jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sujeeun, Lakshmi Y. | Goonoo, Nowsheen | Honita Ramphul | Chummun, Itisha | Gimié, Fanny | Baichoo, Shakuntala | Bhaw-Luximon, Archana
Source: Figshare (article, 2020-01-01)
Field: Materials Science — Biomaterials

Supplementary material from "Correlating <i>in vitro</i> performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms" The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell–material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental <i>in vitro</i> data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach. Fibre diameter, pore diameter, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with <i>in vivo</i> data. This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds. Tissue engineering ; Computer science ; Algorithm ; Biomedical engineering ; Machine learning ; Artificial intelligence ; Materials science ; Engineering Electrospun Nanofibers in Biomedical Applications
Authors: Maciej E. Marchwiany | Magdalena Birowska | Mariusz Popielski | Jacek A. Majewski | Agnieszka Jastrzębska
Source:  (preprint, 2020-02-14)
Field: Materials Science — Materials Chemistry

Predicting the cytotoxicity of MXenes - layered materials - using machine learning methods. <title>Abstract</title> Background: Prediction of the compound cytotoxicity is a crucial issue in the development of new drugs and potential biomedical applications. Experimental studies are time-consuming and expensive. Machine learning models can quickly predict the cytotoxicity of compounds, by extracting new insights from large materials and biological data sets, and provide further guidance for experimental studies. Results: Here, we identify the most relevant features that are responsible for the cytotoxic behavior of layered MXenes materials. The most important result of our work is the identification of 2D MXenes specific surface parameters as responsible for the potential cytotoxicity of these materials, in particular, the presence of transition metal oxides and Lithium atoms on the surface. After successful verification of the correct predictions of our model,we have also succeeded in predicting toxicity for 2D MXenes not tested in vitro. Hence, we have been able to complement the existing knowledge coming from in vitro studies. Conclusions: Our results allow for the future selection of synthesis methods preventing surface oxidation, which should allow production of non-toxic 2D MXenes. Such materials might find application in many fields of science and technology, especially in biotechnology and nanomedicine. MXenes ; Cytotoxicity ; Computer science ; Artificial intelligence ; Materials science ; Nanotechnology ; Chemistry MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Advanced biosensing and bioanalysis techniques
Authors: Leslie Ching Ow Tiong | Kim Jeongrae | Sang Soo Han | Dong-Hun Kim
Source: arXiv (Cornell University) (preprint, 2020-05-26)
Field: Materials Science — Materials Chemistry

Identification of Crystal Symmetry from Noisy Diffraction Patterns by A Shape Analysis and Deep Learning The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.2% space group classification accuracy, outperforming conventional benchmark models by 17-27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the novel MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The newly proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification. Artificial intelligence ; Symmetry (geometry) ; Convolutional neural network ; Orthorhombic crystal system ; Diffraction ; Crystal (programming language) ; Computer science ; Benchmark (surveying) ; Pattern recognition (psychology) ; Monoclinic crystal system ; Identification (biology) ; Algorithm ; Crystallography ; Physics ; Mathematics ; Crystal structure ; Optics ; Geometry ; Chemistry ; Biology ; Geography ; Cartography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Cultural Heritage Materials Analysis
Authors: Sogol Lotfi | Ziyan Zhang | Gayatri Viswanathan | Kaitlyn Fortenberry | Aria Mansouri Tehrani | Jakoah Brgoch
Source: ChemRxiv (preprint, 2020-02-05)
Field: Materials Science — Materials Chemistry

Targeting Productive Composition Space Through Machine-Learning Directed Inorganic Synthesis This work presents an approach to aid the discovery of novel inorganic solids by highlighting regions of underexplored, yet likely productive composition space using machine learning. A support vector regression (SVR) algorithm was constructed first to determine a compound’s formation energy (∆𝐸𝑓,SVR) based solely on chemical composition using data from 313,965 high-throughput density functional theory calculations. The resulting predicted formation energies (r&lt;sup&gt;2&lt;/sup&gt; = 0.94; MAE = 85 meV/atom) were then used to construct zero-kelvin convex hull diagrams and identify compositions immediately on the hull, as well as +50 meV above the convex hull to capture potential compounds that are considered energetically unfavorable but that are still experimentally accessible. Using this methodology, four ternary composition diagrams, Y−Ag−&lt;i&gt;Tr&lt;/i&gt; (&lt;i&gt;Tr&lt;/i&gt; = B, Al, Ga, In), were explored owing to the diversity of chemistries as a function of triel element to provide experimental validation for the predictions. A particularly promising but unexplored region in the Y−Ag−In diagram was identified, and the ensuing solid-state high-temperature synthesis produced YAg&lt;sub&gt;0.65&lt;/sub&gt;In&lt;sub&gt;1.35&lt;/sub&gt;, which has not been reported. First-principle calculations were finally used to determine the ordering of Ag and In as well as confirm the crystal structure solution. Our combination of machine learning, inorganic synthesis, and computational modeling describes a new avenue where data-centric models and computation play a critical role in supporting the experimental examination of unexplored phase diagrams. Convex hull ; Ternary operation ; Chemical space ; Chemistry ; Composition (language) ; Algorithm ; Crystallography ; Regular polygon ; Mathematics ; Machine learning ; Computer science ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Sheng Gui | Zhaodi Chen | Minxin Chen | Benzhuo Lu
Source: arXiv (Cornell University) (preprint, 2020-05-09)
Field: Materials Science — Materials Chemistry

Molecular Sparse Representation by 3D Ellipsoid Radial Basis Function Neural Networks via $L_1$ Regularization In this paper, we have developed an ellipsoid radial basis function neural network (ERBFNN) and algorithm for sparse representing of a molecular shape. To evaluate a sparse representation of the molecular shape model, the Gaussian density map of molecule is approximated by ERBFNN with a relatively small number of neurons. The deep learning models were trained by optimizing a nonlinear loss function with $L_1$ regularization. Experimental results demonstrate that the original molecular shape is able to be represented with good accuracy by much fewer scale of ERBFNN by our algorithm. And our network in principle can be applied to multi-resolution sparse representation of molecular shape and coarse-grained molecular modeling. Ellipsoid ; Regularization (linguistics) ; Radial basis function ; Sparse approximation ; Gaussian ; Artificial neural network ; Representation (politics) ; Algorithm ; Basis (linear algebra) ; Computer science ; Basis function ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Physics ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Medical Imaging Techniques and Applications
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-03-11)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design This is the data and relevant analysis code for the article, <em>Combining automated microfluidic experimentation with machine learning for efficient polymerization design.</em> Microfluidics ; Polymerization ; Computer science ; Artificial intelligence ; Process engineering ; Nanotechnology ; Engineering ; Materials science ; Polymer Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-03-11)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design This is the data and relevant analysis code for the article, <em>Combining automated microfluidic experimentation with machine learning for efficient polymerization design.</em> Microfluidics ; Computer science ; Polymerization ; Nanotechnology ; Materials science ; Polymer Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Heejung Chung | Rodrigo Ribeiro Morcourt de Freitas | Gowoon Cheon | Evan J. Reed
Source: arXiv (Cornell University) (preprint, 2020-10-09)
Field: Materials Science — Materials Chemistry

A data-centric framework for crystal structure identification in atomistic simulations using machine learning Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. Here we propose an alternative framework for a fundamental structural characterization task: classifying atoms according to the crystal structure to which they belong. Our approach is data-centric and favors the employment of Machine Learning over heuristic rules of classification. A group of data-science tools and simple local descriptors of atomic structure are employed together with an efficient synthetic training set. We also introduce the first standard and publicly available benchmark data set for evaluation of algorithms for crystal-structure classification. It is demonstrated that our data-centric framework outperforms all of the most popular heuristic methods -- especially at high temperatures when lattices are the most distorted -- while introducing a systematic route for generalization to new crystal structures. Moreover, through the use of outlier detection algorithms our approach is capable of discerning between amorphous atomic motifs (i.e., noncrystalline phases) and unknown crystal structures, making it uniquely suited for exploratory materials synthesis simulations. Computer science ; Artificial intelligence ; Machine learning ; Heuristic ; Test set ; Intuition ; Characterization (materials science) ; Crystal structure prediction ; Benchmark (surveying) ; Algorithm ; Data mining ; Crystal structure ; Materials science ; Nanotechnology ; Chemistry ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Ion-surface interactions and analysis
Authors: Ella Gale
Source: ePrints Soton (University of Southampton) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Dimensionality in chemistry: using multidimensional data for machine learning In the last hundred years mankind has fully absorbed the idea of multi-dimensional space, starting with 4D space time. Due to the increase in computational power, scientists can now manipulate molecules in 4D (3D vibrating molecules in VR) and work with multidimensional datasets, which are needed to utilize big data and machine learning. However, our intuition from 3D space can fall down when dealing with higher dimensions and a lack of intuition can lead to mistakes in analysis. In this talk I will discuss how to think about the best dimensional space to use to describe chemical problems, how multi-dimensional space is different, techniques for using it and analysing the outputs of machine learning. Intuition ; Curse of dimensionality ; Data space ; Chemical space ; Computer science ; Space (punctuation) ; Artificial intelligence ; Machine learning ; Data science ; Big data ; Data mining ; Cognitive science ; Chemistry ; Psychology Machine Learning in Materials Science ; Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: David Vangulick
Source: Open Repository and Bibliography (University of Liège) (article, 2020-11-19)
Field: Materials Science — Materials Chemistry

From the DNO to the DSO: will artificial intelligence, machine learning and deep learning be the catalysts for this transformation?” Artificial intelligence ; Transformation (genetics) ; Computer science ; Deep learning ; Chemistry Machine Learning in Materials Science ; Artificial Intelligence Applications ; Artificial Intelligence in Healthcare and Education
Authors: 亮太 清水 | Ryota Shimizu | 成 小林 | Shigeru Kobayashi | 佑紀 渡邊 | Yuki WATANABE | 太郎 一杉 | Taro Hitosugi
Source: Institutional Repositories DataBase (IRDB) (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Autonomous materials synthesis by machine learning and robotics Robotics ; Robot ; Automation ; Key (lock) ; Active learning (machine learning) Machine Learning in Materials Science ; Material Selection and Properties ; Nanoporous metals and alloys
Authors: 康伸 安藤 | Yasunobu Ando
Source: Institutional Repositories DataBase (IRDB) (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Autonomous materials synthesis by machine learning and robotics Robotics ; Robot ; Automation ; Key (lock) ; Active learning (machine learning) Machine Learning in Materials Science ; Material Selection and Properties ; Nanoporous metals and alloys
Authors: Jin Soo Lim | Jonathan Vandermause | M. A. Van Spronsen | Albert Musaelian | Yu Xie | Lixin Sun | C. R. O'Connor | Tobias Egle | Nicola Molinari | Jacob Florian | Kaining Duanmu | R. J. Madix | Philippe Sautet | C. M. Friend | Boris Kozinsky
Source: ChemRxiv (preprint, 2020-06-16)
Field: Materials Science — Materials Chemistry

Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics Restructuring of interfaces plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. By developing and performing accelerated machine-learning molecular dynamics followed by an automated analysis method, we discover and characterize previously unidentified surface restructuring mechanisms in an unbiased fashion, including Pd-Ag place exchange and Ag pop-out, as well as step ascent and descent. Remarkably, layer-by-layer dissolution of Pd into Ag is always preceded by an encapsulation of Pd islands by Ag, resulting in a significant migration of Ag out of the surface and a formation of extensive vacancy pits within a period of microseconds. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. Our approach is broadly applicable to complex multimetallic systems and enables the previously intractable mechanistic investigation of restructuring dynamics at atomic resolution. Bimetallic strip ; Metastability ; Restructuring ; Molecular dynamics ; Dissolution ; Materials science ; Chemical physics ; Catalysis ; Atomic force microscopy ; Nanotechnology ; Chemical engineering ; Chemistry ; Crystallography ; Physical chemistry ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Sonali Joshi
Source: Journal of International Crisis and Risk Communication Research (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Predicting Structures of 2D Materials Enabled by Machine Learning Prediction of stable structures of two-dimensional (2D) materials that have not been experimentally realized is an important pre-requisite step for the development of these materials for various technological uses without the extensive trial-error experiments. Traditional methods such as density functional theory (DFT) can be used to find the energies of structures computationally, however, calculating the energies of the total number of structures possible would be daunting timewise as well. We propose using machine learning methods to reduce the search time for 2D materials’ geometric structures. Our case study for this process consists of hexagonal graphene-like boron–carbon–nitrogen (h-BCN). Our dataset consists of 300 randomly generated h-BCN structures optimized using DFT based calculations. A pattern recognition scheme based on nearest neighbors was developed to characterize the atomic environment of the atoms. We will be exploring the use of k-means clustering models to accurately predict the total energy of an h-BCN structure in order to find the lowest energy geometric structure. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science
Authors: Ikeda
Source: The Chemical Society of Japan (article, 2020-02-21)
Field: Materials Science — Materials Chemistry

Organic Semiconductor Properties of Tetrathienonaphthalenes 1 -Molecular Design using Machine Learning and ASSiSt Method and Synthesis- Organic semiconductor ; Computer science ; Artificial intelligence ; Process engineering ; Materials science ; Engineering ; Optoelectronics Machine Learning in Materials Science ; Conducting polymers and applications
Authors: Hexin Bai | Peng Chu | Jeng-Yuan Tsai | Nathan M. Wilson | Xiaofeng Qian | Qimin Yan | Haibin Ling
Source: arXiv (Cornell University) (preprint, 2020-05-27)
Field: Materials Science — Materials Chemistry

Graph Neural Network for Hamiltonian-Based Material Property Prediction Development of next-generation electronic devices for applications call for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties. Traditional electronic structure methods require expensive computation time and memory consumption, thus a fast and accurate prediction model is desired with increasing importance. Representing the interactions among atomic orbitals in any material, a material Hamiltonian provides all the essential elements that control the structure-property correlations in inorganic compounds. Effective learning of material Hamiltonian by developing machine learning methodologies therefore offers a transformative approach to accelerate the discovery and design of quantum materials. With this motivation, we present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The information of each orbital includes the name, relative coordinates with respect to the center of super cell and the atom number, while the interaction between orbitals are represented by the Hamiltonian matrix. The results show that our model can get a promising prediction accuracy with cross-validation. Hamiltonian (control theory) ; Computer science ; Atomic orbital ; Artificial neural network ; Computation ; Graph ; Topology (electrical circuits) ; Theoretical computer science ; Physics ; Artificial intelligence ; Algorithm ; Quantum mechanics ; Mathematics ; Electron Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Alex Casey | Brian C. Barnes | Ilias Bilionis | Steven F. Son
Source: Bulletin of the American Physical Society (article, 2020-03-05)
Field: Materials Science — Materials Chemistry

Deep Learning for Energetic Materials: Predicting Material Properties from Electronic Structure using Convolutional Neural Networks Convolutional neural network ; Deep learning ; Materials science ; Artificial neural network ; Computer science ; Artificial intelligence Machine Learning in Materials Science