jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tamás Temesi | Tibor Czigány
Source: Advanced Engineering Materials (article, 2020-04-29)
Field: Materials Science — Ceramics and Composites

Integrated Structures from Dissimilar Materials: The Future Belongs to Aluminum–Polymer Joints The spread of integrated structural elements and parts made from low‐density materials (for example aluminum and polymers) created a need for joining technologies with which these can be joined. Herein, the most important surface preparation methods and joining processes, with which the surface structure of aluminum can be modified and aluminum and polymer structures can be joined, are reviewed. For both topics, a new classification method is introduced: surface preparation methods are grouped based on the method of creating surface structures, whereas joining technologies are grouped according to heat input and structural changes in the polymer material. Herein, “hot” joining technologies (in which so much heat is formed that the polymer material is melted) are reviewed. This grouping category includes techniques based on friction and induction, ultrasonic and laser welding, and some in situ joining technologies. With these, materials with highly different chemical structures and melting temperatures are joined in fast cycles, in a reliable manner. In the coming years, more integrated structures containing aluminum–polymer joints manufactured with fast, automatable joining techniques (such as ultrasonic and laser welding, in compliance with the requirements of Industry 4.0) will be used throughout the industry. Materials science ; Welding ; Polymer ; Aluminium ; Ultrasonic welding ; Structural material ; Ultrasonic sensor ; Composite material ; Nanotechnology ; Metallurgy ; Mechanical engineering Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Synthesis and properties of polymers
Authors: Marcus M. Noack | Gregory S. Doerk | Ruipeng Li | Masafumi Fukuto | Kevin G. Yager
Source: Scientific Reports (article, 2020-01-28)
Field: Materials Science — Materials Chemistry

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection. Kriging ; Computer science ; Gaussian process ; Variogram ; Surrogate model ; Sensitivity (control systems) ; Gaussian ; Algorithm ; Function (biology) ; Mathematical optimization ; Machine learning ; Data mining ; Mathematics ; Physics Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Advanced Multi-Objective Optimization Algorithms
Authors: Kenton E. Hicks | Andrew Rosen | Zoha H. Syed | Randall Q. Snurr | Omar K. Farha | Justin M. Notestein
Source: ACS Catalysis (article, 2020-12-04)
Field: Chemistry — Inorganic Chemistry

Zr<sub>6</sub>O<sub>8</sub> Node-Catalyzed Butene Hydrogenation and Isomerization in the Metal–Organic Framework NU-1000 Zirconium-based metal–organic frameworks (Zr-MOFs) have been increasingly studied over the past two decades as heterogeneous catalysts due to their synthetic tunability, well-defined nature, and chemical stability. In contrast to traditional zirconia-based heterogeneous catalysts, the community has assumed that Zr-MOFs are inert catalyst supports that do not participate directly in hydrocarbon transformations, such as olefin hydrogenation and isomerization. Here, we report that the Zr-MOF NU-1000 is capable of catalyzing olefin hydrogenation and isomerization, without any postsynthetic modifications, under a hydrogen atmosphere. We probe H2 activation over the nodes of NU-1000 via spectroscopic and computational techniques revealing that H2 dissociation can occur heterolytically across coordinatively unsaturated Zr sites and proximal hydroxide and μ3-oxo ligands. These results, along with catalytic experiments, suggest that H2 activation results in node-supported zirconium hydrides capable of the hydrogenation and isomerization of 1-butene. When examining rate dependence on the partial pressure of H2, we observe first-order dependence for hydrogenation and half-order dependence for isomerization. Half-order H2 rate dependence is consistent with a mechanism where both fragments of cleaved H2 are active for 1-butene isomerization, suggesting that heterolytic cleavage generates acidic protons resulting in parallel, acid-, and hydride-catalyzed isomerization pathways. This work shows that Zr-MOFs have more diverse reactivity than the current literature may suggest and opens possibilities for ways in which Zr-MOFs can be used as heterogeneous catalysts and supports. Isomerization ; Catalysis ; Chemistry ; Hydride ; Olefin fiber ; Dissociation (chemistry) ; Zirconium ; Photochemistry ; Heterogeneous catalysis ; Metal-organic framework ; Heterolysis ; Hydrogen ; Inorganic chemistry ; Physical chemistry ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Naveen K. Dandu | Logan Ward | Rajeev S. Assary | Paul C. Redfern | Badri Narayanan | Ian Foster | Larry A. Curtiss
Source: The Journal of Physical Chemistry A (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms High-fidelity quantum-chemical calculations can provide accurate predictions of molecular energies, but their high computational costs limit their utility, especially for larger molecules. We have shown in previous work that machine learning models trained on high-level quantum-chemical calculations (G4MP2) for organic molecules with one to nine non-hydrogen atoms can provide accurate predictions for other molecules of comparable size at much lower costs. Here we demonstrate that such models can also be used to effectively predict energies of molecules larger than those in the training set. To implement this strategy, we first established a set of 191 molecules with 10-14 non-hydrogen atoms having reliable experimental enthalpies of formation. We then assessed the accuracy of computed G4MP2 enthalpies of formation for these 191 molecules. The error in the G4MP2 results was somewhat larger than that for smaller molecules, and the reason for this increase is discussed. Two density functional methods, B3LYP and ωB97X-D, were also used on this set of molecules, with ωB97X-D found to perform better than B3LYP at predicting energies. The G4MP2 energies for the 191 molecules were then predicted using these two functionals with two machine learning methods, the FCHL-Δ and SchNet-Δ models, with the learning done on calculated energies of the one to nine non-hydrogen atom molecules. The better-performing model, FCHL-Δ, gave atomization energies of the 191 organic molecules with 10-14 non-hydrogen atoms within 0.4 kcal/mol of their G4MP2 energies. Thus, this work demonstrates that quantum-chemically informed machine learning can be used to successfully predict the energies of large organic molecules whose size is beyond that in the training set. Molecule ; Hydrogen ; Quantum chemical ; Quantum ; Organic molecules ; Hydrogen molecule ; Hydrogen bond ; Chemical physics ; Materials science ; Computational chemistry ; Atomic physics ; Chemistry ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Mahmudul Islam | Md Shajedul Hoque Thakur | Satyajit Mojumder | Mohammad Nasim Hasan
Source: Computational Materials Science (article, 2020-12-04)
Field: Materials Science — Materials Chemistry

Extraction of material properties through multi-fidelity deep learning from molecular dynamics simulation Molecular dynamics ; Benchmark (surveying) ; Fidelity ; Computer science ; Range (aeronautics) ; High fidelity ; Sample (material) ; Statistical physics ; Algorithm ; Materials science ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Mengjie Liu | Alon Grinberg Dana | Matthew S. Johnson | Mark S. Goldman | Agnes Jocher | A. Mark Payne | Colin A. Grambow | Kehang Han | Nathan W. Yee | Emily Mazeau | Katrín Blöndal | Richard H. West | Franklin Goldsmith | William H. Green
Source: ChemRxiv (preprint, 2020-12-29)
Field: Materials Science — Materials Chemistry

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. One important change is that RMG v3.0 is now Python 3 compatible, which supports the most up-to-date versions of cheminformatics and machine learning packages that RMG depends on. Additionally, RMG can now generate heterogeneous catalysis models, in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release also includes parallelization for reaction generation and on-the-fly quantum calculations, and a new molecule isomorphism approach to improve computational performance. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems. Cheminformatics ; Python (programming language) ; Computer science ; Generator (circuit theory) ; Reaction mechanism ; On the fly ; Chemistry ; Computational science ; Biological system ; Computational chemistry ; Thermodynamics ; Catalysis ; Physics ; Programming language Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Binghong Chen | Chengtao Li | Hanjun Dai | Le Song
Source: arXiv (Cornell University) (article, 2020-06-29)
Field: Chemistry — Inorganic Chemistry

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product. The vast number of possible chemical transformations makes the size of the search space very big, and retrosynthetic planning is challenging even for experienced chemists. However, existing methods either require expensive return estimation by rollout with high variance, or optimize for search speed rather than the quality. In this paper, we propose Retro*, a neural-based A*-like algorithm that finds high-quality synthetic routes efficiently. It maintains the search as an AND-OR tree, and learns a neural search bias with off-policy data. Then guided by this neural network, it performs best-first search efficiently during new planning episodes. Experiments on benchmark USPTO datasets show that, our proposed method outperforms existing state-of-the-art with respect to both the success rate and solution quality, while being more efficient at the same time. Retrosynthetic analysis ; Benchmark (surveying) ; Computer science ; Artificial neural network ; Artificial intelligence ; Machine learning ; Quality (philosophy) ; Beam search ; Tree (set theory) ; Task (project management) ; Decision tree ; Search algorithm ; Data mining ; Algorithm ; Engineering ; Mathematics Asymmetric Hydrogenation and Catalysis ; Catalysis and Hydrodesulfurization Studies ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jinchao Feng | Joshua L. Lansford | Markos A. Katsoulakis | Dionisios G. Vlachos
Source: Science Advances (article, 2020-10-14)
Field: Materials Science — Materials Chemistry

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)-based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction. Computer science ; Probabilistic logic ; Graphical model ; Trustworthiness ; Artificial intelligence ; Machine learning ; Big data ; Data science ; Computational model ; Uncertainty quantification ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Hailong Hu
Source: Frontiers in Chemistry (review, 2020-03-20)
Field: Materials Science — Materials Chemistry

Recent Advances of Bioresponsive Nano-Sized Contrast Agents for Ultra-High-Field Magnetic Resonance Imaging The ultra-high-field magnetic resonance imaging (MRI) nowadays has been receiving enormous attention in both biomaterial research and clinical diagnosis. MRI contrast agents are generally comprising of T<sub>1</sub>-weighted and T<sub>2</sub>-weighted contrast agent types, where T<sub>1</sub>-weighted contrast agents show positive contrast enhancement with brighter images by decreasing the proton's longitudinal relaxation times and T<sub>2</sub>-weighted contrast agents show negative contrast enhancement with darker images by decreasing the proton's transverse relaxation times. To meet the incredible demand of MRI, ultra-high-field T<sub>2</sub> MRI is gradually attracting the attention of research and medical needs owing to its high resolution and high accuracy for detection. It is anticipated that high field MRI contrast agents can achieve high performance in MRI imaging, where parameters of chemical composition, molecular structure and size of varied contrast agents show contrasted influence in each specific diagnostic test. This review firstly presents the recent advances of nanoparticle contrast agents for MRI. Moreover, multimodal molecular imaging with MRI for better monitoring is discussed during biological process. To fasten the process of developing better contrast agents, deep learning of artificial intelligent (AI) can be well-integrated into optimizing the crucial parameters of nanoparticle contrast agents and achieving high resolution MRI prior to the clinical applications. Finally, prospects and challenges are summarized. Contrast (vision) ; Magnetic resonance imaging ; Computer science ; Materials science ; Nuclear magnetic resonance ; Artificial intelligence ; Radiology ; Medicine ; Physics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Nanoplatforms for cancer theranostics
Authors: B. Mrzygłód | Marek Hawryluk | Marta Janik | Izabela Olejarczyk-Wożeńska
Source: The International Journal of Advanced Manufacturing Technology (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel Abstract The article presents the results of a sensitivity analysis of artificial neural networks developed for a system which predicts the durability of forging tools used in the selected hot die forging process. The developed system makes it possible to calculate the geometric loss of the examined tool for the given values of its operating parameters (number of forgings, tool temperature at selected points, type of the applied protective layer, pressure and path of friction) and estimates the intensity of the occurrence of typical mechanisms of tool destruction, i.e. thermo-mechanical fatigue, mechanical wear, abrasive wear and plastic deformation. Nine neural networks operate in the developed system. Five of them determine the geometric loss of the material used for tools operating with protective layers, including a nitrided layer, a pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN. Four networks make calculations determining the intensity of the occurrence of typical destructive mechanisms. The developed sensitivity analysis allows for each neural network to show which input parameters are most important and have the greatest impact on the explained variables. This is determined based on the network error analysis in the case of elimination of individual variables from the input data. The greater the network error calculated after rejecting an input variable relative to the error obtained for the network with all the input variables, the more sensitive the network to the lack of this variable. The best compliance was obtained for the first developed set of networks regarding the geometric loss of material, while the lowest compliance was obtained for the second developed set of networks regarding the applied protective layers, and in particular for plastic deformation and mechanical fatigue, probably due to the smallest size of these sets in the knowledge base. The obtained results of this analysis are important for the system operation, i.e. supporting the technologist’s decision in the selection of such process parameter values that will increase the die’s lifetime. Forging ; Artificial neural network ; Sensitivity (control systems) ; Durability ; Abrasive ; Mechanical engineering ; Engineering ; Approximation error ; Structural engineering ; Process (computing) ; Control theory (sociology) ; Computer science ; Materials science ; Algorithm ; Artificial intelligence ; Composite material ; Electronic engineering Metal Alloys Wear and Properties ; Surface Treatment and Coatings ; Advanced machining processes and optimization
Authors: Alexander S. Gzyl | Anton O. Oliynyk | Arthur Mar
Source: Crystal Growth & Design (article, 2020-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation Heusler compounds form a diverse group of intermetallic materials encompassing many compositions and structures derived from cubic prototypes, and exhibiting complicated types of disorder phenomena. In particular, preparing solid solutions between half-Heusler ABC and full-Heusler compounds AB2C offers a means to control physical properties. However, as is typical in materials discovery, they represent only a small fraction of possible intermetallic compounds. To address this problem of unbalanced data sets, a machine-learning model was developed using an ensemble approach involving the synthetic minority oversampling technique to predict new compounds likely to adopt half-Heusler structures. The training set was based on experimental crystal structures, including those of nonstoichiometric compounds. The model achieved an accuracy of 98% on the validation set and gave excellent performance in terms of balanced statistical measures. A subset of compounds predicted to adopt half-Heusler structures having existing full-Heusler counterparts was then targeted for preparation. Six of seven of these candidates were successfully synthesized and confirmed to be half-Heusler compounds. Intermetallic ; Materials science ; Heusler compound ; Set (abstract data type) ; Oversampling ; Crystal structure ; Crystallography ; Computer science ; Chemistry ; Metallurgy ; Optoelectronics Heusler alloys: electronic and magnetic properties ; MXene and MAX Phase Materials ; Intermetallics and Advanced Alloy Properties
Authors: Jia-Jia Ye | Chuhong Lin | Xing‐Jiu Huang
Source: Journal of Electroanalytical Chemistry (article, 2020-02-10)
Field: Chemistry — Electrochemistry

Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak Analyte ; Chemistry ; Voltammetry ; Supporting electrolyte ; Electrochemistry ; Analytical Chemistry (journal) ; Electrolyte ; Anodic stripping voltammetry ; Metal ions in aqueous solution ; Selectivity ; Principal component analysis ; Electroanalytical method ; Reproducibility ; Metal ; Biological system ; Electrode ; Chromatography ; Artificial intelligence ; Computer science ; Potentiometric titration ; Physical chemistry Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: Tomáš Vrzal | Michaela Malečková | Jana Olšovská
Source: Analytica Chimica Acta (article, 2020-12-29)
Field: Chemistry — Spectroscopy

DeepReI: Deep learning-based gas chromatographic retention index predictor Kovats retention index ; Chemistry ; Residual ; Normalization (sociology) ; Pooling ; Analyte ; Gas chromatography ; Graphical user interface ; Chromatography ; Artificial intelligence ; Algorithm ; Computer science Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies