jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifan Guo | Junhua Su | Tongxin Bian | Jing Yan | Longkun Que | Hunan Jiang | Jinlong Xie | Ying Li | Yong Wang | Zuowan Zhou | Ying Ma | Jinchuan Liu | Yinhe Lin | Yulong Jia | M Karplus | W Miller | E Neher | H Schwarz | J Simons | G Somorjai | R Zare | B Albinsson | J Janek | H Kondoh | A Krylov | 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 | Jiang-Wei Wu | Yao-Ping Xie | * Mei-Yi | Shu-Hui Yao | Yi Guan | Rong-Jian Zhao | Lu Pan | Zhi-Pan Wu | ; Liu | M Bafekry | M Fadlallah | A Faraji | H Shafique | I Jappor | Y Sarsari | M Ang | Ghergherehchii | Suman Chowdhury | Fazel Shojaei | Bohayra Mortazavi | ; Chowdhury | F Shojaei | B Mortazavi
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, 8225 DOI: 10.1039/D3CP90079G 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 ; Electronic and Structural Properties of Oxides
Authors: Zuhao Shi | Bin Liu | Yuanzheng Yue | Arramel Arramel | Neng Li
Source: arXiv (Cornell University) (preprint, 2023-09-22)
Field: Chemistry — Inorganic Chemistry

Unraveling Medium-Range Order and Melting Mechanism of ZIF-4 under High Temperature Glass formation in Zeolitic Imidazolate Frameworks (ZIFs) has garnered significant attention in the field of Metal-Organic Frameworks (MOFs) in recent years. Numerous works have been conducted to investigate the microscopic mechanisms involved in the melting-quenching process of ZIFs. Understanding the density variations that occur during the melting process of ZIFs is crucial for comprehending the origins of glass formation. However, conducting large-scale simulations has been challenging due to limitations in computational resources. In this work, we utilized deep learning methods to accurately construct a potential function that describes the atomic-scale melting behavior of Zeolitic Imidazolate Framework-4 (ZIF-4). The results revealed the spatial heterogeneity associated with the formation of low-density phases during the melting process of ZIF-4. This work discusses the advantages and limitations of applying deep learning simulation methods to complex structures like ZIFs, providing valuable insights for the development of machine learning approaches in designing Metal-Organic Framework glasses. Zeolitic imidazolate framework ; Quenching (fluorescence) ; Materials science ; Metal-organic framework ; Nanotechnology ; Process (computing) ; Function (biology) ; Work (physics) ; Glass melting ; Computer science ; Chemistry ; Mechanical engineering ; Physics ; Engineering ; Metallurgy ; Physical chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Pigment Synthesis and Properties ; X-ray Diffraction in Crystallography
Source: Angewandte Chemie International Edition (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 24/2023 In their Research Article (e202301408), Zhao-Qing Liu et al. report the activation of lattice oxygen in spinel ZnCo 2 O 4 through filling oxygen vacancies with fluorine,t hus resulting in ahighly active catalyst for electrocatalytic oxygen evolution. Spinel ; Oxygen ; Fluorine ; Catalysis ; Oxygen evolution ; INT ; Oxygen atom ; Chemistry ; Materials science ; Chemical engineering ; Physical chemistry ; Computer science ; Metallurgy ; Engineering ; Electrochemistry ; Molecule ; Organic chemistry ; Electrode ; Operating system Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection
Authors: Greta Chiaravalli | Tiziana Ravasenga | Elisabetta Colombo | Simona Francia | Stefano Marco | Riccardo Sacco | Grazia Pertile | Fabio Benfenati | Guglielmo Lanzani | Xiao Huang | Yuuichi Orimoto | Yuriko Aoki | Li Tang | Qikai Han | Bin Wang | Zhonghua Yang | Chunyuan Song | Guanyu Feng | Shuxin Wang | Marija Cvetnic | Robert plajt | Edi Topic | Mirta Rubc | Haojiang Weng | Junfeng Yao | * Wang | Guohui Li | Madeleine Leger | Jiangfeng Guo | Bryce Macmillan | M Hatem | Tomislav Titi | Fris c ic | Bruce Balcom | * | Barry A ; Li | Shenbing Wu | Changjun Hu | Yuanyuan Liu
Source: Physical Chemistry Chemical Physics (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2024, 26, 3 DOI: 10.1039/D4CP90003K 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 ; Electronic and Structural Properties of Oxides
Authors: Husain Adamji | Aditya Nandy | Ilia Kevlishvili | Yuriy Román‐Leshkov | Heather J. Kulik
Source: ChemRxiv (preprint, 2023-03-31)
Field: Chemistry — Inorganic Chemistry

Computational Discovery of Stable Metal-Organic Frameworks for Methane-to-Methanol Catalysis The challenge of direct partial oxidation of methane to methanol has motivated the targeted search of metal-organic frameworks (MOFs) as a promising class of materials for this transformation because of their site-isolated metals with tunable ligand environments. Thousands of MOFs have been synthesized, yet relatively few have been screened for their promise in methane conversion. We developed a high-throughput virtual screening (HTVS) workflow that identifies MOFs from a diverse space of experimental MOFs that have not been studied for catalysis, yet are thermally stable, synthesizable, and have promising unsaturated metal sites for C–H activation via a terminal metal-oxo species. We carried out density functional theory (DFT) calculations on the radical rebound mechanism for methane to methanol conversion on models of the secondary building units (SBUs) from 87 selected MOFs. While we showed that oxo formation favorability decreases with increasing 3d filling, consistent with prior work, previously observed scaling relations between oxo formation and hydrogen atom transfer (HAT) are disrupted by the greater diversity in our MOF set. Accordingly, we focused on Mn MOFs, which favor oxo intermediates without disfavoring HAT or leading to high methanol release energies—a key feature for methane hydroxylation activity. We identified three Mn MOFs comprising unsaturated Mn centers bound to weak-field carboxylate ligands in planar or bent geometries with promising methane to methanol kinetics and thermodynamics. The energetic spans of these MOFs are indicative of promising turnover frequencies for methane to methanol that warrant further experimental catalytic studies. Methanol ; Catalysis ; Methane ; Chemistry ; Metal-organic framework ; SBus ; Ligand (biochemistry) ; Nanomaterial-based catalyst ; Catalytic cycle ; Formate ; Anaerobic oxidation of methane ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Catalytic Processes in Materials Science
Authors: Dibakar Datta
Source: arXiv (Cornell University) (preprint, 2023-09-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electro-Chemo-Mechanical Modeling of Multiscale Active Materials for Next-Generation Energy Storage: Opportunities and Challenges The recent geopolitical crisis resulted in a gas price surge. Although lithium-ion batteries represent the best available rechargeable battery technology, a significant energy and power density gap exists between LIBs and petrol/gasoline. The battery electrodes comprise a mixture of active materials particles, conductive carbon, and binder additives deposited onto a current collector. Although this basic design has persisted for decades, the active material particle's desired size scale is debated. Traditionally, microparticles have been used in batteries. Advances in nanotechnology have spurred interest in deploying nanoparticles as active materials. However, despite many efforts in nano, industries still primarily use 'old' microparticles. Most importantly, the battery industry is unlikely to replace microstructures with nanometer-sized analogs. This poses an important question: Is there a place for nanostructure in battery design due to irreplaceable microstructure? The way forward lies in multiscale active materials, microscale structures with built-in nanoscale features, such as microparticles assembled from nanoscale building blocks or patterned with engineered or natural nanopores. Although experimental strides have been made in developing such materials, computational progress in this domain remains limited and, in some cases, negligible. However, the fields hold immense computational potential, presenting a multitude of opportunities. This perspective highlights the existing gaps in modeling multiscale active materials and delineates various open challenges in the realm of electro-chemo-mechanical modeling. By doing so, it aims to inspire computational research within this field and promote synergistic collaborative efforts between computational and experimental researchers. Microscale chemistry ; Nanotechnology ; Battery (electricity) ; Materials science ; Supercapacitor ; Power (physics) ; Chemistry ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
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, 13797 DOI: 10.1039/D3CP90112B 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 ; Advanced Photocatalysis Techniques
Authors: X. -J. Zhang | Wei, Jiahao | Hui Jia | Yandi Liu | Li, Guo | Liu, Ling | Yulong Wu | Changlong Liu | Zhang, Xiao-Dong | Yonghui Li
Source: arXiv (Cornell University) (preprint, 2023-05-13)
Field: Materials Science — Materials Chemistry

A Method to Decipher "Genome" from Interatomic Cohesion in the Exploration for a "Central Dogma" Replacement in Material Science In the ball-stick model, interatomic cohesions are considered "sticks". But enormous details and features of the "sticks" are usually oversimplified as indexed quantities or equivocated as geometry characteristics. These indexed quantities or geometry characteristics not only limit the explanatory capability to a few chemical/physical aspects but also eliminate generativity for expected resemblance. And these limitations can be related to the information loss during the conversion. Herein, inspired by the central dogma, a framework is introduced to compact interatomic cohesions into a detailed residue-by-residue "genome" with matched encoding/decoding tools. The framework fuses the quantum mechanical aspects, auto feature extraction, nanostructures and/or simulations, and generative models. As a proof of concept, the realization introduced in this work adopted bosonic/fermionic features, an autoencoder with image recognition processes, Density Functional Theory simulations, and a thiolate-protected gold nanocluster dataset. After repetitive modeling, validating, and analysis based on 26,528 simulated interatomic images, the interatomic cohesion can be almost losslessly encoded into an 8-value-genome, and the genome encoder-decoder pair is also obtained. The model is then automatically extended into a generative model which converts any arbitrary 8-value-genome to a bond image. Nanoclusters ; Bond ; Computer science ; Artificial intelligence ; Function (biology) ; Bond length ; Materials science ; Nanotechnology ; Physics ; Molecule ; Quantum mechanics Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: William C. Witt | van der Oord | Elena Gelžinytė | Teemu Järvinen | Andres Ross | James P. Darby | Cheuk Hin Ho | William J. Baldwin | Matthias Sachs | James R. Kermode | Noam Bernstein | Gábor Cśanyi | Christoph Ortner
Source: Lancaster EPrints (Lancaster University) (preprint, 2023-09-06)
Field: Materials Science — Materials Chemistry

ACEpotentials.jl: A Julia Implementation of the Atomic Cluster Expansion We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion (Drautz, 2019). As the latter provides a complete description of atomic environments, including invariance to overall translation and rotation as well as permutation of like atoms, the resulting potentials are systematically improvable and data efficient. Furthermore, the descriptor's expressiveness enables use of a linear model, facilitating rapid evaluation and straightforward application of Bayesian techniques for active learning. We summarize the capabilities of ACEpotentials.jl and demonstrate its strengths (simplicity, interpretability, robustness, performance) on a selection of prototypical atomistic modelling workflows. Interpretability ; Robustness (evolution) ; Computer science ; Workflow ; Translation (biology) ; Software ; Simplicity ; Permutation (music) ; Quantum ; Theoretical computer science ; Algorithm ; Computational science ; Artificial intelligence ; Physics ; Quantum mechanics ; Programming language ; Database ; Chemistry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Peichen Zhong | Fengyu Xie | Luis Barroso-Luque | Liliang Huang | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2023-07-07)
Field: Materials Science — Materials Chemistry

Modeling intercalation chemistry with multi-redox reactions by sparse lattice models in disordered rocksalt cathodes Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercalation voltage profile is a critical parameter to evaluate the performance of energy storage. The application of commonly used cluster expansion techniques to model the intercalation thermodynamics of such systems from \textit{ab-initio} is challenged by the combinatorial increase in configurational degrees of freedom as the number of species grows. Such challenges necessitate efficient generation of lattice models without over-fitting and proper sampling of the configurational space under charge balance in ionic systems. In this work, we introduce a combined approach that addresses these challenges by (1) constructing a robust cluster-expansion Hamiltonian using the sparse regression technique, including $\ell_0\ell_2$-norm regularization and structural hierarchy; and (2) implementing semigrand-canonical Monte Carlo to sample charge-balanced ionic configurations using the table-exchange method and an ensemble-average approach. These techniques are applied to a disordered rocksalt oxyfluoride Li$_{1.3-x}$Mn$_{0.4}$Nb$_{0.3}$O$_{1.6}$F$_{0.4}$ (LMNOF) which is part of a family of promising earth-abundant cathode materials. The simulated voltage profile is found to be in good agreement with experimental data and particularly provides a clear demonstration of the Mn and oxygen contribution to the redox potential as a function of Li content. Cluster expansion ; Intercalation (chemistry) ; Ionic bonding ; Lattice (music) ; Statistical physics ; Monte Carlo method ; Hamiltonian (control theory) ; Chemistry ; Thermodynamics ; Physics ; Materials science ; Ion ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: M. Hussein N. Assadi | Esmail Doustkhah
Source: ACS Engineering Au (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Transforming Cl-Containing Waste Plastics into Carbon Resource for Steelmaking: Theoretical Insight The accumulation of waste plastics poses a significant environmental challenge, leading to persistent pollution in terrestrial and aquatic ecosystems. A practical approach to address this issue involves the transformation of postconsumer waste plastics into industrially valuable products. This study focuses on an example of harnessing the carbon content in these polymers for carbon-demanding industrial processes, thereby reducing waste plastics from the environment and alleviating the demand for mined carbon resources. Employing quantum simulations, we examine the viability of polychloroprene as a carburizing agent in the steelmaking process. Our simulations reveal that polychloroprene exhibits excellent carbon diffusivity in molten iron, with a theoretical diffusion coefficient of 8.983 × 10<sup>-5</sup>cm<sup>2</sup> s<sup>-1</sup>. This value competes favorably with that of metallurgical coke and surpasses the carbon diffusivity of other polymers, such as polycarbonate, polyurethane, and polysulfide. Additionally, our findings demonstrate that the chlorine content in polychloroprene does not permeate into molten iron but instead remains confined to the molten iron and slag interface. Materials science ; Carbon fibers ; Coke ; Waste management ; Carburizing ; Steelmaking ; Municipal solid waste ; Environmental science ; Metallurgy ; Composite material ; Engineering Thermal and Kinetic Analysis ; Polymer crystallization and properties ; Recycling and Waste Management Techniques
Authors: Shuang Han | Steen Lysgaard | Tejs Vegge | Heine Anton Hansen
Source: ChemRxiv (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

Rapid mapping of alloy surface phase diagrams via Bayesian evolutionary multitasking Many global optimization tasks have partially or completely shared search spaces. This is particularly common in computational surface science, where surfaces are optimized to their lowest-energy configurations under various idealized chemical conditions to construct surface phase diagrams. If each task represents a global optimization of the surface system under a unique reaction condition, all tasks essentially shares the same configurational search space as well as the same black-box function that takes as input the configuration and returns the electronic energy of the relaxed structure. However, the configurational search space can be huge for multi-component systems such as alloys, while each energy evaluation also requires an expensive electronic structure calculation. Bayesian optimization represents an efficient paradigm for optimizing an expensive black-box function. Evolutionary multitasking is a newly emerging paradigm for solving multiple global optimization problems simultaneously. Here we present a novel Bayesian evolutionary multitasking (BEM) framework combining the best of the two. As a case study, we have used our method to derive surface phase diagrams of Pt-Ni alloy catalysts under a wide range of reaction conditions for steam methane reforming. Integrating knowledge such as graph theory, lattice symmetry and active learning, the BEM framework as a whole can significantly accelerate the mapping of surface phase diagrams for very complex heterogeneous systems. Bayesian optimization ; Human multitasking ; Computer science ; Evolutionary algorithm ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Jun Meng | Md Sariful Sheikh | Ryan Jacobs | Jian Liu | William O. Nachlas | Xiangguo Li | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2023-08-26)
Field: Materials Science — Materials Chemistry

Computational Discovery of Fast Interstitial Oxygen Conductors New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, e.g., below 400 Celsius. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here, we combine physically-motivated structure and property descriptors, ab initio simulations, and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La4Mn5Si4O22+d (LMS), a representative member of perrierite/chevkinite family. We found LMS has higher oxygen ionic conductivity than the widely used yttria-stabilized ZrO2, and among the highest surface oxygen exchange rates at intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. This work developed and demonstrated a powerful approach for discovering new families of interstitial oxygen conductors and suggests many more such materials remain to be discovered. Electrical conductor ; Oxygen ; Materials science ; Chemical physics ; Diffusion ; Conductivity ; Interstitial defect ; Chemistry ; Thermodynamics ; Optoelectronics ; Physical chemistry ; Composite material ; Physics Advancements in Solid Oxide Fuel Cells ; Nuclear Materials and Properties ; Catalytic Processes in Materials Science