jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenke Li | Xiujian Zhao | François‐Xavier Coudert | Chao Liu
Source: ChemRxiv (preprint, 2022-06-10)
Field: Materials Science — Ceramics and Composites

Effect of calcium on the atomic and electronic structures of CdSe QDs embedded soda-lime-silica glasses In this work, we have investigated effects of calcium on the atomic and electronic structures of CdSe QDs by ab initio molecular dynamics. The increase of CaO content in glass is found to break the linkage between Na and O atoms, while interactions between Cd atoms and non-bridging oxygens are not disrupted. The presence of the CdSe QD promotes the migration of Na atoms from the glass matrix to the interface, and even the interior of CdSe QD, while Ca atoms are only found to be present at the QD/glass interface and in glass matrices far from QD. However, on the basis of radial concentration distribution, the CdSe QD only impacts the structure of glass at short-range near the interface. The increase in CaO content is also shown to increase the HOMO-LUMO gap of CdSe QDs embedded glasses. Our findings can extend understanding of the effect of calcium on the structural and luminescence properties of CdSe QDs embedded glasses. Materials science ; Soda-lime glass ; Quantum dot ; Soda lime ; Chemical physics ; Nanotechnology ; Chemistry Glass properties and applications ; Luminescence Properties of Advanced Materials ; Phase-change materials and chalcogenides
Authors: Elnaz Ahani | Mona Motaghed | Mohammad Fereydouni | Tal Kafri | Kristen Dellinger | Christopher L. Kepley | Dean D. Metcalfe
Source: UNC Libraries (article, 2022-04-05)
Field: Chemistry — Organic Chemistry

Harnessing the Anti-Tumor Mediators in Mast Cells as a New Strategy for Adoptive Cell Transfer for Cancer The emergence of cancer immunotherapies utilizing adoptive cell transfer (ACT) continues to be one of the most promising strategies for cancer treatment. Mast cells (MCs) which occur throughout vascularized tissues, are most commonly associated with Type I hypersensitivity, bind immunoglobin E (IgE) with high affinity, produce anti-cancer mediators such as tumor necrosis factor alpha (TNF-α) and granulocyte macrophage colony-stimulating factor (GM-CSF), and generally populate the tumor microenvironments. Yet, the role of MCs in cancer pathologies remains controversial with evidence for both anti-tumor and pro-tumor effects. Here, we review the studies examining the role of MCs in multiple forms of cancer, provide an alternative, MC-based hypothesis underlying the mechanism of therapeutic tumor IgE efficacy in clinical trials, and propose a novel strategy for using tumor-targeted, IgE-sensitized MCs as a platform for developing new cellular cancer immunotherapies. This autologous MC cancer immunotherapy could have several advantages over current cell-based cancer immunotherapies and provide new mechanistic strategies for cancer therapeutics alone or in combination with current approaches. Adoptive cell transfer ; Mast cell ; Tumor cells ; Cancer research ; Biology ; Immunology ; Immune system ; T cell Click Chemistry and Applications ; Mast cells and histamine ; CRISPR and Genetic Engineering
Authors: Takahiro Seki
Source: Vacuum and Surface Science (article, 2022-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polymers, Liquid Crystals and Interfaces―Expectations for Data-driven Approaches This article introduces two topics on surface mediated photoinduced effects : photoalignment control of liquid crystals (LCs) and mass transport process in azobenzene-containing polymers. Due to a large cooperative behavior of LCs, the photoisomerization of azobenzene on a substrate surface (command layer) can control the alignment of a large number of LC molecules. Recent work has revealed that the command layer can be also set at the free surface for LC polymer films. On the other hand, the photoinduced mass transport is also a subject of extensive study in azobenzene films. We have recently demonstrated the crucial role of the free surface in the mass transport process. It is expected that optimization of the systems and elucidation of unresolved issues for such soft-interfaces can be made through the data-driven approaches. Azobenzene ; Photoisomerization ; Polymer ; Materials science ; Liquid crystal ; Layer (electronics) ; Molecule ; Mass transport ; Nanotechnology ; Substrate (aquarium) ; Surface (topology) ; Chemical physics ; Isomerization ; Optoelectronics ; Chemistry ; Composite material ; Organic chemistry ; Physics ; Engineering physics ; Catalysis Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Electrowetting and Microfluidic Technologies
Authors: Daniel M. Packwood | Yu Kaneko | Daiji Ikeda | Mitsuru Ohno
Source: arXiv (Cornell University) (preprint, 2022-04-21)
Field: Materials Science — Materials Chemistry

Data-Driven Design of a New Organic Semiconductor via an Electronic Structure Chart Data-driven methodologies for designing new materials are developing apace, yet advances for organic crystals have been infrequent. For organic crystals, the need to predict solid-state electronic properties from molecular structure alone is an exceedingly difficult task for typical, regression-based design strategies. In this paper, we present a new strategy for designing organic crystals which circumvents the need to regress solid-state physical properties directly. At the core of this strategy is an electronic structure chart, a two-dimensional projection of an organic crystal database in which each material is positioned according to its solid-state electronic properties. We illustrate this strategy by identifying a new molecule which is predicted to show a targeted band gap and better-than-average band curvatures in the crystalline state. This strategy is the first data-driven method which can design new molecules on the basis of genuine solid-state electronic properties, and has potential to accelerate breakthroughs in the field of organic electronics and beyond. Chart ; Organic semiconductor ; Electronics ; Solid-state ; Electronic structure ; Computer science ; Projection (relational algebra) ; Field (mathematics) ; Organic electronics ; Basis (linear algebra) ; Band gap ; Nanotechnology ; Materials science ; Engineering physics ; Algorithm ; Chemistry ; Computational chemistry ; Engineering ; Mathematics ; Electrical engineering ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Nawal Taoufik | Fatima Zahra Janani | Habiba Khiar | M. Sadiq | Mohamed Abdennouri | Mika Sillanpää | Mounia Achak | Noureddine Barka
Source: Research Square (preprint, 2022-08-18)
Field: Materials Science — Materials Chemistry

MgO-La2O3 mixed metal oxides heterostructure catalysts for photodegradation of dyes pollutant: synthesis, characterization and artificial intelligence modelling Photodegradation ; Characterization (materials science) ; Catalysis ; Pollutant ; Materials science ; Metal ; Heterojunction ; Chemical engineering ; Photocatalysis ; Nanotechnology ; Chemistry ; Metallurgy ; Engineering ; Organic chemistry ; Optoelectronics Magnesium Oxide Properties and Applications ; Pigment Synthesis and Properties
Authors: Atreyee Banerjee | Hsiao‐Ping Hsu | Kurt Kremer | Oleksandra Kukharenko
Source: arXiv (Cornell University) (preprint, 2022-11-25)
Field: Materials Science — Materials Chemistry

Data-driven identification and analysis of the glass transition in polymer melts Understanding the nature of glass transition, as well as precise estimation of the glass transition temperature for polymeric materials, remain open questions in both experimental and theoretical polymer sciences. We propose a data-driven approach, which utilizes the high-resolution details accessible through the molecular dynamics simulation and considers the structural information of individual chains. It clearly identifies the glass transition temperature of polymer melts of weakly semiflexible chains. By combining principal component analysis and clustering, we identify the glass transition temperature in the asymptotic limit even from relatively short-time trajectories, which just reach into the Rouse-like monomer displacement regime. We demonstrate that fluctuations captured by the principal component analysis reflect the change in a chain's behaviour: from conformational rearrangement above to small rearrangements below the glass transition temperature. Our approach is straightforward to apply, and should be applicable to other polymeric glass-forming liquids. Glass transition ; Polymer ; Principal component analysis ; Chain (unit) ; Materials science ; Component (thermodynamics) ; Chemical physics ; Molecular dynamics ; Statistical physics ; Monomer ; Displacement (psychology) ; Thermodynamics ; Chemistry ; Physics ; Computer science ; Computational chemistry ; Composite material Material Dynamics and Properties ; Data Visualization and Analytics ; Theoretical and Computational Physics
Authors: Toshinori Shimanouchi | Masatoshi Fujii
Source: Vacuum and Surface Science (article, 2022-01-09)
Field: Materials Science — Materials Chemistry

Possible Development on Data-driven Research for Soft Interfacial Materials Recent development of soft materials for medical, pharmaceutical, and bio-inspired devices has produced and accumulated enormous substances concerning spectra and images of target materials. An effective design and development on these fields would require the utilization of scientific assets. Data-driven approach would play the important role to give the novel design strategy of soft interfacial materials. Nanotechnology ; Soft materials ; Development (topology) ; Computer science ; Materials science ; Biochemical engineering ; Engineering Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Svetoslav Nikolov | Julien Tranchida | Aidan P. Thompson | Mitchell Wood
Source:  (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Data-driven Magnetic Materials Modeling; Advances in Classical Molecular Dynamics. Abstract not provided. Molecular dynamics ; Computer science ; Dynamics (music) ; Statistical physics ; Biological system ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; NMR spectroscopy and applications
Authors: Marc Duquesnoy | Teo Lombardo | Fernando Caro | Florent Haudiquez | Alain C. Ngandjong | Jiahui Xu | Hassan Oularbi | Alejandro A. Franco
Source: arXiv (Cornell University) (preprint, 2022-01-12)
Field: Materials Science — Materials Chemistry

Functional Data-Driven Framework for Fast Forecasting of Electrode Slurry Rheology Simulated by Molecular Dynamics Computational modeling of the manufacturing process of Lithium-Ion Battery (LIB) composite electrodes based on mechanistic approaches, allows predicting the influence of manufacturing parameters on electrode properties. However, ensuring that the calculated properties match well with experimental data, is typically time and resources consuming In this work, we tackled this issue by proposing a functional data-driven framework combining Functional Principal Component Analysis and K-Nearest Neighbors algorithms. This aims first to recover the early numerical values of a mechanistic electrode manufacturing simulation to predict if the observable being calculated is prone to match or not, \textit{i.e} screening step. In a second step it recovers additional numerical values of the ongoing mechanistic simulation iterations to predict the mechanistic simulation result, \textit{i.e} forecasting step. We demonstrated this approach in context of LIB manufacturing through non-equilibrium molecular dynamics (NEMD) simulations, aiming to capture the rheological behavior of electrode slurries. We discuss in full details our novel methodology and we report that the expected mechanistic simulation results can be obtained 11 times faster with respect to running the complete mechanistic simulation, while being accurate enough from an experimental point of view, with a $F1_{score}$ equals to 0.90, and a $R^2_{score}$ equals to 0.96 for the learnings validation. This paves the way towards a powerful tool to drastically reduce the utilization of computational resources while running mechanistic simulations of battery manufacturing electrodes. Context (archaeology) ; Computer science ; Molecular dynamics ; Process (computing) ; Battery (electricity) ; Electrode ; Slurry ; Rheology ; Work (physics) ; Simulation ; Mechanical engineering ; Materials science ; Chemistry ; Physics ; Engineering ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fault Detection and Control Systems
Authors: Adolphus Lye | Nawal Prinja | Edoardo Patelli
Source:  (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Probabilistic Artificial Intelligence Prediction of Material Properties for Nuclear Reactor Designs Probabilistic logic ; Computer science ; Artificial intelligence ; Reliability engineering ; Machine learning ; Engineering Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Nuclear Materials and Properties
Authors: Pritom J. Bora | Bibhusita Mahanta | K Kishore | Praveen C. Ramamurthy
Source:  (article, 2022-12-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of Bi-Layered Strong Microwave Absorber Based on Polymer-Fly Ash Cenosphere Composite With A Data-Driven Approach In this work, a strong microwave absorption characteristic viz., minimum reflection loss (RL) -69.5 dB (8.2-12.4 GHz absorption bandwidth i.e., RL< -10 dB) is evaluated for the bi-layered polyvinyl butyral (PVB)-polyaniline (PANI) coated fly ash cenosphere (FAC) composite. Materials data-driven approach is used to model and optimize the associated parameters (based on an experimental evaluation), and hence to realize the best RL. The outstanding RL performance is achieved for the 10 wt% PANI-FAC loaded PVB (top layer, thickness 1.5 mm) and 6 wt% PANI-FAC loaded PVB (a bottom layer, thickness 2 mm). Polyvinyl butyral ; Cenosphere ; Materials science ; Composite number ; Reflection loss ; Composite material ; Fly ash ; Microwave ; Polyaniline ; Absorption (acoustics) ; Polymer ; Layer (electronics) ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Volodymyr Bezverkhniy
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Искусственный Интеллект, Физическое Обоснование Химической Связи и Синтез Новых Веществ. (Artificial Intelligence, Physical Substantiation of the Chemical Bond and Synthesis of New Substances) Bond ; Biochemical engineering ; Chemistry ; Nanotechnology ; Psychology ; Engineering ; Materials science ; Business History and advancements in chemistry ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Evan Walter Clark Spotte‐Smith | Samuel M. Blau | Kristin A. Persson
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

GPS for the SEI: Charting Electrochemical Mechanisms with Reaction Networks An electrolyte is placed under an applied potential - what happens next? This question of electrochemical reactivity seems simple on its surface. However, the answer of what intermediates, products, and byproducts will form, as well as how they will form, is immensely challenging to predict. Traditional theoretical methods have either used intuitively guided mechanistic calculations (e.g. density functional theory) that abstract away competition between reactions or dynamical simulations (e.g. ab initio molecular dynamics) that are computationally intractable beyond extremely short time scales. Recently, we have taken a new approach: using data-driven reaction networks to automatically explore electrochemical reactive landscapes. In this talk, we will present an end-to-end methodology to answer questions of reactivity in complex environments. Drawing from recent studies of solid electrolyte interphase (SEI) formation in lithium-ion batteries, we will describe how to generate and analyze reaction networks, leading to insights regarding both formation mechanisms of experimentally identified products as well as the prediction of novel SEI components. We will then illustrate how individual mechanisms obtained via reaction networks can be combined to understand reactive competition using kinetic Monte Carlo simulations. The methodology that we describe has potential not just to expand our understanding of battery chemistry, but of electrochemical reactivity in general. Reactivity (psychology) ; Electrochemistry ; Electrolyte ; Chemistry ; Battery (electricity) ; Computer science ; Ab initio ; Monte Carlo method ; Computational chemistry ; Biochemical engineering ; Physics ; Thermodynamics ; Electrode ; Physical chemistry ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Nataliya Lopanitsyna | Guillaume Fraux | Maximilian A. Springer | Sandip De | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2022-12-26)
Field: Materials Science — Materials Chemistry

Modeling high-entropy transition-metal alloys with alchemical compression Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys, and is remarkably stable when extrapolating to structures outside its training set. We use this framework to study element segregation in a computational experiment that simulates an equimolar alloy of all 25 elements, mimicking the seminal experiments by Cantor et al., and use our observations on the short-range order relations between the elements to define a data-driven set of Hume-Rothery rules that can serve as guidance for alloy design. We conclude with a study of three prototypical alloys, CoCrFeMnNi, CoCrFeMoNi and IrPdPtRhRu, determining their stability and the short-range order behavior of their constituents. High entropy alloys ; Alloy ; Entropy (arrow of time) ; Computer science ; Thermodynamics ; Statistical physics ; Materials science ; Metallurgy ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Entropy Alloys Studies
Authors: Brian Lenhart | Michael K. Zuraw | William E. Mustain
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Chemistry — Electrochemistry

Statistical Approach to Design Zn Particle Size, Shape, and Crystallinity for Alkaline Batteries The modern alkaline primary battery is composed of a highly alkaline, gelled electrolyte-zinc powder anode dispersion (slurry), a cellulose-based separator, and an electrolytic manganese dioxide (EMD) cathode pellet – typically arranged in a cylindrical cell geometry. This battery system has its origins in the 1950s and thus has had decades to be researched and matured and it is known that the limiting performance of this battery system is concentrated at the slurried zinc anode. This had led many researchers to focus on the anode for improvement with most previous engineering efforts targeting enhanced performance, capacity, and stability of this material. Due to the various material fabrication processes involved with manufacturing industrial zinc powder, there exists an array of unique zinc particle size, shape, and crystallinity combinations that distinguish industrial zinc powders from one another. These industrial zinc powders are typically produced through trial-and-error processes using existing “rules of thumb”. Consequentially, it is possible that something has been missed and a data-driven approach could elucidate the optimum combination of zinc particle properties. In this study, we investigate the effect of Zn particle size, shape, and degree of crystallinity on the achievable capacity and corrosion current. Various types of zinc powders were sieved into size fraction ranges and chemically and thermo-chemically treated to control their crystallinity. The procedure for producing zinc powders of various sizes, shapes, and crystallinities led to a 64-powder Zn matrix that was then electrochemically analyzed by linear sweep voltammetry to determine the corrosion current and galvanostatically discharged to determine the achievable capacity. These experiments were done in a newly-designed, custom-built electroanalytical cell that will be described in the talk. Following the data collection, a 4 k factorial statistical analysis was performed. This analysis identified which of the controlling variables have a statistically significant influence on the achievable capacity and corrosion. The statistical model was also able to provide guidance regarding the preferred particle shape. This information can be used to develop actionable procedures for battery manufacturers to create cells that are more stable, longer lasting, and have higher energy densities. Crystallinity ; Zinc ; Materials science ; Alkaline battery ; Linear sweep voltammetry ; Anode ; Electrolyte ; Particle size ; Separator (oil production) ; Limiting current ; Corrosion ; Metallurgy ; Chemical engineering ; Battery (electricity) ; Composite material ; Electrochemistry ; Electrode ; Cyclic voltammetry ; Chemistry ; Engineering Electrochemical Analysis and Applications ; Conducting polymers and applications ; Advanced Battery Technologies Research
Authors: Cibrán López | Agustí Emperador | Edgardo Saucedo | Riccardo Rurali | Claudio Cazorla
Source: arXiv (Cornell University) (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

Universal ion-transport descriptors and classes of inorganic solid-state electrolytes Solid-state electrolytes (SSE) with high ion conductivity are pivotal for the development and large-scale adoption of green-energy conversion and storage technologies such as fuel cells, electrocatalysts and solid-state batteries. Yet, SSE are extremely complex materials for which general rational design principles remain indeterminate. Here, we unite first-principles materials modelling, computational power and modern data analysis techniques to advance towards the solution of such a fundamental and technologically pressing problem. Our data-driven survey reveals that the correlations between ion diffusivity and other materials descriptors in general are monotonic, although not necessarily linear, and largest when the latter are of vibrational nature and explicitly incorporate anharmonic effects. Surprisingly, principal component and k-means clustering analysis show that elastic and vibrational descriptors, rather than the usual ones related to chemical composition and ion mobility, are best suited for reducing the high complexity of SSE and classifying them into universal classes. Our findings highlight the need of considering databases that incorporate temperature effects to improve our understanding of SSE and point towards a generalized approach to the design of energy materials. Anharmonicity ; Computer science ; Electrolyte ; Component (thermodynamics) ; Monotonic function ; Thermal diffusivity ; Ion ; Materials science ; Statistical physics ; Thermodynamics ; Chemistry ; Mathematics ; Physics ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Solid-state spectroscopy and crystallography