal a good correlation with our assessment using ML. Among the top elemental combinations were Pt-Ti, Pd-V, Ni-Nb, and Ti-Zn, all of which appear unexplored experimentally. Oxygenate ; Density functional theory ; Syngas ; Catalysis ; Ab initio ; Computer science ; Metastability ; Materials science ; Chemistry ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: B. Herzog | B. Casier | S. Lebègue | D. Rocca
Source: arXiv (Cornell University) (preprint, 2022-08-13)
Field: Materials Science — Materials Chemistry

Solving the Schrödinger Equation in the Configuration Space with Generative Machine Learning The configuration interaction approach provides a conceptually simple and powerful approach to solve the Schrödinger equation for realistic molecules and materials but is characterized by an unfavourable scaling, which strongly limits its practical applicability. Effectively selecting only the configurations that actually contribute to the wavefunction is a fundamental step towards practical applications. We propose a machine learning approach that iteratively trains a generative model to preferentially generate the important configurations. By considering molecular applications it is shown that convergence to chemical accuracy can be achieved much more rapidly with respect to random sampling or the Monte Carlo configuration interaction method. This work paves the way to a broader use of generative models to solve the electronic structure problem. Generative grammar ; Computer science ; Convergence (economics) ; Wave function ; Configuration space ; Simple (philosophy) ; Space (punctuation) ; Generative model ; Scaling ; Monte Carlo method ; Train ; Schrödinger equation ; Statistical physics ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Mathematics ; Physics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shuang Tang | Jenna Jean-Baptiste | Schuyler Vecchiano | Adam Lukasiewicz | Alexandria Burger
Source: arXiv (Cornell University) (preprint, 2022-01-15)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict the L-Point Direct Bandgap of Bi1-xSbx Nanomaterials With the development of modern nanoscience and nanotechnology, Bi1-xSbx can be synthesized into different nanoscale and nanostructured forms, including thin films, nanowires, nanotubes, nanoribbons, and many others. However, due to the strong correlation between electrons and holes at the L-point in the Brillouin zone, the direct band evolves in an anomalous manner under the quantum confinement when nanostructured. Due to the alloying and the low symmetry, predicting the L-point direct bandgap in a nanomaterial using either ab initio calculations or kp perturbations can be computationally costive or inaccurate. We here try to solve this problem using the machine learning methods, including the support vector regression, the regression tree, the Gaussian process regression, and the artificial neural network. A goodness-of-fit of ~0.99 can be achieved for Bi1-xSbx thin films and nanowires. Nanomaterials ; Brillouin zone ; Band gap ; Nanowire ; Percolation (cognitive psychology) ; Materials science ; Nanoscopic scale ; Support vector machine ; Condensed matter physics ; Nanotechnology ; Artificial neural network ; Quantum dot ; Ab initio ; Statistical physics ; Artificial intelligence ; Computer science ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science
Authors: Fei Zhan | Zheng, Lirong | Yao, Haodong | Geng, Zhi | Yu, Can | Han, Xue | Song, Xueqi | Chen, Shuguang | Zhao, Haifeng
Source: arXiv (Cornell University) (preprint, 2022-05-07)
Field: Materials Science — Materials Chemistry

A Graph Neural Network-Based Approach to XANES Data Analysis X-ray absorption spectroscopy (XAS) is an indispensable tool to characterize the atomic-scale three-dimensional local structure of the system, in which XANES is the most important energy region to reflect the three-dimensional structure. However quantitative analysis of three-dimensional structure from XANES requires users to have a deep understanding and accurate judgment of structural information and summarize several structural parameters, which is often difficult to achieve. In this work, We construct \textbf{physics-informed Graph neural network} and \textbf{Transformer} models for calculating XANES from the input three-dimensional structure; we improve the efficiency of the model based on the physical meaning of XAS; then we combine the model and optimization algorithm to fit the three-dimensional structure of given system. This method does not require users to summarize the structural parameters, has wide application range. It can be applied to the three-dimensional structure analysis of solid materials and has positive significance for the study of structure-function relationship in the fields of energy and catalysis. In addition, this method is expected to be developed into an online three-dimensional structure analysis method for XAS related beamlines. XANES ; X-ray absorption spectroscopy ; Computer science ; Artificial neural network ; Graph ; Transformer ; Electronic structure ; Data structure ; Spectroscopy ; Algorithm ; Data mining ; Theoretical computer science ; Absorption spectroscopy ; Machine learning ; Physics ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Graph Neural Networks
Authors: Maria G. Khrenova | Alexandra V. Krivitskaya | Alexander V. Nemukhin
Source: ChemRxiv (preprint, 2022-08-24)
Field: Materials Science — Materials Chemistry

On the Fly Determination of the Substrate Activation in Hydrolases Using a Neural Network Active sites of enzymes are able to activate substrates and perform chemical reactions that cannot occur in solutions. We focus here on hydrolysis reactions catalyzed by enzymes and initiated by the nucleophilic attack of the carbonyl carbon atom of the substrate. From the electronic structure standpoint, substrate activation can be characterized in terms of the Laplacian of the electron density. This is a simple and easily visible imaging that allows one to “visualize” electrophilic site on the carbonyl carbon atom, that is presented only in the activated species. The efficiency of the substrate activation by the enzyme can be quantified from the ratio of reactive and nonreactive states from the QM/MM MD trajectories. We propose a neural network that assigns the Laplacian of electron density maps to the reactive and nonreactive species. The neural network is trained on the cysteine protease enzyme-substrate complexes and successfully validated on the zinc-containing hydrolase, thus showing a wide range of applications of the proposed approach. Electrophile ; Substrate (aquarium) ; Nucleophile ; Chemistry ; Active site ; Catalysis ; Hydrolysis ; Enzyme ; Combinatorial chemistry ; Hydrolase ; Laplace operator ; Photochemistry ; Organic chemistry ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Yejun Gu | Christopher D. Stiles | Jaafar A. El‐Awady
Source: arXiv (Cornell University) (preprint, 2022-09-11)
Field: Materials Science — Materials Chemistry

A Statistical Perspective for Predicting the Strength of Metals: Revisiting the Hall-Petch Relationship using Machine Learning The mechanical properties of a material are intimately related to its microstructure. This is particularly important for predicting mechanical behavior of polycrystalline metals, where microstructural variations dictate the expected material strength. Until now, the lack of microstructural variability in available datasets precluded the development of robust physics-based theoretical models that account for randomness of microstructures. To address this, we have developed a probabilistic machine learning framework to predict the flow stress as a function of variations in the microstructural features. In this framework, we first generated an extensive database of flow stress for a set of over a million randomly sampled microstructural features, and then applied a combination of mixture models and neural networks on the generated database to quantify the flow stress distribution and the relative importance of microstructural features. The results show excellent agreement with experiments and demonstrate that across a wide range of grain size, the conventional Hall-Petch relationship is statistically valid for correlating the strength to the average grain size and its comparative importance versus other microstructural features. This work demonstrates the power of the machine-learning based probabilistic approach for predicting polycrystalline strength, directly accounting for microstructural variations, resulting in a tool to guide the design of polycrystalline metallic materials with superior strength, and a method for overcoming sparse data limitations. Materials science ; Randomness ; Crystallite ; Microstructure ; Probabilistic logic ; Grain size ; Stress (linguistics) ; Context (archaeology) ; Flow stress ; Machine learning ; Artificial intelligence ; Computer science ; Metallurgy ; Statistics ; Mathematics ; Geology Machine Learning in Materials Science ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: An Su | Xin Zhang | Chengwei Zhang | Debo Ding | Yun‐Fang Yang | Keke Wang | Yuanbin She
Source: ChemRxiv (preprint, 2022-11-14)
Field: Materials Science — Materials Chemistry

Data-driven design of porphyrin photocatalysts using deep transfer learning from quantum chemical databases Metalloporphyrins and Porphyrins (MpPs) have received increasing attention as potential photocatalysts for hydrogen and oxygen evolution and the reduction of carbon dioxide (CO2). Recent studies have found that both HOMO/LUMO energy levels and energy gaps are important factors controlling the performance of MpPs photocatalysts. Since pure quantum chemistry methods consume a large amount of computational resources and cannot learn directly from previously accumulated data, in this study, we proposed a deep transfer learning approach to rapidly predict the HOMO/LUMO energy levels and energy gaps based on two quantum chemistry databases. To complement the open-source Porphyrin-based Dyes Database (PBDD), which contains porphyrin structures designed as photosensitizers, we curated a new database, the Metalloporphyrins and Porphyrins Database (MpPD), in which MpPs were specifically designed as potential photocatalysts. We also adapted two state-of-the-art chemical deep learning models to accommodate more complex systems of MpPs. The PorphyBERT model and PorphyDMPNN model, both pretrained with PBDD and fine-tuned with MpPD, performed satisfactorily in predicting HOMO/LUMO level and energy gap. We concluded the paper by recommending 14 MpPs as potential photocatalysts for CO2 reduction, whose structures have similar value in out-of-sample model prediction and DFT calculations. Porphyrin ; HOMO/LUMO ; Quantum ; Deep learning ; Artificial photosynthesis ; Database ; Photochemistry ; Transfer of learning ; Energy transfer ; Electron transfer ; Redox ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Catalysis ; Photocatalysis ; Chemical physics ; Molecule ; Artificial intelligence ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Madhumita Rano | Debashree Ghosh
Source: ChemRxiv (preprint, 2022-07-27)
Field: Materials Science — Materials Chemistry

Efficient machine learning configuration interaction for bond breaking problems Machine learning-assisted configuration interaction (MLCI) has been shown earlier as a promising method in determining the electronic structure of the model and molecular Hamiltonians. In the MLCI approach to molecular Hamiltonians, it has been noticed that prediction is strongly dependent on the connectedness of the training and validation spaces. In this work, we have tested three different models with different output parameters (abs-MLCI, transformed-MLCI, and log-MLCI) to verify the robustness of training these models. We define robustness as the extent of error in prediction even when the spaces (training and validation) are nected. We notice that the log-MLCI model is best suited to this approach and is, therefore, a powerful model for accurate one-shot variational energies. This is tested not confor chemical bond breaking in water, carbon monoxide, nitrogen, and dicarbon molecules. Robustness (evolution) ; Computer science ; Notice ; Training set ; Artificial intelligence ; Machine learning ; Algorithm ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Diyi Liu | Mitchell Luskin | S. H. Carr
Source: arXiv (Cornell University) (preprint, 2022-07-22)
Field: Materials Science — Materials Chemistry

Seeing moiré: convolutional network learning applied to twistronics Moiré patterns made of two-dimensional (2D) materials represent highly tunable electronic Hamiltonians, allowing a wide range of quantum phases to emerge in a single material. Current modeling techniques for moiré electrons requires significant technical work specific to each material, impeding large-scale searches for useful moiré materials. In order to address this difficulty, we have developed a material-agnostic machine learning approach and test it here on prototypical one-dimensional (1D) moiré tight-binding models. We utilize the stacking dependence of the local density of states (SD-LDOS) to convert information about electronic bandstructure into physically relevant images. We then train a neural network that successfully predicts moiré electronic structure from the easily computed SD-LDOS of aligned bilayers. This network can satisfactorily predict moiré electronic structures, even for materials that are not included in its training data. Moiré pattern ; Convolutional neural network ; Stacking ; Computer science ; Deep learning ; Materials science ; Electronic structure ; Artificial neural network ; Artificial intelligence ; Physics ; Condensed matter physics ; Computer vision Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Yipin Lv | Baotao Kang | Guozhu Chen | Yuan Yuan | Jianyu Ren | Jin Yong Lee
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Revisiting the Origin of Orr and Her Activities of N-Doped Γ-Graphdiyne from the Perspective of Edge Effects Perspective (graphical) ; Enhanced Data Rates for GSM Evolution ; Doping ; Materials science ; Engineering ; Computer science ; Telecommunications ; Artificial intelligence ; Optoelectronics Graphene research and applications ; Advanced Memory and Neural Computing ; Advanced Photocatalysis Techniques