jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi Li | Jingzi Zhang | Ke Zhang | Mengkun Zhao | Kailong Hu | Xi Lin
Source: ACS Applied Materials & Interfaces (article, 2022-12-06)
Field: Materials Science — Materials Chemistry

Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit The figure of merit (<i>zT</i>) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of <i>zT</i> values via machine leaning has emerged as a promising method for exploring high-performance materials. However, the machine learning-based predictions still suffer from unsatisfactory accuracy, and this is related to the size of the data set, the hyperparameters of models, and the quality of the data. In this work, 5038 pieces of data of thermoelectric materials were selected, and several regression models were generated to predict <i>zT</i> values. This large data set-driven light gradient boosting (LGB) model with 57 features performed with an excellent accuracy, achieving a coefficient of determination (<i>R</i><sup>2</sup>) value of 0.959, a root mean squared error (RMSE) of 0.094, a mean absolute error (MAE) of 0.057, and a correlation coefficient (<i>R</i>) of 0.979. Owing to the large size of the data set, the prediction accuracy exceeds that of most reported <i>zT</i> predictions via machine learning. The "M<sub>E</sub> Lattice Parameter" was verified as the most important feature in the <i>zT</i> prediction. Furthermore, nine potential candidates were screened out from among one million pieces of data. This study solves the problem of the data set size, adjusts the hyperparameters of the models, uses feature engineering to improve data quality, and provides an efficient strategy to perform wide-ranging screening for promising materials. Hyperparameter ; Mean squared error ; Figure of merit ; Gradient boosting ; Computer science ; Boosting (machine learning) ; Artificial intelligence ; Machine learning ; Root mean square ; Data set ; Feature engineering ; Feature (linguistics) ; Algorithm ; Statistics ; Mathematics ; Deep learning ; Random forest ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Xiaoyang Wang | Yinan Wang | Linfeng Zhang | Fu‐Zhi Dai | Handong Wang
Source: Nuclear Fusion (article, 2022-08-10)
Field: Materials Science — Materials Chemistry

A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment Abstract Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the understanding of the macroscopic property deterioration of tungsten under harsh and complex service environments. The interatomic potential used in the MD simulations is required to accurately describe a wide spectrum of relevant defect properties, which is by far challenging to the existing interatomic potentials. In this paper, we propose a new three-body embedding descriptor and hybridize it into the deep-potential (DP) framework, an end-to-end deep learning interatomic potential model. The potential model for tungsten, named DP-HYB, is trained with a database constructed by the concurrent learning method. The DP-HYB model is able to accurately predict elastic constants, stacking fault energy, the formation energies of free surfaces, and point defects, which are considered in the training dataset. It also accurately evaluates the formation energies of grain boundaries and prismatic loops, the core structure of screw dislocation, the Peierls barrier, and the transition path of the screw dislocation migration, which do not explicitly present in the training dataset. The DP-HYB is a good candidate for the atomistic simulations of tungsten property deterioration, especially those involving the mechanical property degradation under the harsh fusion service environment. Tungsten ; Materials science ; Interatomic potential ; Molecular dynamics ; Dislocation ; Stacking-fault energy ; Fusion ; Property (philosophy) ; Multiscale modeling ; Atomic units ; Crystallographic defect ; Scale (ratio) ; Potential energy ; Chemical physics ; Computer science ; Statistical physics ; Artificial intelligence ; Condensed matter physics ; Physics ; Computational chemistry ; Atomic physics ; Chemistry ; Composite material ; Metallurgy Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jie Hao | Lihong Gao | Zhuang Ma | Yanbo Liu | Ling Liu | Shizhen Zhu | Tian Weizhi | Xiaoyu Liu | Zhigang Zhou | Alexandr A. Rogachev | Hanyang Liu
Source: Ceramics International (article, 2022-06-16)
Field: Materials Science — Materials Chemistry

Exploration of the oxidation and ablation resistance of ultra-high-temperature ceramic coatings using machine learning Support vector machine ; Lasso (programming language) ; Random forest ; Machine learning ; Materials science ; Artificial intelligence ; Regression ; Algorithm ; Mean squared error ; Ridge ; Mathematics ; Computer science ; Statistics Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: Baqer Aljaman | Usama Ahmed | Umer Zahid | V. Mahendra Reddy | S. Mani Sarathy | Abdul Gani Abdul Jameel
Source: Fuel (article, 2022-02-01)
Field: Chemistry — Analytical Chemistry

A comprehensive neural network model for predicting flash point of oxygenated fuels using a functional group approach Flash point ; Artificial neural network ; Group contribution method ; MATLAB ; Chemistry ; Ether ; Test set ; Training set ; Biological system ; Branching (polymer chemistry) ; Coefficient of determination ; Organic chemistry ; Computer science ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Heat transfer and supercritical fluids
Authors: Jinggang Lan | Vladimir V. Rybkin | Alfredo Pasquarello
Source: Angewandte Chemie International Edition (article, 2022-07-18)
Field: Materials Science — Materials Chemistry

Temperature Dependent Properties of the Aqueous Electron** The temperature-dependent properties of the aqueous electron have been extensively studied using mixed quantum-classical simulations in a wide range of thermodynamic conditions based on one-electron pseudopotentials. While the cavity model appears to explain most of the physical properties of the aqueous electron, only a non-cavity model has so far been successful in accounting for the temperature dependence of the absorption spectrum. Here, we present an accurate and efficient description of the aqueous electron under various thermodynamic conditions by combining hybrid functional-based molecular dynamics, machine learning techniques, and multiple time-step methods. Our advanced simulations accurately describe the temperature dependence of the absorption maximum in the presence of cavity formation. Specifically, our work reveals that the red shift of the absorption maximum results from an increasing gyration radius with temperature, rather than from global density variations as previously suggested. Aqueous solution ; Radius of gyration ; Electron ; RADIUS ; Molecular dynamics ; Absorption (acoustics) ; Work (physics) ; Chemistry ; Absorption spectroscopy ; Thermodynamics ; Range (aeronautics) ; Atmospheric temperature range ; Electron density ; Solvated electron ; Materials science ; Chemical physics ; Physics ; Computational chemistry ; Physical chemistry ; Polymer ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Jie Zhao | Xiaoyan Wang
Source: ACS Omega (article, 2022-03-16)
Field: Materials Science — Materials Chemistry

Screening Perovskites from ABO<sub>3</sub> Combinations Generated by Constraint Satisfaction Techniques Using Machine Learning Perovskite oxides are attractive candidates for various scientific applications because of their outstanding structure flexibilities and attractive physical and chemical properties. However, labor-intensive and high-cost experimental and density functional theory calculation approaches are normally used to screen candidate perovskites. Herein, a machine learning method is employed to identify perovskites from ABO<sub>3</sub> combinations formulated as constraint satisfaction problems based on the restrictions of charge neutrality and Goldschmidt tolerance factor. By eliminating five features based on their correlation and importance, 16 features refined from 21 features are employed to describe 343 known ABO<sub>3</sub> compounds for perovskite formability and stability model training. It is found that the top three features for predicting formability are structural features of the A-O bond length, tolerance, and octahedral factors, whereas the top nine features for predicting the stability are elemental and structural features related to the B-site elements. The precision and recall of the two models are 0.983, 1.00 and 0.971, 0.943, respectively. The formability prediction model categorizes 2229 ABO<sub>3</sub> combinations into 1373 perovskites and 856 nonperovskites, whereas the stability prediction model distinguishes 430 stable perovskites from 1799 unstable ones. Three hundred thirty-eight combinations are recognized as both formable and stable perovskites for future investigation. Formability ; Perovskite (structure) ; Stability (learning theory) ; Materials science ; Density functional theory ; Constraint (computer-aided design) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry ; Crystallography ; Engineering ; Composite material ; Mechanical engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Waheed Tariq | Faizan Ali | Chaudhry Arslan | Abdul Nasir | Syed Hamza Gillani | Abdul Rehman
Source: Frontiers in Environmental Chemistry (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

Synthesis and applications of graphene and graphene-based nanocomposites: Conventional to artificial intelligence approaches Recent advances in graphene research have enabled the utilization of its nanocomposites for numerous energy-based and environmental applications. Recently, the advancement in graphene-based polymer nanocomposites has received much attention with special emphasis on synthesis and application. Graphene-based nanocomposites show astonishing electrical, mechanical, chemical, and thermal characteristics. Graphene nanocomposites (GNCs) are synthesized using a variety of methods, including covalent and non-covalent methods, a chemical-based deposition approach, hydrothermal growth, electrophoresis deposition, and physical deposition. Chemical methods are the most viable route for producing graphene in small quantities at low temperatures. The technique can also produce graphene films on a variety of substrate materials. The use of artificial intelligence (AI) for the synthesis of AI-created nanoparticles has recently received a lot of attention. These nanocomposite materials have excellent applications in the environmental, energy, and agricultural sectors. Due to high carrier mobility, graphene-based materials enhance the photocatalytic performance of semiconductor materials. Similarly, these materials have high potential for pollutant removal, especially heavy metals, due to their high surface area. This article highlights the synthesis of graphene-based nanocomposites with special reference to harnessing the power of modern AI tools to better understand GNC material properties and the way this knowledge can be used for its better applications in the development of a sustainable future. Graphene ; Nanocomposite ; Nanotechnology ; Materials science ; Nanoparticle Graphene research and applications ; Graphene and Nanomaterials Applications ; Supercapacitor Materials and Fabrication
Authors: Yunzhe Wang | Shanping Liu | Peter Lile | Sam Walton Norwood | Alberto Hernández | Sukriti Manna | Tim Mueller
Source: npj Computational Materials (article, 2022-08-19)
Field: Materials Science — Materials Chemistry

Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning Abstract The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but predicting the stable structures of clusters can be computationally expensive. In this work, we present a procedure for rapidly predicting low-energy structures of nanoclusters by combining a genetic algorithm with interatomic potentials actively learned on-the-fly. Applying this approach to aluminum clusters with 21 to 55 atoms, we have identified structures with lower energy than any reported in the literature for 25 out of the 35 sizes. Our benchmarks indicate that the active learning procedure accelerated the average search speed by about an order of magnitude relative to genetic algorithm searches using only density functional calculations. This work demonstrates a feasible way to systematically discover stable structures for large nanoclusters and provides insights into the transferability of machine-learned interatomic potentials for nanoclusters. Nanoclusters ; Cluster (spacecraft) ; Transferability ; Computer science ; Work (physics) ; Energy (signal processing) ; Genetic algorithm ; On the fly ; Algorithm ; Machine learning ; Materials science ; Physics ; Nanotechnology Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Jaber Rezaei Mianroodi | Pratheek Shanthraj | Chuanlai Liu | Samad Vakili | Sharan Roongta | Nima H. Siboni | Nathan Perchikov | Yang Bai | Bob Svendsen | Franz Roters | Dierk Raabe | Martin Diehl
Source: npj Computational Materials (article, 2022-04-28)
Field: Materials Science — Materials Chemistry

Modeling and simulation of microstructure in metallic systems based on multi-physics approaches Abstract The complex interplay between chemistry, microstructure, and behavior of many engineering materials has been investigated predominantly by experimental methods. Parallel to the increase in computer power, advances in computational modeling methods have resulted in a level of sophistication which is comparable to that of experiments. At the continuum level, one class of such models is based on continuum thermodynamics, phase-field methods, and crystal plasticity, facilitating the account of multiple physical mechanisms (multi-physics) and their interaction during microstructure evolution. This paper reviews the status of simulation approaches and software packages in this field and gives an outlook towards promising research directions. Microstructure ; Sophistication ; Statistical physics ; Field (mathematics) ; Crystal plasticity ; Physics ; Computer science ; Computational science ; Materials science ; Metallurgy ; Mathematics Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Solidification and crystal growth phenomena
Authors: Qiyuan Zhao | Yinan Xu | Jeffrey Greeley | Brett M. Savoie
Source: Nature Communications (article, 2022-08-18)
Field: Materials Science — Materials Chemistry

Deep reaction network exploration at a heterogeneous catalytic interface Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga<sup>3+</sup> as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications. Computer science ; Catalysis ; Interface (matter) ; Distributed computing ; Heterogeneous network ; Network model ; Chemistry ; Artificial intelligence ; Parallel computing ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Nouf M. Al‐Enazi | Khawla Alsamhary | Mansour Kha | Fuád Ameén
Source: Bioprocess and Biosystems Engineering (article, 2022-12-20)
Field: Materials Science — Materials Chemistry

In vitro anticancer and antibacterial performance of biosynthesized Ag and Ce co-doped ZnO NPs Nanoparticle ; Materials science ; Cytotoxicity ; Nanotechnology ; Doping ; Zinc ; Nuclear chemistry ; Scanning electron microscope ; In vitro ; Chemical engineering ; Chemistry ; Biochemistry ; Metallurgy ; Composite material Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics
Authors: Petr Rozhin | Jada Abdel Monem Gamal | Silvia Giordani | Silvia Marchesan
Source: Materials (review, 2022-01-28)
Field: Materials Science — Materials Chemistry

Carbon Nanomaterials (CNMs) and Enzymes: From Nanozymes to CNM-Enzyme Conjugates and Biodegradation Carbon nanomaterials (CNMs) and enzymes differ significantly in terms of their physico-chemical properties-their handling and characterization require very different specialized skills. Therefore, their combination is not trivial. Numerous studies exist at the interface between these two components-especially in the area of sensing-but also involving biofuel cells, biocatalysis, and even biomedical applications including innovative therapeutic approaches and theranostics. Finally, enzymes that are capable of biodegrading CNMs have been identified, and they may play an important role in controlling the environmental fate of these structures after their use. CNMs' widespread use has created more and more opportunities for their entry into the environment, and thus it becomes increasingly important to understand how to biodegrade them. In this concise review, we will cover the progress made in the last five years on this exciting topic, focusing on the applications, and concluding with future perspectives on research combining carbon nanomaterials and enzymes. Nanotechnology ; Biochemical engineering ; Biocatalysis ; Biodegradation ; Nanomaterials ; Chemistry ; Materials science ; Engineering ; Biochemistry ; Catalysis ; Organic chemistry Advanced Nanomaterials in Catalysis ; Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors
Authors: Giovanni Trezza | Luca Bergamasco | Matteo Fasano | Eliodoro Chiavazzo
Source: npj Computational Materials (article, 2022-06-03)
Field: Chemistry — Inorganic Chemistry

Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization Abstract We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO 2 and H 2 O. A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified. In energy transformations, thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values, which is often only partially accessible, hence possibly preventing the computation of desired objective functions. We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area. We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications. Sorption ; Sorbent ; Metal-organic framework ; Computation ; Underpinning ; Set (abstract data type) ; Materials science ; Figure of merit ; Thermodynamics ; Computer science ; Chemistry ; Adsorption ; Physics ; Algorithm ; Physical chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Ellie Bennett | Matthew W. Greenberg | Abraham J. Jordan | Leslie S. Hamachi | Soham Banerjee | Simon J. L. Billinge | Jonathan S. Owen
Source: Chemistry of Materials (article, 2022-01-06)
Field: Materials Science — Materials Chemistry

Size Dependent Optical Properties and Structure of ZnS Nanocrystals Prepared from a Library of Thioureas ZnS nanocrystals (λmax(1Se–1S3/2h) = 260–320 nm, d = 1.7–10.0 nm) are synthesized from Zn(O2CR)2 (O2CR = tetradecanoate, oleate and 2-hexyldecanoate), N,N′-disubstituted and N,N′,N′-trisubstituted thioureas, and P,P,N-trisubstituted phosphanecarbothioamides. The influence of precursor substitution, ligand sterics, and reaction temperature on the final nanocrystal size was evaluated. Using saturated hydrocarbon solvents and saturated aliphatic carboxylate ligands, polymeric byproducts could be avoided and pure ZnS nanocrystals isolated. Elevated temperatures, slower precursor conversion reactivity, and branched zinc 2-hexyldecanoate yield the largest ZnS nanocrystals. Carefully purified zinc carboxylate, rapidly converting precursors, and cooling the synthesis mixture following complete precursor conversion provide quasispherical nanocrystals with the narrowest shape dispersity. Nanocrystal sizes were measured using pair distribution function (PDF) analysis of X-ray scattering and scanning transmission electron microscopy (STEM) and plotted versus the energy of their first excitonic optical absorption. The resulting empirical relationship provides a useful method to characterize the nanocrystal size from 1.7 to 4.0 nm using optical absorption spectroscopy. Nanocrystal ; Dispersity ; Carboxylate ; Absorption (acoustics) ; Absorption spectroscopy ; Zinc ; Reactivity (psychology) ; Dynamic light scattering ; Chemistry ; Spectroscopy ; Materials science ; Analytical Chemistry (journal) ; Crystallography ; Organic chemistry ; Nanotechnology ; Nanoparticle ; Optics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications