jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shubham Pandey | Jiaxing Qu | Vladan Stevanović | Peter C. St. John | Prashun Gorai
Source: Patterns (article, 2021-09-30)
Field: Materials Science — Materials Chemistry

Predicting energy and stability of known and hypothetical crystals using graph neural network The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerated screening. Here, we demonstrate the importance of a balanced training dataset of GS and higher-energy structures to accurately predict total energies using a generic graph neural network architecture. Using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> </mml:math> 16,500 density functional theory calculations from the National Renewable Energy Laboratory (NREL) Materials Database and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> </mml:math> 11,000 calculations for hypothetical structures as our training database, we demonstrate that our model satisfactorily ranks the structures in the correct order of total energies for a given composition. Furthermore, we present a thorough error analysis to explain failure modes of the model, including both prediction outliers and occasional inconsistencies in the training data. By examining intermediate layers of the model, we analyze how the model represents learned structures and properties. Outlier ; Artificial neural network ; Computer science ; Stability (learning theory) ; Graph ; Graph theory ; Renewable energy ; Energy (signal processing) ; Data mining ; Training set ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematics ; Engineering ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Nicholas R. Singstock | Jessica C. Ortiz-Rodríguez | Joseph T. Perryman | Christopher Sutton | Jesús M. Velázquez | Charles B. Musgrave
Source: Journal of the American Chemical Society (article, 2021-06-10)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Synthesis of Multinary Chevrel Phase Chalcogenides The Chevrel phase (CP) is a class of molybdenum chalcogenides that exhibit compelling properties for next-generation battery materials, electrocatalysts, and other energy applications. Despite their promise, CPs are underexplored, with only ∼100 compounds synthesized to date due to the challenge of identifying synthesizable phases. We present an interpretable machine-learned descriptor (<i>H</i><sub>δ</sub>) that rapidly and accurately estimates decomposition enthalpy (Δ<i>H</i><sub>d</sub>) to assess CP stability. To develop <i>H</i><sub>δ</sub>, we first used density functional theory to compute Δ<i>H</i><sub>d</sub> for 438 CP compositions. We then generated >560 000 descriptors with the new machine learning method SIFT, which provides an easy-to-use approach for developing accurate and interpretable chemical models. From a set of >200 000 compositions, we identified 48 501 CPs that <i>H</i><sub>δ</sub> predicts are synthesizable based on the criterion that Δ<i>H</i><sub>d</sub> < 65 meV/atom, which was obtained as a statistical boundary from 67 experimentally synthesized CPs. The set of candidate CPs includes 2307 CP tellurides, an underexplored CP subset with a predicted preference for channel site occupation by cation intercalants that is rare among CPs. We successfully synthesized five of five novel CP tellurides attempted from this set and confirmed their preference for channel site occupation. Our joint computational and experimental approach for developing and validating screening tools that enable the rapid identification of synthesizable materials within a sparse class is likely transferable to other materials families to accelerate their discovery. Chemistry ; Class (philosophy) ; Density functional theory ; Set (abstract data type) ; Atom (system on chip) ; Stability (learning theory) ; Phase (matter) ; Preference ; Algorithm ; Artificial intelligence ; Machine learning ; Computational chemistry ; Computer science ; Parallel computing Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Inorganic Chemistry and Materials
Authors: Yi Dong | Yu Zhang | Mingchu Ran | Xiao Zhang | Shaojun Liu | Yang Yang | Wenshuo Hu | Chenghang Zheng | Xiang Gao
Source: Journal of Materials Chemistry A (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Accelerated identification of high-performance catalysts for low-temperature NH<sub>3</sub>-SCR by machine learning A machine learning approach for SCR catalyst discovery is presented to realize the rapid screening of optimal catalysts. Catalysis ; Identification (biology) ; Computer science ; Process engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science
Authors: Shu Jiang | Zhuosheng Zhang | Hai Zhao | Jiangtong Li | Yang Yang | Bao‐Liang Lu | Ning Xia
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

When SMILES Smiles, Practicality Judgment and Yield Prediction of Chemical Reaction via Deep Chemical Language Processing Simplified Molecular Input Line Entry System (SMILES) provides a text-based encoding method to describe the structure of chemical species and formulize general chemical reactions. Considering that chemical reactions have been represented in a language form, we present a symbol only model to generally predict the yield of organic synthesis reaction without considering complex quantum physical modeling or chemistry knowledge. Our model is the first deep neural network application that treats chemical reaction text segments as embedding representation to the most recent deep natural language processing. Experimental results show our model can effectively predict chemical reactions, which achieves a high accuracy of 99.76% on practicality judgment and the Root Mean Square Error (RMSE) is around 0.2 for yield prediction. Our work shows the great potential for automatic yield prediction for organic reactions under general conditions and further applications in synthesis path prediction with the least modeling cost. Computer science ; Yield (engineering) ; Embedding ; Representation (politics) ; Artificial intelligence ; Artificial neural network ; Chemical reaction ; Algorithm ; Biological system ; Machine learning ; Natural language processing ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Yang Liu | Avik Halder | Söenke Seifert | Nicholas Marcella | Štefan Vajda | Anatoly I. Frenkel
Source: ACS Applied Materials & Interfaces (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

Probing Active Sites in Cu<sub><i>x</i></sub>Pd<sub><i>y</i></sub> Cluster Catalysts by Machine-Learning-Assisted X-ray Absorption Spectroscopy Size-selected clusters are important model catalysts because of their narrow size and compositional distributions, as well as enhanced activity and selectivity in many reactions. Still, their structure-activity relationships are, in general, elusive. The main reason is the difficulty in identifying and quantitatively characterizing the catalytic active site in the clusters when it is confined within subnanometric dimensions and under the continuous structural changes the clusters can undergo in reaction conditions. Using machine learning approaches for analysis of the operando X-ray absorption near-edge structure spectra, we obtained accurate speciation of the Cu<sub><i>x</i></sub>Pd<sub><i>y</i></sub> cluster types during the propane oxidation reaction and the structural information about each type. As a result, we elucidated the information about active species and relative roles of Cu and Pd in the clusters. Cluster (spacecraft) ; Catalysis ; Materials science ; Absorption (acoustics) ; Active site ; Absorption spectroscopy ; XANES ; Spectroscopy ; Chemical physics ; Spectral line ; Propane ; Cluster size ; Selectivity ; X-ray spectroscopy ; X-ray photoelectron spectroscopy ; Crystallography ; Electronic structure ; Computational chemistry ; Chemistry ; Computer science ; Physics ; Thermodynamics ; Optics ; Nuclear magnetic resonance ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Alistair J. Sterling | Stamatia Zavitsanou | Joseph Ford | Fernanda Duarte
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2021-02-17)
Field: Materials Science — Materials Chemistry

Selectivity in organocatalysis—From qualitative to quantitative predictive models Abstract Recent advances in both experimental and computational techniques pose an exciting time for chemistry. Computational tools traditionally used to interpret experimental trends have now evolved into predictive models able to guide the design of novel catalysts. This review discusses the evolution of these models, as well as challenges and future avenues in the field of organocatalysis. Through representative examples we demonstrate how traditional physical organic chemistry tools in combination with machine learning models provide a powerful approach to achieve deeper understanding alongside greater predictive power. This article is categorized under: Structure and Mechanism &gt; Reaction Mechanisms and Catalysis Electronic Structure Theory &gt; Density Functional Theory Data Science &gt; Artificial Intelligence/Machine Learning Organocatalysis ; Predictive power ; Computer science ; Field (mathematics) ; Artificial intelligence ; Biochemical engineering ; Management science ; Machine learning ; Chemistry ; Data science ; Catalysis ; Enantioselective synthesis ; Engineering ; Epistemology ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Sami Malola | Hannu Häkkinen
Source: Nature Communications (article, 2021-04-13)
Field: Materials Science — Materials Chemistry

Prospects and challenges for computer simulations of monolayer-protected metal clusters Precise knowledge of chemical composition and atomic structure of functional nanosized systems, such as metal clusters stabilized by an organic molecular layer, allows for detailed computational work to investigate structure-property relations. Here, we discuss selected recent examples of computational work that has advanced understanding of how these clusters work in catalysis, how they interact with biological systems, and how they can make self-assembled, macroscopic materials. A growing challenge is to develop effective new simulation methods that take into account the cluster-environment interactions. These new hybrid methods are likely to contain components from electronic structure theory combined with machine learning algorithms for accelerated evaluations of atom-atom interactions. Cluster (spacecraft) ; Computer science ; Work (physics) ; Atom (system on chip) ; Nanotechnology ; Chemical physics ; Computational science ; Materials science ; Chemistry ; Physics Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Surface Chemistry and Catalysis
Authors: Wancheng Yu | Can Zhu | Yosuke Tsunooka | Wei Huang | Yifan Dang | Kentaro Kutsukake | Shunta Harada | Miho Tagawa | Toru Ujihara
Source: CrystEngComm (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Geometrical design of a crystal growth system guided by a machine learning algorithm This study proposes a new high-speed method for designing crystal growth systems. It is capable of optimizing large numbers of parameters simultaneously which is difficult for traditional experimental and computational techniques. Crystal (programming language) ; Computer science ; Algorithm Crystallization and Solubility Studies ; Solidification and crystal growth phenomena
Authors: Zezhu Zeng | Cunzhi Zhang | Hulei Yu | Wen Li | Yanzhong Pei | Yue Chen
Source: Materials Today Physics (article, 2021-07-27)
Field: Materials Science — Materials Chemistry

Ultralow and glass-like lattice thermal conductivity in crystalline BaAg2Te2: Strong fourth-order anharmonicity and crucial diffusive thermal transport Anharmonicity ; Thermal conductivity ; Materials science ; Phonon ; Condensed matter physics ; Thermoelectric materials ; Thermal ; Thermal conduction ; Boltzmann equation ; Thermoelectric effect ; Lattice (music) ; Chemical physics ; Thermodynamics ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Cheng Chen | Bo Yang | Rumeng Si | Chen Chen | Fangfang Chen | Rui Gao | Yizhe Li | Jun Tang | Xiaoyi Lv
Source: Optik (article, 2021-05-05)
Field: Chemistry — Analytical Chemistry

Fast detection of cumin and fennel using NIR spectroscopy combined with deep learning algorithms Computer science ; Artificial intelligence ; Identification (biology) ; Pattern recognition (psychology) ; Artificial neural network ; Algorithm ; Deep learning ; Convolutional neural network ; Near-infrared spectroscopy ; Machine learning ; Physics ; Botany ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Mert Y. Sengul | Yao Song | Nadire Nayir | Yawei Gao | Ying Hung | Tirthankar Dasgupta | Adri C. T. van Duin
Source: npj Computational Materials (article, 2021-05-19)
Field: Materials Science — Materials Chemistry

INDEEDopt: a deep learning-based ReaxFF parameterization framework Abstract Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of the parameters is complex and requires the development of new techniques. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to accelerate and improve the quality of the ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model, which finds the minimum discrepancy regions and eliminates unfeasible regions, and constructs a more comprehensive understanding of physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field. We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method. Computer science ; ReaxFF ; Force field (fiction) ; Algorithm ; Chemistry ; Artificial intelligence ; Molecular dynamics ; Computational chemistry ; Interatomic potential Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Dmitriy D. Matyushin | Anastasia Yu. Sholokhova | Aleksey K. Buryak
Source: International Journal of Molecular Sciences (article, 2021-08-25)
Field: Chemistry — Spectroscopy

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16-50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online. Kovats retention index ; Polar ; Chromatography ; Chemistry ; Gas chromatography ; Artificial intelligence ; Analytical Chemistry (journal) ; Biological system ; Computer science Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Marek Gnatowski | Szymon Buchaniec | Grzegorz Brus
Source: International Journal of Hydrogen Energy (article, 2021-10-08)
Field: Materials Science — Materials Chemistry

The prediction of the polarization curves of a solid oxide fuel cell anode with an artificial neural network supported numerical simulation The presented study focuses on a numerical simulation of the transport phenomena inside a solid oxide fuel cell anode. The classical mathematical model leads to a notable discrepancy between measured and predicted overpotentials. One of the possible reasons is the assumption of the constant values of electrochemical reaction charge transfer coefficients. A modified formulation of the problem includes data-driven correction of reaction charge transfer coefficients in the electrochemical reaction model. Here we show a dedicated computational scheme in which an artificial neural network updates the charge transfer coefficients depending on the operational conditions and the available datasets. The neural network was trained on twelve experimental data points of an anode's polarization curve obtained from the literature. The training set contained data for the anode operating in two different temperatures - 800 °C and 900 °C. The test set contained additional six data points for an anode operating at 1000 °C. Charge transfer coefficients were proposed by the Artificial Neural Network as a functional relation of the temperature and withdrawn current. The results of the predictions are juxtaposed with the experimental data from the literature. It was shown that an Artificial Neural Network could improve an electrochemical reaction model in Solid Oxide Fuel Cell modeling. Anode ; Artificial neural network ; Polarization (electrochemistry) ; Solid oxide fuel cell ; Charge transfer coefficient ; Electrochemistry ; Oxide ; Test data ; Approximation error ; Materials science ; Biological system ; Computer science ; Mechanics ; Chemistry ; Algorithm ; Electrode ; Physics ; Artificial intelligence ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Saeed Mohammadzadeh | Emma Lejeune
Source: Extreme Mechanics Letters (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

Predicting mechanically driven full-field quantities of interest with deep learning-based metamodels Field (mathematics) ; Computer science ; Finite element method ; Machine learning ; Artificial intelligence ; Fidelity ; Deep learning ; Algorithm ; Mathematics ; Engineering ; Structural engineering Machine Learning in Materials Science ; Drilling and Well Engineering ; Model Reduction and Neural Networks
Authors: Fabio Leoni | John Russo
Source: Physical Review X (article, 2021-07-09)
Field: Materials Science — Materials Chemistry

Nonclassical Nucleation Pathways in Stacking-Disordered Crystals The nucleation of crystals from liquid melt is often characterized by a competition between different crystalline structures or polymorphs and can result in nuclei with heterogeneous compositions. These mixed-phase nuclei can display nontrivial spatial arrangements, such as layered and onionlike structures, whose composition varies according to the radial distance, and which so far have been explained on the basis of bulk and surface free-energy differences between the competing phases. Here we extend the generality of these nonclassical nucleation processes, showing that layered and onionlike structures can emerge solely based on structural fluctuations even in the absence of free-energy differences. We consider two examples of competing crystalline structures, hcp and fcc forming in hard spheres relevant for repulsive colloids and dense liquids, and the cubic and hexagonal diamond forming in water relevant also for other group 14 elements such as carbon and silicon. We introduce a novel structural order parameter that combined with a neural-network classification scheme allows us to study the properties of the growing nucleus from the early stages of nucleation. We find that small nuclei have distinct size fluctuations and compositions from the nuclei that emerge from the growth stage. The transition between these two regimes is characterized by the formation of onionlike structures, in which the composition changes with the distance from the center of the nucleus, similar to what is seen in the two-step nucleation process. Nucleation ; Chemical physics ; Nucleus ; Diamond ; Materials science ; Stacking ; Crystallography ; Phase (matter) ; Silicon ; Hexagonal crystal system ; Chemistry ; Physics ; Thermodynamics Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Ngoc-Long Tran | Trong-Ha Nguyen | Van-Tien Phan | Duy‐Duan Nguyen
Source: Advances in Materials Science and Engineering (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A Machine Learning‐Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel The purpose of this study is to develop a practical artificial neural network (ANN) model for predicting the atmospheric corrosion rate of carbon steel. A set of 240 data samples, which are collected from the experimental results of atmospheric corrosion in tropical climate conditions, are utilized to develop the ANN model. Accordingly, seven meteorological and chemical factors of corrosion, namely, the average temperature, the average relative humidity, the total rainfall, the time of wetness, the hours of sunshine, the average chloride ion concentration, and the average sulfur dioxide deposition rate, are used as input variables for the ANN model. Meanwhile, the atmospheric corrosion rate of carbon steel is considered as the output variable. An optimal ANN model with a high coefficient of determination of 0.999 and a small root mean square error of 0.281 mg/m 2 .month is retained to predict the corrosion rate. Moreover, the sensitivity analysis shows that the rainfall and hours of sunshine are the most influential parameters on predicting the atmospheric corrosion rate, whereas the average chloride ion concentration, the average temperature, and the time of wetness are less sensitive to the atmospheric corrosion rate. An ANN‐based formula, which accommodates all input parameters, is thereafter proposed to estimate the atmospheric corrosion rate of carbon steel. Finally, a graphical user interface is developed for calculating the atmospheric corrosion rate of carbon steel in tropical climate conditions. Materials science ; Carbon steel ; Corrosion ; Carbon fibers ; Metallurgy ; Composite material ; Composite number Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis