jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Oscar Ramírez | Sebastián Castillo | Sebastián Bonardd | César Saldías | James N. O’Shea | Christopher Philip Clive Ryder | David Díaz Díaz | Leiva Angel
Source: Research Square (preprint, 2023-05-02)
Field: Materials Science — Materials Chemistry

Marrying plasmonic earth-abundant metals with catalytic metals for visible-light- promoted hydrogen generation on biobased materials Abstract Bimetallic CuPt alloyed nanoparticles were conveniently synthesized on biohydrogels and were capable of carrying out hydrogen release from ammonium borane hydrolysis. The biohydrogel consisted of bead-shaped alginate chains crosslinked by calcium ions, which were used as support material to synthesize and stabilize the bimetallic nanoparticles, employing adsorption and coreduction strategy steps. The as-prepared nanoparticles exhibited light absorption in the visible range (580 nm) resulting from the surface plasmon resonance (SPR) phenomenon ascribed to the presence of copper in the alloyed system. On the other hand, the presence of platinum atoms in these nanoalloys endows them with a notable catalytic performance toward ammonia borane hydrolysis as a hydrogen release reaction, reaching k r values from 0.32 x 10 -4 to 2.23 x 10 -4 mol L -1 min -1 as the Pt content increases. Finally, by taking advantage of the SPR light absorption shown by CuPt 1:1, it was demonstrated that these entities could be successfully employed as photocatalysts for the hydrogen generation reaction, boosting its activity by almost 2.06 times compared to its performance in dark conditions. This catalytic enhancement was mainly ascribed to the plasmonic-derived light-harvesting properties and the specimen's metallic composition. Ammonia borane ; Bimetallic strip ; Catalysis ; Nanoparticle ; Materials science ; Adsorption ; Hydrogen production ; Hydrolysis ; Visible spectrum ; Hydrogen ; Surface plasmon resonance ; Inorganic chemistry ; Photocatalysis ; Chemical engineering ; Photochemistry ; Chemistry ; Nanotechnology ; Organic chemistry Hydrogen Storage and Materials ; Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques
Authors: Vahid Jamebozorgi | Karsten Rasim | Christian Schröder
Source: Research Square (preprint, 2023-05-19)
Field: Materials Science — Metals and Alloys

Multiparadigm modeling of texture effects on pitting corrosion in ferritic stainless steel Abstract Texture has long been recognized as a critical factor influencing the physical processes and properties of condensed matter. In this study, we implemented a multiparadigm approach and introduce a robust methodology to investigate the effects of texture in corrosion, which overcomes the common limitations of quantum-based computational approaches in terms of time and system size. Our approach provides the same level of accuracy as atomistic calculations but with significantly less computational cost. The methodology, based on the finite element method (FEM), employs 3D digital representations of polycrystalline microstructures. As a proof of concept, we apply our approach to the case of pitting corrosion in ferritic stainless steel. As shown in the literature irregular pit growth patterns through pitting corrosion are primarily caused by texture. Our study reveals that texture has a significant impact on the pitting corrosion rate leading to a wide range of irregular pit growth patterns in polycrystals. Our findings are supported by atomistic calculations and experimental literature, demonstrating the validity of our approach. Texture (cosmology) ; Corrosion ; Pitting corrosion ; Crystallite ; Materials science ; Microstructure ; Finite element method ; Metallurgy ; Computer science ; Structural engineering ; Artificial intelligence ; Engineering ; Image (mathematics) Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Microstructure and mechanical properties
Authors: Ashna Jose | Émilie Devijver | N. Jakse | Roberta Poloni
Source: ChemRxiv (preprint, 2023-12-01)
Field: Materials Science — Materials Chemistry

Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning In recent data-driven approaches to materials discov- ery, scenarios where target quantities are expensive to compute or measure are often overlooked. In such cases, it becomes imperative to construct a training set that includes the most diverse, representative, and informative samples. Here, a novel regression tree-based active learning algorithm is employed for such a purpose. It is applied to predict band gap and adsorption properties of metal-organic frameworks (MOFs), a novel class of materials that results from the virtually infinite combinations of their building units. Simpler and low dimensional descrip- tors, such as the Stoichiometric-120 and geometric properties, found here to better represent MOFs in the low data regime, are used to compute the feature space for this model. The partition given by a regression tree constructed on the labeled part of the dataset is used to select new samples to be added to the training set, thereby limiting its size while maximizing the prediction quality. Through tests on the QMOF, hMOF, and dMOF data sets, we show that our method is effective in constructing small training data sets to learn regression models that predict well the target properties, thus reducing the label- ing cost. Specifically, our active learning approach is highly beneficial when labels are unevenly distributed in the descriptor space and when the label distribution is imbalanced, which is often the case for real world data. This offers a unique tool to efficiently analyze complex structure-property relationships in materials and accelerate materials discovery. Property (philosophy) ; Computer science ; Partition (number theory) ; Tree (set theory) ; Machine learning ; Regression ; Set (abstract data type) ; Artificial intelligence ; Class (philosophy) ; Training set ; Data mining ; Feature (linguistics) ; Limiting ; Measure (data warehouse) ; Mathematics ; Statistics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Shuang Han | Steen Lysgaard | Tejs Vegge | Heine Anton Hansen
Source: ChemRxiv (preprint, 2023-04-28)
Field: Materials Science — Materials Chemistry

Rapid mapping of alloy surface phase diagrams via Bayesian evolutionary multitasking Surface phase diagrams are essential for understanding the dependence of surface chemistry on reaction condition. For multi-component systems such as alloys, the derivation of such diagrams often relies on separate global optimization tasks under different reaction conditions. Here we show that this can be significantly accelerated by leveraging the fact that all tasks essentially share a unified configurational search space, and only a single expensive electronic structure calculation is required to evaluate the stability of a surface structure under all reaction conditions. In particular, we present a novel Bayesian evolutionary multitasking (BEM) framework combining Bayesian statistics with evolutionary multitasking, which allows efficient mapping of surface phase diagrams for very complex heterogeneous systems. As proofs of concept, we showcase the performance of our methods in deriving the alloy surface phase diagrams for two heterogeneous catalytic systems: the electrochemical oxygen reduction reaction (ORR) and the gas phase steam methane reforming (SMR) reaction. Human multitasking ; Computer science ; Bayesian probability ; Surface (topology) ; Phase diagram ; Steam reforming ; Materials science ; Phase (matter) ; Chemistry ; Artificial intelligence ; Mathematics ; Catalysis ; Geometry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jing Huang | Lei Yang | Shasha Zeng
Source: Materials Science (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Structures and Electronic Properties of Titanium Oxo Clusters with Tartrate Ligand: A Density Functional Theory Study Titanium oxo clusters are considered a model for nanotitanium dioxide and have gradually become a new field of functional materials in solar cells. In this work, the precise structural information of two types of tartrate-based titanium-oxo clusters was investigated by quantum chemical approaches. Density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations provided the precise structural parameters of Ti4(C4H2O6)(AS)2(OiPr)10 and Ti4(C4H2O6)(ATS)2(OiPr)10. Our work provided reliable results in agreement with the experimental results. In both molecules, the Ti-O bonds were mainly divided into three categories: the bonds in the first category were the coordination bonds between the central skeleton Ti atom and the tartaric acid group, whose bond lengths were 2.000 – 2.080 Å; the bonds in the second category were the coordination bond formed by the Ti atom and the oxygen on the AS ligand sulphonic acid group, whose bond lengths were 2.148 – 2.310 Å; the bonds in the third category were the Ti-O bonds with shorter bond lengths, which were the coordination bond formed by the Ti atom and the oxygen on the isopropylalkoxy group with their bond lengths in the value range from 1.777 Å to 1.795 Å. These kinds of Ti-O bonds also can be found in other Ti4 titanium-oxo clusters. Molecular orbitals showed that electron transfer from the AS/ATS ligand to the {Ti4} central skeleton was induced by sunlight with maximum UV-Vis absorption of 605/629 nm. Our calculation results provide strong guidance for the discovery of new efficient titanium oxo in solar cells. Density functional theory ; Bond length ; Ligand (biochemistry) ; Crystallography ; Chemistry ; Time-dependent density functional theory ; Atom (system on chip) ; Molecule ; Computational chemistry ; Bond order ; Titanium ; Crystal structure ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Magnetism in coordination complexes
Authors: Satish Kumar Awasthi | Parbati Biswas | Brajendra K. Singh | Sasanka Deka | Sandeep Kaur‐Ghumaan
Source: ChemistrySelect (article, 2023-12-11)
Field: Chemistry — Physical and Theoretical Chemistry

Celebrating 100 Years of University of Delhi (1922–2022) Abstract University of Delhi (DU) has attained global recognition for its research and teaching in Chemistry. This Guest Editorial introduces the research showcased in the “Institute Feature: Celebrating 100 Years of University of Delhi (1922–2022) ” ‐ a joint special collection from the European Journal of Organic Chemistry (EurJOC) , European Journal of Inorganic Chemistry (EurJIC) and ChemistrySelect The collection highlights the contributions to the field of chemistry through research articles and reviews authored by faculty members from the Department of Chemistry, alumni of the department and faculty members of chemistry department of various colleges affiliated with the university, celebrating 100 years of DU. Library science ; New delhi ; Chemistry ; Engineering ; History ; Computer science ; Archaeology History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Shayantan Chaudhuri | Andrew J. Logsdail | Reinhard J. Maurer
Source: arXiv (Cornell University) (preprint, 2023-06-09)
Field: Materials Science — Materials Chemistry

Stability of Single Metal Atoms on Defective and Doped Diamond Surfaces Polycrystalline boron-doped diamond (BDD) is widely used as a working electrode material in electrochemistry, and its properties, such as its stability, make it an appealing support material for nanostructures for electrocatalytic applications. Recent experiments have shown that electrodeposition can lead to the creation of stable small nanoclusters and even single metal adatoms on BDD surfaces. We investigate the adsorption energy and kinetic stability of single metal atoms adsorbed onto an atomistic model of BDD surfaces using density functional theory. The surface model is constructed using hybrid quantum/molecular mechanics embedding techniques and is based on an oxygen-terminated diamond (110) surface. We use the hybrid quantum mechanics/molecular mechanics method to assess the ability of different density-functional approximations to predict the adsorption structure, energy and the barrier for diffusion on pristine and defective surfaces. We find that surface defects (vacancies and surface dopants) strongly anchor metal adatoms on vacancy sites. We further investigate the thermal stability of metal adatoms, which reveals high barriers associated with lateral diffusion away from the vacancy site. The result provides an explanation for the high stability of experimentally imaged single metal adatoms on BDD and a starting point to investigate the early stages of nucleation during metal surface deposition. Materials science ; Diamond ; Nanoclusters ; Chemical physics ; Density functional theory ; Vacancy defect ; Surface diffusion ; Dopant ; Nucleation ; Kinetic Monte Carlo ; Adsorption ; Nanotechnology ; Doping ; Crystallography ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Chemistry ; Composite material ; Optoelectronics Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; Surface Chemistry and Catalysis
Authors: Tamás Pajkossy
Source: Journal of Solid State Electrochemistry (article, 2023-02-08)
Field: Chemistry — Electrochemistry

Past and present of electrochemical science in Hungary Abstract The electrochemistry-related scientific activities in Hungary over the past 3 decades are reviewed. In the first section, we summarize those research areas that are already ceased; in the next section, the ongoing research is discussed; finally, the trends and outlook are highlighted. A special emphasis is put on new experimental methods elaborated in the country. Section (typography) ; Special section ; Electrochemistry ; Nanotechnology ; Engineering ; Engineering ethics ; Engineering physics ; Computer science ; Materials science ; Chemistry ; Physical chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Advancements in Battery Materials
Authors: Harender S. Dhattarwal | Ang Gao | Richard C. Remsing
Source: arXiv (Cornell University) (preprint, 2023-01-17)
Field: Materials Science — Materials Chemistry

Dielectric Saturation in Water from a Long Range Machine Learning Model Machine learning-based neural network potentials have the ability to provide ab initio-level predictions while reaching large length and time scales often limited to empirical force fields. Traditionally, neural network potentials rely on a local description of atomic environments to achieve this scalability. These local descriptions result in short range models that neglect long range interactions necessary for processes like dielectric screening in polar liquids. Several approaches to including long range electrostatic interactions within neural network models have appeared recently, and here we investigate the transferability of one such model, the self consistent neural network (SCFNN), which focuses on learning the physics associated with long range response. By learning the essential physics, one can expect that such a neural network model should exhibit at least partial transferability. We illustrate this transferability by modeling dielectric saturation in a SCFNN model of water. We show that the SCFNN model can predict non-linear response at high electric fields, including saturation of the dielectric constant, without training the model on these high field strengths and the resulting liquid configurations. We then use these simulations to examine the nuclear and electronic structure changes underlying dielectric saturation. Our results suggest that neural network models can exhibit transferability beyond the linear response regime and make genuine predictions when the relevant physics is properly learned. Dielectric ; Artificial neural network ; Transferability ; Scalability ; Computer science ; Machine learning ; Saturation (graph theory) ; Artificial intelligence ; Statistical physics ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science
Authors: Markus Reiher | Katja‐Sophia Csizi | Miguel Steiner
Source: Research Square (preprint, 2023-10-17)
Field: Materials Science — Materials Chemistry

Quantum Magnifying Glass for Chemistry at the Nanoscale Quantum ; Parametrization (atmospheric modeling) ; Nanoscopic scale ; Quantum state ; Computer science ; Nanotechnology ; Statistical physics ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Andreas Erbe | Marc Tesch | Olaf Ru diger | Bernhard Kaiser | Serena Debeer | Martin Rabe | Veljko Vujc | Bratislav Marinkovic | Vladimir Srec kovic | Sanja Tos ic | Darko Jevremovic | Ljubinko Ignjatovic | Maja Rabasovic | Dragutin evic | Nenad Simonovic | Nigel Mason | M Karplus | W Miller | E Neher | H Schwarz | J Simons | G Somorjai | R Zare | B Albinsson | H Kondoh | J Zhang | C Adamo | M Devries | C Diaz | S George | D Ghosh | K Holmberg | G Meijer | G Patwari | M Rodgers | S Sampath | M Suhm | A Troisi | Poonam Rawat | Pulkit Sharma | Prince Trivedi | Faraz Ghous | Abha Bishnoi | ; Jacqueline | A Labra-Mun oz | S Herre | Van Der Zant | Vera C alukovic | Anton Lefering | Ramon Egli | * | Martina Huber | ; Dale | T Duncan | Samantha Piper | Maria Forsyth | Douglas Macfarlane | Mega Kar | ; Gao | P Zou | * Hou | J Wu | Z Wang | J | Yewen Zhang | Wenjing Li | Cefeng Zhou | Runfeng Chen | ; Zhikai Liu | Hailu Liu | Fayong Li | Lingfeng Li | Chunyu Du | Jia Li | * Dong Xie | Guokang Han
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2023, 25, 26935 DOI: 10.1039/D3CP90211K This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Computer science ; Library science ; Chemistry ; Computer security ; Psychology Luminescence Properties of Advanced Materials ; Quantum Dots Synthesis And Properties ; Catalytic Processes in Materials Science
Authors: M Karplus | W Miller | E Neher | H Schwarz | J Simons | G Somorjai | R Zare | B Albinsson | H Kondoh | J Zhang | C Adamo | M Devries | C Diaz | S George | D Ghosh | K Holmberg | G Meijer | G Patwari | M Rodgers | S Sampath | M Suhm | A Troisi | Ke Wang | Kai Ren | Yinlong Hou | Yuan Cheng | * | Gang Zhang | ; Chemshell | You Lu | Kakali Sen | Chin Yong | David Gunn | John Purton | Jingcheng Guan | Alec Desmoutier | Jamal Nasir | Xingfan Zhang | Lei Zhu | Qing Hou | Joe Jackson-Masters | Sam Watts | Rowan Hanson | Harry Thomas | Omal Jayawardena | Andrew Logsdail | Scott Woodley | Hans Senn | Paul Sherwood | C Richard | Alexey Catlow | Thomas Sokol | Pan Keal ; Guan | Yujin Wang | Youyong Ji | Yang Li | M Song ; Abdulrahman | Jonas Alfaraidi | Eric Schaab | Michael Mcclure | Taylor Kellogg | Muazzam Hodgkins | Stephen Idris | Brent Bradforth | * Melot | E Mark | Peter Thompson | Fang Djurovich | Zhen Xu | Wei Zhao | Dongzhi Li | Xueqin Liu | * Zhou | Lichang Wang ; Lei | Linwen Jia | * Haoran Jiang | Siqin Zhou | Anhua Yan | Xiaofeng Wu | Zhang
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2023, 25, 21753 DOI: 10.1039/D3CP90179C This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Computer science ; Library science ; Physics ; Chemistry ; Computer security ; Psychology Luminescence Properties of Advanced Materials ; Quantum Dots Synthesis And Properties ; Catalytic Processes in Materials Science
Authors: Firat Yalcin | Michael Wolloch
Source: arXiv (Cornell University) (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

SurfFlow: high-throughput surface energy calculations for arbitrary crystals We introduce SurfFlow, an open-source high-throughput workflow package designed for automated first-principles calculations of surface energies in arbitrary crystals. Our package offers a comprehensive solution capable of handling multi-element crystals, nonstoichiometric compositions, and asymmetric slabs, for all potential terminations. To streamline the computational process, SurfFlow employs an efficient pre-screening method that discards surfaces with suspected high surface energy before conducting resource-intensive density functional theory computations. The results generated are seamlessly compiled into an optimade-compliant database, ensuring easy access and compatibility. Additionally, a user-friendly web interface facilitates workflow submission and management, provides result visualization, and enables the examination of Wulff shapes. SurfFlow represents a valuable tool for researchers looking to explore surface energies and their implications in a diverse range of systems. Workflow ; Computation ; Computer science ; Computational science ; Visualization ; Throughput ; Compatibility (geochemistry) ; Distributed computing ; Materials science ; Nanotechnology ; Database ; Algorithm ; Data mining ; Operating system Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Andrij Vasylenko | Benjamin M. Asher | Chris Collins | Michael W. Gaultois | George R. Darling | Matthew S. Dyer | Matthew J. Rosseinsky
Source: arXiv (Cornell University) (preprint, 2023-02-01)
Field: Materials Science — Materials Chemistry

Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation. Computer science ; Sampling (signal processing) ; Bayesian optimization ; Bayesian probability ; Focus (optics) ; Throughput ; Energy (signal processing) ; Chemical space ; Space (punctuation) ; Machine learning ; Biochemical engineering ; Process engineering ; Artificial intelligence ; Chemistry ; Mathematics ; Engineering ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions