jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yaowen Liu | Martin C. Dietl | Robin Heckershoff | Chunyu Han | Hongwei Shi | Matthias Rudolph | Frank Röminger | Isabella Caligiuri | Kanwal Asif | Muhammad Adeel | Thomas Scattolin | A. Stephen K. Hashmi
Source: Angewandte Chemie (article, 2023-05-19)
Field: Chemistry — Organic Chemistry

Goldkatalysierte formale [4+2]‐Cycloaddition als Zugang zu antitumoraktiven spirozyklischen Oxindolen aus Alkinen und von Isatin abgeleiteten Ketiminen Abstract Aufgrund seines hervorragenden Bioaktivitätsprofils, das zunehmend in der pharmazeutischen und synthetischen Chemie genutzt wird, ist Spirooxindol ein wichtiger Grundbaustein. Wir beschreiben hier eine effiziente Methode zum Aufbau hochfunktionalisierter neuer Spirooxindolocarbamate über eine Gold‐katalysierte Cycloadditionsreaktion von terminalen Alkinen oder Inamiden mit von Isatin abgeleiteten Ketiminen. Dieses Protokoll weist eine gute Kompatibilität mit funktionellen Gruppen auf, verwendet leicht verfügbare Ausgangsstoffe, milde Reaktionsbedingungen, geringe Katalysatorbeladungen und keine Additive. Es ermöglicht die Umwandlung verschiedener funktionalisierter Alkingruppen in zyklische Carbamate. Es gelang eine Synthese im Grammmaßstab und DFT‐Berechnungen bestätigen die Machbarkeit des mechanistischen Ansatzes. Einige der Zielprodukte zeigen eine gute bis ausgezeichnete antiproliferative Aktivität auf menschlichen Tumorzelllinien. Darüber hinaus zeigte eine der aktivsten Verbindungen eine bemerkenswerte Selektivität für Tumorzellen gegenüber normalen Zellen. Chemistry ; Stereochemistry Catalytic Alkyne Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Yang Gao | Phil S. Baran
Source: ChemRxiv (preprint, 2023-09-13)
Field: Chemistry — Organic Chemistry

Ni-Catalyzed Enantioselective Decarboxylative Acylation: Rapid, Modular Access to a-Amino Ketones A new approach to the enantiocontrolled synthesis of alpha-amino ketone derivatives is disclosed by employing a decarboxylative acylation strategy. Thus, when an acyl chloride and an alpha-amido-containing redox-active ester are exposed to Ni-catalysis, a chiral ligand, and metal reductant, alpha-amido ketones are produced in good yield and high ee. The reaction exhibits broad substrate scope, can be easily scaled up, and is applied to dramatically simplify the synthesis of several known structures. Acylation ; Enantioselective synthesis ; Ketone ; Catalysis ; Chemistry ; Ligand (biochemistry) ; Yield (engineering) ; Substrate (aquarium) ; Combinatorial chemistry ; Organic chemistry ; Materials science ; Receptor Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Synthetic Organic Chemistry Methods
Authors: Ziyuan Wang
Source:  (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

Application of Artificial Intelligence in New Energy Materials As global energy demand continues to rise and environmental concerns become more pressing, the exploration and advancement of innovative energy materials have emerged as a paramount subject in contemporary societies. AI, being a cutting-edge technology, is displaying immense potential and future applications across diverse domains. The increasing popularity of artificial intelligence technology has caught the attention of many in this particular setting. AI has enormous potential when it comes to studying new energy materials and environmental conservation. As AI continues to advance, it is revealing immense potential in the realm of new energy materials, driven by the expanding need for sustainable energy in society, amidst the rapid progress of science and technology. AI makes contributions to sustainable energy encompass more than just expediting the exploration and creation of fresh energy materials; it also enhances their efficacy while mitigating expenses, expediting the realization of sustainable energy. Expediting ; Popularity ; Energy (signal processing) ; Energy consumption ; Computer science ; Energy conservation ; Engineering ; Systems engineering ; Political science Machine Learning in Materials Science ; Metal Extraction and Bioleaching
Authors: Guangshuai Han | Yixuan Sun | Yining Feng | Guang Lin | Na Lu
Source: Advanced Electronic Materials (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Guided Thermoelectric Materials Design and Discovery (Adv. Electron. Mater. 8/2023) Artificial Intelligence With the development of artificial intelligence, many industries have undergone a technological revolution, and the field of materials science is no exception. In article number 2300042, Guang Lin, Na Lu and co-workers combine advanced materials science theory and AI technology to design a framework for rapidly discovering high-performance thermoelectric materials. Materials science ; Thermoelectric effect ; Nanotechnology ; Thermoelectric materials ; Engineering physics ; Electronic materials ; Field (mathematics) ; Engineering ethics ; Engineering ; Composite material ; Physics ; Thermodynamics Machine Learning in Materials Science
Authors: Jean‐François Gérard
Source: SPIRE - Sciences Po Institutional REpository (article, 2023-05-10)
Field: Materials Science — Materials Chemistry

Discovering new polymeric materials by combining high-throughput tools for synthesis and characterisation with Artificial Intelligence International audience Applications of artificial intelligence ; Field (mathematics) ; Key (lock) ; Feature (linguistics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Liangliang Xu | Xiaojuan Hu | Mengrong Li | Dongyoon Woo | Ning Xu | Sergey V. Levchenko | Zhongkang Han | Jinwoo Lee
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Descriptors of Transition Metal-Doped Ceria Catalysts for the Co-Selective No Reduction Reaction Catalysis ; Transition metal ; Doping ; Reduction (mathematics) ; Materials science ; Chemistry ; Inorganic chemistry ; Chemical engineering ; Organic chemistry ; Mathematics ; Optoelectronics ; Engineering Catalytic Processes in Materials Science ; Data Quality and Management ; Machine Learning in Materials Science
Authors: Franck Dumeignil
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2023-10-20)
Field: Materials Science — Materials Chemistry

Catalysis of the Future: Hybrid Catalysis, Artificial Intelligence, High Throughput Experiments… and their Combinations Séminaire invité Throughput ; Computer science ; Artificial intelligence ; Catalysis ; Biochemical engineering ; Nanotechnology ; Biology ; Engineering ; Materials science ; Telecommunications ; Biochemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Changrok So | Young‐Shin Kim | Jong Hyuk Park | Gwan-Yeong Kim | Daniel Cha | Jong Hwan Ko | Boseok Kang
Source: Advanced Intelligent Systems (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Data‐Driven Design of Electrically Conductive Nanocomposite Materials: A Case Study of Acrylonitrile–Butadiene–Styrene/Carbon Nanotube Binary Composites Electrically Conductive Nanocomposite Materials In article number 2200399, Boseok Kang, Jong Hwan Ko, and co-workers have developed an artificial intelligence (AI) system predicting various physical properties of carbon nanotube (CNT) / acrylonitrile-butadiene-styrene (ABS) nanocomposites by training the model with data collected from online literature. The AI model not only predicts physical properties of the nanocomposites when manufacturing information is given, but also conversely determines manufacturing conditions for the nanocomposites with desired physical properties. This demonstrates that AI-supported material development systems would be expanded into various applications in composite manufacturing. Nanocomposite ; Materials science ; Carbon nanotube ; Acrylonitrile butadiene styrene ; Composite material ; Composite number ; Acrylonitrile ; Styrene-butadiene ; Nanotube ; Electrical conductor ; Styrene ; Copolymer ; Polymer Machine Learning in Materials Science
Authors: Tan Lo Wong | Yasith S. Perera | Cristina Vallés | Adel Nasser | Chamil Abeykoon
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Data-Driven Model on the Thermal Transfer Mechanism of Composite Phase Change Materials Materials science ; Thermal conductivity ; Composite number ; Composite material ; Thermal energy storage ; Heat transfer ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Heat Transfer and Optimization
Authors: Trevor Gokey | David L. Mobley
Source: ChemRxiv (preprint, 2023-09-21)
Field: Chemistry — Physical and Theoretical Chemistry

Hierarchical clustering of chemical space using binary-encoded SMARTS for building data-driven chemical perception models Molecular mechanics force fields require a chemical perception model to assign parameters to molecules. A recent advancement in force fields is the use of the SMARTS substructure query language as the perception model. Although it is straightforward to write SMARTS patterns to define new force field parameters, it is difficult to manually determine the particular patterns needed to improve performance. Here we present a scheme to automatically generate SMARTS patterns that partitions the data according to a given objective. From this operation we describe how hierarchies of SMARTS patterns can be generated from the input data. Such hierarchies can be used for building force fields from scratch or improve upon established ones. We test our methods by building SMARTS hierarchies that map SMARTS patterns to existing force fields such as GAFF and Sage which allows us to test that our approach can recognize the chemical substructures typically use by human-designed force fields. We then build a SMARTS hierarchy that clusters bonds based on quantum mechanically-derived bond lengths which demonstrates the possibility of creating novel force field chemical perception models. Force field (fiction) ; Hierarchy ; Space (punctuation) ; Computer science ; Field (mathematics) ; Cluster analysis ; Perception ; Chemical space ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Chemistry Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Anna V. Nartova | А. В. Матвеев | M. Yu. Mashukov | V. A. Belotserkovskii | Natalya Sankova | Vitalii Yu. Kudinov | A. G. Okunev
Source: Кинетика и катализ (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

iOk Platform for Automatic Search and Analysis of Objects on Images Using Artificial Intelligence in the Study of Supported Catalysts iOk platform for automatic search and analysis of objects on images using artificial intelligence is presented. iOk platform combines web-service ParticlesNN and cloud services DLgram and No Code ML, which use Telegram messenger as interface. Platform allows working with any types of images (electron, probe, optical microscopy, photography) of any quality without preprocessing. Users can train the neural network by itself on their own images. The results of image recognition are objects, its areas, sizes and position on image. Presented services are in free access, no coding skills are required. iOk Platform is a user-friendly tool for the work with any type of images for automatic search of the objects and determination of their parameters. Computer science ; Artificial intelligence ; Computer vision ; Human–computer interaction Machine Learning in Materials Science ; Cell Image Analysis Techniques
Authors: Andreas Leitherer | Byung Chul Yeo | Christian H. Liebscher | Luca M. Ghiringhelli
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

Data for "Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy" This repository contains training data (HAADF images and FFT-HAADF descriptors, 80/20 splits employed in this work), neural-network models (the final, optimal model as well as the collection of models obtained via Bayesian optimization hyperparameter search), as well as experimental and synthetic images. Identification (biology) ; Electron microscope ; Materials science ; Microscopy ; Artificial intelligence ; Computer science ; Optics ; Physics ; Biology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Manoj K S Chhangani | Sofia Hussain | C. Mehta | R Baig | R Varma | M Bhute | Y Mahant | S Kondawar | E Bradley | L Castle | Q Chaudhry | P Chaudhary | R Murthy | R Kikkeri | G Chedid | A Yassin | B Chhikara | S Kumar | N Jain | A Kumar | R Kumar | S Ding | W Wang | M Gawande | A Rathi | J Tucek | K Gerloff | C Albrecht | A Boots | I Forster | R Schins | R Kessler | A Lemine | M Zagho | T Altahtamouni | N Bensalah | A Llorens | E Lloret | P Picouet | R Trbojevich | A Fernandez | S Misra | P Moitra | P Kondaiah | S Bhattacharya | V Morris | N Woodward | A Gunning | G Oberdorster | E Oberdorster | J Oberdorster | G Pyrgiotakis | A Vasanthakumar | Y Gao | M Eleftheriadou | E Toledo | A Dearaujo | J Mcdevitt | T Han | G Mainelis | R Mitchell | P Demokritou | G Sacha | P Varona | A Shirazi | R Tiwari | D Mandal | K Parang | E Singh | H Nalwa | J Singh | C Uboldi | G Giudetti | F Broggi | D Gilliland | J Ponti | F Rossi
Source: International Research Journal of Modernization in Engineering Technology and Science (article, 2023-09-14)
Field: Chemistry — Physical and Theoretical Chemistry

PERSPECTIVES ON NANOSCALE CHEMISTRY AND ITS APPLICATIONS Nanoscale chemistry is a dynamic field at the crossroads of chemistry and nanotechnology, have garnered considerable attention from both the research community and various industries.This paper aims to present an overview of the prevailing trends and future potential within these spheres.It delves into the foundational principles underpinning these disciplines, elucidating the techniques for synthesizing and manipulating nanomaterials imbued with exceptional properties.A central focus is dedicated to nanocatalysis strategies that leverage these nanomaterials as proficient catalysts, exhibiting heightened selectivity and reactivity.Notable strides in the design of catalytic nanomaterials, spanning noble metals, metal oxides, and carbon-based nanomaterials, are prominently featured.The integration of computational methodologies and artificial intelligence has expedited the advancement of nanoscale chemistry research, underscoring the collaborative role of technology in propelling these domains forward.Notably, this paper accentuates the pivotal contribution of nanocatalysis in fostering sustainable and environmentally-conscious chemical transformations, aligning with the broader objectives of ecological responsibility.Amidst the promising prospects, this discourse acknowledges the challenges that lie ahead and underscores the imperative for interdisciplinary cooperation.It also underscores the ethical and responsible development necessary for harnessing the full potential of nanotechnology to address the pressing global imperatives.In essence, this manuscript encapsulates the vibrancy of nanochemistry and nanocatalysis, advocating for their symbiotic relationship in shaping a more sustainable future. Nanoscopic scale ; Nanotechnology ; Chemistry ; Materials science Various Chemistry Research Topics
Authors: Yigitcan Comlek | Liwei Wang | Wei Chen
Source:  (article, 2023-08-20)
Field: Materials Science — Materials Chemistry

Mixed-Variable Global Sensitivity Analysis With Applications to Data-Driven Combinatorial Materials Design Abstract Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate the novel Latent Variable Gaussian Process (LVGP) with Sobol’ analysis to develop the first metamodel-based mixed-variable GSA method. Through two analytical case studies, we first validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the new metamodel-based mixed-variable GSA method can benefit various engineering design applications, we employ our method with multi-objective Bayesian optimization (BO) to accelerate the Pareto front design exploration in many-level combinatorial design spaces. Specifically, we implement a sensitivity-aware design framework for metal-organic framework (MOF) materials that are constructed only from qualitative design variables and show the benefits of our method for expediting the exploration of novel MOF candidates from a many-level large combinatorial design space. Expediting ; Computer science ; Sobol sequence ; Metamodeling ; Variable (mathematics) ; Engineering design process ; Context (archaeology) ; Categorical variable ; Sensitivity (control systems) ; Design of experiments ; Probabilistic design ; Surrogate model ; Mathematical optimization ; Machine learning ; Systems engineering ; Mathematics ; Engineering Machine Learning in Materials Science ; Polymer crystallization and properties ; Computational Drug Discovery Methods
Authors: Kandler Smith
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

(Invited) Simulation of Battery Chemo-Mechanical Physics Incorporating Degradation Computer-aided engineering models have accelerated the design cycle for large-format battery cells, packs, and systems. Building on that success, researchers are developing physics-based models to accelerate advancements in battery manufacturing, degradation/lifetime prediction, and materials research. This talk highlights achievements and challenges. The first simulates the reaction sequences of solid/electrolyte interfacial growth, coupling atomistic- to continuum-scale models to identify pathways to extend the calendar life of the silicon anode. The second describes electrochemical-mechanical degradation of 3D polycrystalline cathode particles to quantify degradation processes and identify optimal material architectures and fast-charge protocols. Third, artificial intelligence algorithms are coupled with electrochemical and degradation physics to identify electrochemical performance parameters, diagnose degradation, and predict lifetime. Degradation (telecommunications) ; Battery (electricity) ; Cathode ; Anode ; Materials science ; Electrochemistry ; Electrolyte ; Computer science ; Nanotechnology ; Engineering ; Electrical engineering ; Physics ; Electrode Machine Learning in Materials Science
Authors: Saltanat Yegemberdiуeva | Nartai Khaldarov | Marat Rakhimov
Source: REPORTS (review, 2023-09-30)
Field: Chemistry — Organic Chemistry

A COMPREHENSIVE REVIEW ON BUTYL ALCOHOLS SYNTHESIS THROUGH DIFFERENT METHODS The synthesis of butyl alcohols, pivotal in various industrial applications, stands as a dynamic and ever-evolving field. This comprehensive review objective to traverse the expansive landscape of synthesis methodologies, delineating the historical developments while bringing to fore the modern, sophisticated approaches. Leveraging a meticulous review methodology, we scrutinize a plethora of synthesis paths including chemical and biological routes, and emerging techniques rooted in nanotechnology and green synthesis methods. Through a critical lens, we dissect the yield and efficiency, economic considerations, and environmental impact of these methods, offering a comparative analysis grounded in the latest research and case studies from both industrial and academic spheres. Our discussion extends to envisaging the future prospects, highlighting the burgeoning avenues in sustainable and technologically advanced synthesis strategies, and underscoring the potential for innovation in harnessing artificial intelligence and fostering academic-industry collaborations. We conclude with a hopeful outlook, advocating for a synthesis landscape that harmoniously blends tradition and innovation, steering towards sustainable, efficient, and economically viable production pathways. This manuscript serves as a comprehensive guide and a catalyst for fostering a vibrant dialogue in the butyl alcohols synthesis landscape, urging a harmonized progression rooted in sustainability and efficiency. Chemistry Chemical Synthesis and Reactions
Authors: Erik Tomas Holmen Olofsson
Source: Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Development of a digital twin of the catalyst extrusion process from combining physics-based and data-driven models Extrusion ; Process (computing) ; Computer science ; Materials science ; Metallurgy ; Programming language Machine Learning in Materials Science
Source: Chemistry International (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Polymer Synthesis 2022, in conjunction with MACRO2022This interactive educational workshop on polymers for synthesis was organized in the format of an in-person meeting in conjunction with MACRO2022 in Winnipeg, Canada on 17 July 2022..It was the first in a projected series of four workshops, covering respectively: synthesis, characterization, processing, and applications of polymers.All three lectures touched on the understanding of the basic science, terms and concepts that are critical to polymer synthesis related to chaingrowth polymerization with a particular emphasis on polymerization-induced self-assembly (PISA).Thought-provoking insights into the experimental design, modeling/simulation, and synthesis using artificial intelligence; coupled with the results and discussion of research were presented.Since MACRO2022 Polymer science ; Polymer ; Materials science ; Composite material Advanced Polymer Synthesis and Characterization