jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingru Lu | David C. Leitch
Source: ChemRxiv (preprint, 2023-08-16)
Field: Materials Science — Materials Chemistry

Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions With the advent of high-throughput methods for both computation and experimentation, data-rich approaches to discovering and understanding chemical reactions are becoming ever more central to catalysis research. Organopalladium catalysis is at the forefront of these new approaches, providing a rich proving ground for method development and validation. This critical Perspective discusses a number of recent case studies from academic and industrial laboratories that illustrate how to generate, analyze, and correlate large data sets for quantitative predictions of reactivity and selectivity. Both the power and potential pitfalls of these approaches are discussed, as are the opportunities for both practical predictions and fundamental mechanistic insights. Organopalladium ; Biochemical engineering ; Computer science ; Reactivity (psychology) ; Perspective (graphical) ; Data science ; Management science ; Catalysis ; Chemistry ; Artificial intelligence ; Engineering ; Palladium Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Jingru Lu | David C. Leitch
Source: ChemRxiv (preprint, 2023-10-10)
Field: Materials Science — Materials Chemistry

Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions With the advent of high-throughput methods for both computation and experimentation, data-rich approaches to discovering and understanding chemical reactions are becoming ever more central to catalysis research. Organopalladium catalysis is at the forefront of these new approaches, providing a rich proving ground for method development and validation. This critical Perspective discusses a number of recent case studies from academic and industrial laboratories that illustrate how to generate, analyze, and correlate large data sets for quantitative predictions of reactivity and selectivity. Both the power and potential pitfalls of these approaches are discussed, as are the opportunities for both practical predictions and fundamental mechanistic insights. Organopalladium ; Computer science ; Biochemical engineering ; Reactivity (psychology) ; Perspective (graphical) ; Throughput ; Data science ; Management science ; Catalysis ; Chemistry ; Artificial intelligence ; Engineering ; Palladium Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Shaoxun Fan | Andrew L. Hitt | Ming Tang | Babak Sadigh | Fei Zhou
Source: arXiv (Cornell University) (preprint, 2023-10-23)
Field: Materials Science — Materials Chemistry

Accelerate Microstructure Evolution Simulation Using Graph Neural Networks with Adaptive Spatiotemporal Resolution Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided. Computer science ; Speedup ; Adaptive mesh refinement ; Convolutional neural network ; Representation (politics) ; Graph ; Microstructure ; Artificial neural network ; Pipeline (software) ; Ground truth ; Computational science ; Artificial intelligence ; Algorithm ; Parallel computing ; Theoretical computer science ; Materials science Machine Learning in Materials Science ; Magnetic Properties and Applications ; Metallurgy and Material Forming
Authors: Mohammad Jabed Perves Bappy | Gerard Sapena Polo | Anthony Monistere | David K. Mills
Source: Biomedical Materials & Devices (article, 2023-08-07)
Field: Materials Science — Biomaterials

Electrospinning of Silver and Zinc-Coated Halloysite Nanotube Polylactic Acid Scaffolds Polylactic acid ; Halloysite ; Materials science ; Electrospinning ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Chemical engineering ; Coating ; Nanofiber ; Nuclear chemistry ; Fiber ; Nanoparticle ; Zinc ; Polymer ; Composite material ; Nanotechnology ; Chemistry ; Metallurgy Clay minerals and soil interactions ; Therapeutic Uses of Natural Elements ; Iron oxide chemistry and applications
Authors: Yuqing Shi | Haidong Xu | Yijing He | Xuan Tang | Hongru Tian | Jie Liang
Source: ACS Omega (article, 2023-06-05)
Field: Chemistry — Organic Chemistry

Antibacterial Mesoporous Silica Granules Containing a Stable N-Halamine Moiety High-efficacy and regenerable antimicrobial silica granules were prepared via oxa-Michael addition between poly(vinyl alcohol) (PVA) and methylene-bis-acrylamide (MBA) under the catalysis of sodium carbonate in an aqueous solution. Diluted water glass was added, and the solution pH was adjusted to about 7 to precipitate PVA-MBA modified mesoporous silica (PVA-MBA@SiO<sub>2</sub>) granules. N-Halamine-grafted silica (PVA-MBA-Cl@SiO<sub>2</sub>) granules were achieved by adding diluted sodium hypochlorite solution. It was found that a BET surface area of about 380 m<sup>2</sup> g<sup>-1</sup> for PVA-MBA@SiO<sub>2</sub> granules and a Cl<sup>+</sup>% of about 3.80% for PVA-MBA-Cl@SiO<sub>2</sub> granules could be achieved under optimized preparation conditions. Antimicrobial tests showed that the as-prepared antimicrobial silica granules were capable of about a 6-log inactivation of <i>Staphylococcus aureus</i> and <i>Escherichia coli</i> O157:H7 within 10 min of contact. Furthermore, the as-prepared antimicrobial silica granules can be recycled many times due to the excellent regenerability of their N-halamine functional groups and can be saved for a long time. With the above-mentioned advantages, the granules have potential applications in water disinfection. Sodium hypochlorite ; Aqueous solution ; Mesoporous silica ; Vinyl alcohol ; Nuclear chemistry ; Antimicrobial ; Chemistry ; Methylene blue ; Moiety ; Polymer chemistry ; Materials science ; Chemical engineering ; Mesoporous material ; Organic chemistry ; Catalysis ; Photocatalysis ; Polymer Antimicrobial agents and applications ; Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Kianoosh Sattari | Dawei Li | Yunchao Xie | Olexandr Isayev | Jian Lin
Source: ChemRxiv (preprint, 2023-04-09)
Field: Materials Science — Materials Chemistry

De Novo Design of Molecules Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning De Novo design of molecules with targeted properties represents a new frontier in molecule development. Despite enormous progress, two main challenges remain, i.e., (i) generation of novel molecules with targeted and quantifiable properties; (ii) generated molecules having property values beyond the range in the training dataset. To tackle these challenges, we propose a novel reinforced regressional and conditional generative adversarial network (RRCGAN) to generate chemically valid, small molecules with targeted heat capacity (Cv) values as a proof-of-concept study. As validated by DFT, ~80% of the generated samples have a relative error (RE) of < 20% of the targeted Cv values. To bias the generation of molecules with the Cv values beyond the range of the original training molecules, transfer learning was applied to iteratively retrain the RRCGAN model. After only two iterations of transfer learning, the mean Cv of the generated molecules increases to 44.0 cal/(mol·K) from the mean value of 31.6 cal/(mol·K) shown in the initial training dataset. This demonstrated computation methodology paves a new avenue to discovering small molecules with biased properties. Molecule ; Generative grammar ; Range (aeronautics) ; Computation ; Transfer of learning ; Computer science ; Small molecule ; Artificial intelligence ; Mathematics ; Algorithm ; Machine learning ; Physics ; Materials science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Megan Stanley | Marwin Segler
Source: ChemRxiv (preprint, 2023-07-12)
Field: Materials Science — Materials Chemistry

Fake it until you make it? Generative De Novo Design and Virtual Screening of Synthesizable Molecules Computational techniques, including virtual screening, de novo design, and generative models, play an increasing role in expediting DMTA cycles for modern molecular discovery. However, computationally proposed molecules must be synthetically feasible for laboratory testing. In this perspective, we offer a succinct introduction to the subject, showcase typical workflows to integrate synthesis planning, synthesizability scoring, and molecule generation. Finally, we address limitations and opportunities for future research. Expediting ; Workflow ; Virtual screening ; Generative grammar ; Computer science ; Perspective (graphical) ; Data science ; Software engineering ; Artificial intelligence ; Systems engineering ; Drug discovery ; Engineering ; Chemistry Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Briséïs Mercadier | Samuel W. Coles | Mathieu Duttine | Christophe Legein | Monique Body | Olaf J. Borkiewicz | Oleg I. Lebedev | Benjamin J. Morgan | Christian Masquelier | Damien Dambournet
Source: ChemRxiv (preprint, 2023-09-06)
Field: Chemistry — Inorganic Chemistry

Dynamic Lone Pairs and Fluoride-Ion Disorder in Cubic-BaSnF4 Introducing compositional or structural disorder within crystalline solid electrolytes is a common strategy for increasing their ionic conductivity. (M,Sn)F2 fluorites have previously been proposed to exhibit two forms of disorder within their cationic host frameworks: occupational disorder from randomly distributed M and Sn cations, and orientational disorder from Sn(II) stereoactive lone pairs. Here, we characterise the structure and fluoride-ion–dynamics of cubic-BaSnF4, using a combination of experimental and computational techniques. Rietveld refinement of XRD data confirms an average fluorite structure with {Ba,Sn} cation disorder, and the 119Sn Mo ̈ssbauer spectrum demonstrates the presence of stereoactive Sn(II) lone pairs. X-ray total-scattering PDF analysis and ab initio molecular dynamics simulations reveal a complex local structure with a high degree of intrinsic fluoride-ion disorder, where 1/3 of fluoride ions occupy octahedral “interstitial” sites: this fluoride-ion disorder is a consequence of repulsion between Sn lone pairs and fluoride ions that destabilises Sn-coordinated tetrahedral fluoride-ion sites. Variable-temperature 19F NMR experiments and analysis of our molecular dynamics simulations reveal highly inhomogeneous fluoride-ion dynamics, with fluoride ions in Sn-rich local environments significantly more mobile than those in Ba-rich environments. Our simulations also reveal dynamical reorientation of the Sn lone pairs that is biased by the local cation configuration and is coupled to the local fluoride-ion dynamics. We end by discussing the effect of host-framework disorder on long-range diffusion pathways in cubic BaSnF4 . Fluoride ; Lone pair ; Ion ; Ionic bonding ; Chemistry ; Crystallography ; Molecular dynamics ; Fluorite ; Octahedron ; Chemical physics ; Rietveld refinement ; Crystal structure ; Computational chemistry ; Inorganic chemistry ; Molecule Inorganic Fluorides and Related Compounds ; Molten salt chemistry and electrochemical processes ; Advanced Condensed Matter Physics
Authors: Abdul Abdul | Altaf Ur Rahman Khan | Bo Jing | Jehan Akbar Khan | Qammer H. Abbasi | Jingfu Bao
Source: Research Square (preprint, 2023-11-23)
Field: Materials Science — Materials Chemistry

Exploring the Properties of Zr2CO2/GaS Van der Waals Heterostructures for Optoelectronic Applications Abstract We investigate the structural, electronic and optical properties of eight possible Zr 2 CO 2 /GaS van der Waals heterostructures using first-principles calculations. These structures display favorable stability, indicated by matching crystal structures and a negative formation energy (-6.57 eV). In all configurations, they act as indirect bandgap semiconductors with a type-II band alignment, allowing eﬀicient electron- hole separation. Optical studies reveal their suitability for optoelectronic applications. Zr 2 CO 2 /GaS under 3% biaxial compressive strain meets the criteria for photocatalytic water splitting, suggesting their potential for electronic and optoelectronic devices in the visible spectrum. Our findings present prospects for advanced photocatalytic materials and optical devices. Heterojunction ; van der Waals force ; Materials science ; Semiconductor ; Optoelectronics ; Band gap ; Crystal (programming language) ; Chemistry ; Molecule MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Hubert Hellwig | Andrzej Nowok | Paulina Peksa | Mateusz Dulski | Robert Musioł | Sebastian Pawlus | P. Kuś
Source: International Journal of Molecular Sciences (article, 2023-12-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecular Dynamics and Near-Tg Phenomena of Cyclic Thioethers This article presents the synthesis and molecular dynamics investigation of three novel cyclic thioethers: 2,3-(4'-methylbenzo)-1,4,7,10-tetrathiacyclododeca-2-ene (compound <b>1</b>), 2,3,14,15-bis(4',4″(5″)-methylbenzo)-1,4,7,10,13,16,19,22,25-octathiacyclotetracosa-2,14-diene (compound <b>2</b>), and 2,3,8,9-bis(4',4″(5″)-methylbenzo)-1,4,7,10-tetrathiacyclododeca-2,8-diene (compound <b>3</b>). The compounds exhibit relatively high glass transition temperatures (<i>T<sub>g</sub></i>), which range between 254 and 283 K. This characteristic positions them within the so-far limited category of crown-like glass-formers. We demonstrate that cyclic thioethers may span both the realms of ordinary and sizeable molecular glass-formers, each featuring distinct physical properties. Furthermore, we show that the <i>T<sub>g</sub></i> follows a sublinear power law as a function of the molar mass within this class of compounds. We also reveal multiple dielectric relaxation processes of the novel cyclic thioethers. Above the <i>T<sub>g</sub></i>, their dielectric loss spectra are dominated by a structural relaxation, which originates from the cooperative reorientation of entire molecules and exhibits an excess wing on its high-frequency slope. This feature has been attributed to the Johari-Goldstein (JG) process. Each investigated compound exhibits also at least one intramolecular secondary non-JG relaxation stemming from conformational changes. Their activation energies range from approximately 19 kJ/mol to roughly 40 kJ/mol. Finally, we analyze the high-pressure molecular dynamics of compound <b>1</b>, revealing a pressure-induced increase in its <i>T<sub>g</sub></i> with a <i>dT<sub>g</sub></i>/<i>dp</i> coefficient equal to 197 ± 8 K/GPa. Glass transition ; Intramolecular force ; Relaxation (psychology) ; Chemistry ; Molecular dynamics ; Aryl ; Dielectric ; Stereochemistry ; Polymer chemistry ; Crystallography ; Computational chemistry ; Materials science ; Alkyl ; Organic chemistry ; Polymer Liquid Crystal Research Advancements ; Photochemistry and Electron Transfer Studies ; Material Dynamics and Properties
Authors: Jie An | Minlong Wang | Jiaman Hou | Chao Wang | Xiaohe Zhang | Ying Du | Qixin Dong | Lijun Wang | Ke Ni | Fazheng Ren
Source: Research Square (preprint, 2023-12-08)
Field: Chemistry — Organic Chemistry

Intramolecular Chalcogen Bonding Activated SuFEx Click Chemistry for Efficient Organic-Inorganic Linking Chalcogen ; Chemistry ; Sulfonyl ; Click chemistry ; Alkyl ; Intramolecular force ; Reactivity (psychology) ; Organic chemistry ; Nucleophile ; Covalent bond ; Polymer chemistry ; Fluoride ; Combinatorial chemistry ; Inorganic chemistry ; Catalysis Click Chemistry and Applications ; Fluorine in Organic Chemistry ; Sulfur-Based Synthesis Techniques
Authors: Yuan An | Jane Greenberg | Alex Kalinowski | Xintong Zhao | Xiaohua Hu | Fernando J. Uribe‐Romo | Kyle Langlois | Jacob Furst | Diego A. Gómez‐Gualdrón
Source: arXiv (Cornell University) (preprint, 2023-09-20)
Field: Materials Science — Materials Chemistry

Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG) Using LLM We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured databases and knowledge extracted from the literature. To enhance MOF-KG accessibility for domain experts, we aim to develop a natural language interface for querying the knowledge graph. We have developed a benchmark comprised of 161 complex questions involving comparison, aggregation, and complicated graph structures. Each question is rephrased in three additional variations, resulting in 644 questions and 161 KG queries. To evaluate the benchmark, we have developed a systematic approach for utilizing the LLM, ChatGPT, to translate natural language questions into formal KG queries. We also apply the approach to the well-known QALD-9 dataset, demonstrating ChatGPT's potential in addressing KGQA issues for different platforms and query languages. The benchmark and the proposed approach aim to stimulate further research and development of user-friendly and efficient interfaces for querying domain-specific materials science knowledge graphs, thereby accelerating the discovery of novel materials. Computer science ; Knowledge graph ; Benchmark (surveying) ; Graph ; Domain (mathematical analysis) ; Question answering ; Domain knowledge ; Graph database ; Natural language understanding ; Knowledge extraction ; Natural language ; Data science ; Information retrieval ; Artificial intelligence ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Lekun Li | Guozhen SHEN
Source: Journal of Inorganic Materials (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

2D MXenes Based Flexible Photodetectors: Progress and Prospects Since the discovery of two-dimensional (2D) transition metal carbides/nitrides (MXenes) in 2011, they have attracted great attention in the fields of energy storage, catalysis, sensors, electromagnetic interference shielding and microwave absorption and so on owing to their special 2D layered structure, excellent electrical conductivity and electrochemical properties.In recent years, with the deep understanding of MXenes, the research related to the optoelectronic properties of MXenes has been widely concerned.Unlike other application fields, optoelectronic devices based on MXenes materials focus on their extension of the semiconductor properties, including tunable band gap of the MXenes via the design of the surface functional groups and layer control, etc., so as to achieve their transformation from metal to semiconductor properties.This paper will revolve around the photoelectric properties of MXenes materials, mainly introduce its application in flexible optoelectronic devices, and then systematically describe the current status and trend of MXenes materials in transparent electronic devices, photodetectors, image sensors, transistors as well as artificial neural vision network systems.The challenges and future development prospects of MXenes -based flexible optoelectronic devices will also be discussed. MXenes ; Materials science ; Photodetector ; Engineering physics ; Optoelectronics ; Nanotechnology ; Systems engineering ; Engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Meng Li | Matthew T. Curnan | Hao Chi | Stephen D. House | Christopher M. Andolina | Jeong Woo Han | Wissam A. Saidi | Götz Veser | Judith C. Yang
Source: Research Square (preprint, 2023-10-03)
Field: Materials Science — Materials Chemistry

Cu/Cu2O interface-modulated two-stage reduction of Cu2O under methanol Reduction (mathematics) ; Stage (stratigraphy) ; Interface (matter) ; Methanol ; Materials science ; Chemistry ; Geology ; Composite material ; Mathematics ; Geometry ; Organic chemistry Copper-based nanomaterials and applications ; Catalytic Processes in Materials Science ; Semiconductor materials and devices
Authors: Manjusha Kulkarni Manjusha Kulkarni
Source: Journal of Science and Technology (article, 2023-07-18)
Field: Chemistry — Analytical Chemistry

UV Assisted Photocatalytic degradation of Azo dye in Textile Effluent by Advanced Oxidation Processes Using TiO2 Nanoparticles In the present investigation the photocatalytic degradation studies of Orange G (azo dye)employing Advanced Oxidation Processes (AOP) in aqueous suspension under 8W low-pressure mercury vapor lamp irradiation is demonstrated.The optimization of various experimental parameters such as effect of catalyst concentration, effect of substrate concentration, effect of addition of H 2 O 2 is thoroughly investigated.Additionally, the obtained results indicates that the employment of AOPs process and selection of the operational parameters led to a complete degradation and substantial dye mineralization supported by the values of the reduction in Chemical Oxygen Demand (COD) and Total Organic Carbon (TOC) of the treated dye solution. Photocatalysis ; Degradation (telecommunications) ; Effluent ; Textile ; Nanoparticle ; Photochemistry ; Chemical engineering ; Chemistry ; Materials science ; Catalysis ; Nanotechnology ; Environmental science ; Organic chemistry ; Environmental engineering ; Computer science ; Composite material ; Engineering Dye analysis and toxicity ; Water and Wastewater Treatment ; Dyeing and Modifying Textile Fibers