jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiangyan Sun | Ke Liu | Yuquan Lin | Lingjie Wu | Haoming Xing | Minghong Gao | Ji Liu | Suocheng Tan | Zekun Ni | Qi Han | Junqiu Wu | Jie Fan
Source: arXiv (Cornell University) (preprint, 2021-08-09)
Field: Materials Science — Materials Chemistry

ChemiRise: a data-driven retrosynthesis engine We have developed an end-to-end, retrosynthesis system, named ChemiRise, that can propose complete retrosynthesis routes for organic compounds rapidly and reliably. The system was trained on a processed patent database of over 3 million organic reactions. Experimental reactions were atom-mapped, clustered, and extracted into reaction templates. We then trained a graph convolutional neural network-based one-step reaction proposer using template embeddings and developed a guiding algorithm on the directed acyclic graph (DAG) of chemical compounds to find the best candidate to explore. The atom-mapping algorithm and the one-step reaction proposer were benchmarked against previous studies and showed better results. The final product was demonstrated by retrosynthesis routes reviewed and rated by human experts, showing satisfying functionality and a potential productivity boost in real-life use cases. Retrosynthetic analysis ; Graph ; Computer science ; Template ; Artificial intelligence ; Convolutional neural network ; Data mining ; Theoretical computer science ; Chemistry ; Programming language ; Stereochemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Afrid Mazhar Shirsekar
Source: TigerPrints (Clemson University) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Demystifying Structure-Property Correlations for Perovskite Oxides Using Machine Learning to Discover Lead-Free Alternatives Solar Cells Discovering optimal materials for a given application has become extremely difficult due to the vast scope of structure possibilities and requires substantial computational expense to evaluate even one of all possible structures. An increase in the structural complexity can offset combinatorial explosion and could take months, if not years, even with the advanced computational architecture, to screen every candidate.Here in this study, we propose a computational approach based on statistical learning combined with DFT based computations that can effectively screen over the entire range of possible candidates and predict the material’s electronic properties with high fidelity. Specifically, we use advanced machine learning algorithms such as gradient boosted decision trees and electronic structure bandgap calculation data to screen perovskite structures as alternatives for lead-free solar cells. Perovskites are compounds with chemical formula ABX3 where A and B are cations and X is an anion that bonds to both cations. The perovskite class offers compositional flexibility which allows us to tune the structure to obtain better solar absorption efficiency. Using machine learning, we could establish a structure-property correlation, by mapping the attributes of the structures to their bandgaps, which enabled us to screen over all compounds within the perovskite class thus drastically accelerating our search of the optimal lead-free alternatives for solar cells. With purpose of making the dataset more robust, this study explored the complexity of the composition effect by evaluating the substitution of one or more elements in different proportions and arrangements over the possible sites available in the base perovskite structure on the material's bandgap. Lead (geology) ; Property (philosophy) ; Perovskite (structure) ; Materials science ; Engineering ; Philosophy ; Geology ; Chemical engineering ; Epistemology Machine Learning in Materials Science
Authors: Mohamed Mokhtar | Loai Nasrat | Mahmoud A. Attia
Source: International Journal of Emerging Electric Power Systems (article, 2021-06-12)
Field: Materials Science — Materials Chemistry

Mathematical modeling of polymer dielectric strength considering filling concentration Abstract Enhancement of Dielectric strength is an important issue to enhance power system reliability. Insulation filling such as Alumina Trihydrate (ATH) and Silicon dioxide (Sio 2 ) are used to enhance the dielectric strength of polymer insulator. The paper proposes a mathematical model of dielectric strength considering different temperatures and filling percentages using neural network. Moreover, comparison of filling materials is carried out through this model. The comparison shows the superiority of ATH in enhancing the dielectric strength of the polymer. Also, the obtained model will help in getting the best filling concentration without needing to repeat the tests which can be considered as a techno-economical solution to enhance the dielectric strength. Finally, the curve fitting is used to get the dielectric strength model not only as a function of filling concentration percentage but also as a function of temperature. Dielectric ; Materials science ; Dielectric strength ; Composite material ; Reliability (semiconductor) ; Polymer ; Insulator (electricity) ; Doping ; Power (physics) ; Thermodynamics ; Optoelectronics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Debanjali Chatterjee | Bairav S. Vishnugopi | Partha P. Mukherjee
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Surfaces, Coatings and Films

Machine-Learning Based Transport Property Analytics in Porous Electrodes The electrochemical performance of lithium-ion batteries strongly relies upon the composition and microstructure of the porous electrodes. An electrochemically-coupled set of transport processes underlie the composite electrodes depending on the microstructural arrangement of its constituent material phases. However, the intrinsically stochastic, anisotropic and heterogeneous nature of these porous electrodes makes the characterization of their effective transport properties quite complex. In this work, we examine the use of machine learning as a viable route for fast and accurate abstraction of effective electrode properties, e.g. the tortuosity and effective electronic conductivity of exemplar electrode microstructures based on three-dimensional X-ray tomography images. The proposed computational framework can be generally extended to characterize various modes of transport resistances in porous media. Tortuosity ; Electrode ; Materials science ; Porosity ; Microstructure ; Characterization (materials science) ; Porous medium ; Anisotropy ; Nanotechnology ; Computer science ; Composite material ; Chemistry ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Panagiota Birmpa | Jinchao Feng | Markos A. Katsoulakis | Luc Rey-Bellet
Source: arXiv (Cornell University) (preprint, 2021-07-17)
Field: Materials Science — Materials Chemistry

Model Uncertainty and Correctability for Directed Graphical Models Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence. They allow us to integrate in a natural way expert knowledge, physical modeling, heterogeneous and correlated data and quantities of interest. For exactly this reason, multiple sources of model uncertainty are inherent within the modular structure of the graphical model. In this paper we develop information-theoretic, robust uncertainty quantification methods and non-parametric stress tests for directed graphical models to assess the effect and the propagation through the graph of multi-sourced model uncertainties to quantities of interest. These methods allow us to rank the different sources of uncertainty and correct the graphical model by targeting its most impactful components with respect to the quantities of interest. Thus, from a machine learning perspective, we provide a mathematically rigorous approach to correctability that guarantees a systematic selection for improvement of components of a graphical model while controlling potential new errors created in the process in other parts of the model. We demonstrate our methods in two physico-chemical examples, namely quantum scale-informed chemical kinetics and materials screening to improve the efficiency of fuel cells. Graphical model ; Computer science ; Probabilistic logic ; Machine learning ; Artificial intelligence ; Modular design ; Parametric statistics ; Graph ; Model selection ; Data mining ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Yaroslav Balytskyi | Justin Bendesky | Tristan C. Paul | Guy M. Hagen | Kelly McNear
Source: arXiv (Cornell University) (preprint, 2021-11-11)
Field: Chemistry — Analytical Chemistry

Raman spectroscopy in open world learning settings using the Objectosphere approach Raman spectroscopy in combination with machine learning has significant promise for applications in clinical settings as a rapid, sensitive, and label-free identification method. These approaches perform well in classifying data that contains classes that occur during the training phase. However, in practice, there are always substances whose spectra have not yet been taken or are not yet known and when the input data are far from the training set and include new classes that were not seen at the training stage, a significant number of false positives are recorded which limits the clinical relevance of these algorithms. Here we show that these obstacles can be overcome by implementing recently introduced Entropic Open Set and Objectosphere loss functions. To demonstrate the efficiency of this approach, we compiled a database of Raman spectra of 40 chemical classes separating them into 20 biologically relevant classes comprised of amino acids, 10 irrelevant classes comprised of bio-related chemicals, and 10 classes that the Neural Network has not seen before, comprised of a variety of other chemicals. We show that this approach enables the network to effectively identify the unknown classes while preserving high accuracy on the known ones, dramatically reducing the number of false positives while preserving high accuracy on the known classes, which will allow this technique to bridge the gap between laboratory experiments and clinical applications. False positive paradox ; Artificial intelligence ; Computer science ; Machine learning ; Identification (biology) ; Set (abstract data type) ; Artificial neural network ; Raman spectroscopy ; Data set ; Training set ; Nat ; Relevance (law) ; Pattern recognition (psychology) ; Data mining ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Pankaj Kumar | Vinit Sharma | Sharmila N. Shirodkar | Pratibha Dev
Source: arXiv (Cornell University) (preprint, 2021-03-12)
Field: Materials Science — Materials Chemistry

Predicting phase preferences of two-dimensional transition metal dichalcogenides using machine learning Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of several possible structures, with the most common being the trigonal prismatic and octahedral symmetry phases. Since the structure determines the electronic properties, being able to predict phase-preferences of TMDs from just the knowledge of the constituent atoms is highly desired, but has remained a long-standing problem. In this study, we applied high-throughput quantum mechanical computations with machine learning algorithms to solve this old problem. Our analysis provides insights into determining physiochemical factors that dictate the phase-preference of a TMD, identifying and going beyond the attributes considered by earlier researchers in predicting crystal structures. A knowledge of these underlying physiochemical factors not only helps us to rationalize, but also to accurately predict structural preferences. We show that machine learning algorithms are powerful tools that can be used not only to find new materials with targeted properties, but also to find connections between elemental attributes and the target property/properties that were not previously obvious. Computer science ; Phase transition ; Transition metal ; Computation ; Phase (matter) ; Preference ; Property (philosophy) ; Symmetry (geometry) ; Artificial intelligence ; Octahedron ; Crystal structure ; Crystal structure prediction ; Machine learning ; Materials science ; Algorithm ; Chemistry ; Crystallography ; Mathematics ; Physics ; Condensed matter physics ; Geometry ; Catalysis Machine Learning in Materials Science ; 2D Materials and Applications ; Catalysis and Oxidation Reactions
Authors: Hassna El‐Bousiydy | Teo Lombardo | Emiliano N. Primo | Marc Duquesnoy | Mathieu Morcrette | Patrik Johansson | Patrice Simon | Alexis Grimaud | Alejandro A. Franco
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Materials Chemistry

Digging in Documents: Using Text Mining to Reveal Hidden Knowledge in the Lithium-Ion Battery Literature Scientific publications constitute the main information source of battery-related data for academic research. There is an impressive number of papers published on Lithium Ion Batteries (LIBs) for instance. Such impressive amount of data in principle could be used by machine learning (ML) methods in order to optimize efforts and even make possible the discovery of new optimal experimental conditions. Yet, the majority is published as unstructured data, making ML analysis difficult. Here, in order to unravel researchers’ habits when reporting their results, we present a text mining study using primarily publications containing original experimental data from the LIB field. We analyze the reported data structure and we discuss the remaining challenges for applying automated knowledge discovery techniques. We then present a Named Entity Recognition approach to automatically extract quantified parameters from the battery literature and how to build an in house generated database containing a wide diversity of materials, experimental parameters and conditions. Our in house approach achieves a data extraction F-scores ≥ 90% and can thereby pave the way towards an automatized way of generating large databases for use in battery ML models. References El-Bouysidy, H., Lombardo, T., Primo, E. N., Duquesnoy, M., Morcrette, M., Johansson, P., Simon, P., Grimaud, A., &amp; Franco, A. A. submitted (2020). Torayev, Amangeldi, Pieter CMM Magusin, Clare P. Grey, Céline Merlet, and Alejandro A. Franco. Journal of Physics: Materials 2, no. 4 (2019): 044004. Computer science ; Battery (electricity) ; Field (mathematics) ; Knowledge extraction ; Information retrieval ; Lithium (medication) ; Data science ; Data mining ; Artificial intelligence ; Physics ; Psychology ; Power (physics) Machine Learning in Materials Science ; Data Quality and Management ; Web Data Mining and Analysis
Authors: Yu Kitamura | Emi Terado | Zechen Zhang | Hirofumi Yoshikawa | Tomoko Inose | Hiroshi Uji‐i | Masaharu Tanimizu | Akihiro Inokuchi | Yoshinobu Kamakura | Daisuke Tanaka
Source: ChemRxiv (preprint, 2021-03-09)
Field: Chemistry — Inorganic Chemistry

Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10−4 S cm−1 for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%). Lanthanide ; Materials science ; Adsorption ; Metal-organic framework ; Reproducibility ; Metal ; Relative humidity ; Chemical engineering ; Chromatography ; Thermodynamics ; Chemistry ; Organic chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Yu Kitamura | Emi Terado | Zechen Zhang | Hirofumi Yoshikawa | Tomoko Inose | Hiroshi Uji‐i | Masaharu Tanimizu | Akihiro Inokuchi | Yoshinobu Kamakura | Daisuke Tanaka
Source: ChemRxiv (preprint, 2021-04-07)
Field: Chemistry — Inorganic Chemistry

Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10−4 S cm−1 for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%). Lanthanide ; Materials science ; Adsorption ; Metal-organic framework ; Reproducibility ; Relative humidity ; Chromatography ; Thermodynamics ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Andrew E. Sifain | Levi Lystrom | Richard A. Messerly | Justin S. Smith | Benjamin Nebgen | Kipton Barros | Sergei Tretiak | Nicholas Lubbers | Brendan J. Gifford
Source: ChemRxiv (preprint, 2021-02-24)
Field: Materials Science — Materials Chemistry

Predicting Phosphorescence Energies and Inferring Wavefunction Localization with Machine Learning Long-time excited state dynamics of triplet states and subsequent emission via phosphorescence are commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained using ab initio datasets may expedite the discovery of phosphorescent compounds. However, we show that standard ML approaches for modeling potential energy surfaces that succeed on small molecules do not generalize to molecules of larger sizes, due to the failure to account for spatial localities in spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the transition energy. Trained on phosphorescent transition energies of organic molecules, the model achieves prediction accuracies of ~4 kcal/mol on the held-out test set and ~13 kcal/mol on an out-of-sample test set of large phosphorescent molecules. These localization weights have a strong relationship with the ab initio spin density of the triplet to singlet state transition, and thus infer localities of the molecule that determine the spin transition, despite that no direct electronic information was provided during training. Phosphorescence ; Ab initio ; Singlet state ; Excited state ; Molecule ; Wave function ; Spin (aerodynamics) ; Artificial neural network ; Physics ; Chemistry ; Materials science ; Atomic physics ; Computer science ; Machine learning ; Quantum mechanics ; Thermodynamics ; Fluorescence Machine Learning in Materials Science ; Software System Performance and Reliability
Authors: Drew Vecchio | Samuel H. Mahler | Mark D. Hammig | Nicholas A. Kotov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-04-09)
Field: Materials Science — Biomaterials

Automatic structural analysis of bioinspired percolating network materials using graph theory ABSTRACT Mimicking numerous biological membranes and nanofiber-based tissues, there are multiple materials that are structured as percolating nanoscale networks (PPNs). They reveal unique combination of properties and the family of PNN-based composites and nanoporous materials is rapidly expanding. Their technological significance and the necessity of their structural design require a unifying approach for their structural description. However, their complex aperiodic architectures are difficult to describe using traditional methods that are tailored for crystals. A related problem is the lack of computational tools that enable one to capture and enumerate the patterns of stochastically branching fibrils that are typical for these composites. Here, we describe a conceptual methodology and a computational package, StructuralGT, to automatically produce a graph theoretical (GT) description of PNNs from various micrographs. Using nanoscale networks formed by aramid nanofibers (ANFs) as examples, we demonstrate structural analysis of PNNs with 13 GT parameters. Unlike qualitative assessments of physical features employed previously, StructuralGT allows quantitative description of the complex structural attributes of PNNs enumerating the network’s morphology, connectivity, and transfer patterns. Accurate conversion and analysis of micrographs is possible for various levels of noise, contrast, focus, and magnification while a dedicated graphical user interface provides accessibility and clarity. The GT parameters are expected to be correlated to material properties of PNNs (e.g. ion transport, conductivity, stiffness) and utilized by machine learning tools for effectual materials design. Table of Content Computer science ; Nanotechnology ; Nanofiber ; Materials science ; Merge (version control) ; Graph ; Biological system ; Artificial intelligence ; Theoretical computer science Supramolecular Self-Assembly in Materials ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Alexander Butz | Ebrahim Norouzi | Lukas Morand | Matthias David Weber | D. Helm | B. Schlay | Felix Bauer | K. Pfeffer | Ulrich E. Klotz | Miriam Eisenbart
Source: Fraunhofer-Publica (Fraunhofer-Gesellschaft) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Optimized measurement of the relaxation behavior of copper and copper alloys by means of cantilever trials and machine learning-supported parameter identification as basis for the simulation of the relaxation behavior of copper parts Optimierte Messung des Relaxationsverhaltens von Kupfer und Kupferlegierungen mittels Canti lever-Versuchen und Machine Learning gestützter Parameteridentifikation als Basis für die Simulation des Relaxationsverhalten von Kupferbauteilen 378 Copper ; Relaxation (psychology) ; Basis (linear algebra) ; Identification (biology) ; Cantilever Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Microstructure and mechanical properties