jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: María González‐Rodríguez | Djedjiga Ait Eldjoudi | Alfonso Cordero‐Barreal | Mariam Farrag | María Varela‐García | Carlos Torrijos-Pulpón | Clara Ruiz‐Fernández | Maurizio Capuozzo | Alessandro Ottaiano | Francisca Lago | Jesús Pino | Yousof Farrag | Oreste Gualillo
Source: Antioxidants (article, 2023-12-14)
Field: Chemistry — Organic Chemistry

Oleocanthal, an Antioxidant Phenolic Compound in Extra Virgin Olive Oil (EVOO): A Comprehensive Systematic Review of Its Potential in Inflammation and Cancer OC exhibits promising therapeutic potential against both inflammation and cancer. We addressed its ability to target inflammatory genes and pathways, offering potential treatments for conditions like rheumatic diseases by regulating pathways such as NF-kB and MAPK. Additionally, OC's anticancer properties, particularly its notable inhibition of c-Met signaling across various cancers, highlight its efficacy, showcasing promise as a potential treatment. Cancer ; Medicine ; Inflammation ; Mediterranean diet ; Clinical trial ; Bioinformatics ; MEDLINE ; Systematic review ; Pharmacology ; Traditional medicine ; Biotechnology ; Biology ; Biochemistry ; Internal medicine Edible Oils Quality and Analysis ; Cholesterol and Lipid Metabolism ; Phytochemicals and Antioxidant Activities
Authors: Gregory S. Doerk | Aaron Stein | Suwon Bae | Marcus M. Noack | Masafumi Fukuto | Kevin G. Yager
Source: Science Advances (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends The directed self-assembly (DSA) of block copolymers (BCPs) is a powerful approach to fabricate complex nanostructure arrays, but finding morphologies that emerge with changes in polymer architecture, composition, or assembly constraints remains daunting because of the increased dimensionality of the DSA design space. Here, we demonstrate machine-guided discovery of emergent morphologies from a cylinder/lamellae BCP blend directed by a chemical grating template, conducted without direct human intervention on a synchrotron x-ray scattering beamline. This approach maps the morphology-template phase space in a fraction of the time required by manual characterization and highlights regions deserving more detailed investigation. These studies reveal localized, template-directed partitioning of coexisting lamella- and cylinder-like subdomains at the template period length scale, manifesting as previously unknown morphologies such as aligned alternating subdomains, bilayers, or a "ladder" morphology. This work underscores the pivotal role that autonomous characterization can play in advancing the paradigm of DSA. Nanotechnology ; Lamella (surface anatomy) ; Copolymer ; Materials science ; Characterization (materials science) ; Nanostructure ; Block (permutation group theory) ; Curse of dimensionality ; Morphology (biology) ; Self-assembly ; Computer science ; Polymer ; Artificial intelligence ; Biology Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies
Authors: Katarina Batalović | Jana Radaković | Bojana Kuzmanović | Mirjana Medić Ilić | Bojana Paskaš Mamula
Source: Journal of Energy Storage (article, 2023-05-20)
Field: Materials Science — Materials Chemistry

Machine learning-based high-throughput screening of Mg-containing alloys for hydrogen storage and energy conversion applications Hydrogen storage ; Intermetallic ; Hydride ; Enthalpy ; Gravimetric analysis ; Materials science ; Energy storage ; Hydrogen ; Thermodynamics ; Inorganic chemistry ; Chemical engineering ; Chemistry ; Metallurgy ; Metal ; Alloy ; Organic chemistry Hydrogen Storage and Materials ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Eunjae Shim | Ambuj Tewari | Tim Cernak | Paul M. Zimmerman
Source: Journal of Chemical Information and Modeling (review, 2023-06-14)
Field: Materials Science — Materials Chemistry

Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations. Transfer of learning ; Computer science ; Limit (mathematics) ; Machine learning ; Perspective (graphical) ; Artificial intelligence ; Active learning (machine learning) ; Data science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Xinyao Xu | Wenlin Zhao | Yaxi Hu | Liquan Wang | Jiaping Lin | Huimin Qi | Lei Du
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Discovery of thermosetting polymers with low hygroscopicity, low thermal expansivity, and high modulus by machine learning Based on the workflow of the materials genome approach, the challenge of enhancing multiple conflicting properties of an advanced thermosetting polymer of polycyanurates was addressed through high-throughout screening using machine learning. Thermosetting polymer ; Polymer ; Materials science ; Workflow ; Composite material ; Computer science ; Polymer science Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Epoxy Resin Curing Processes
Authors: Maximilian Seifermann | Patrick Reiser | Pascal Friederich | Pavel A. Levkin
Source: Small Methods (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

High‐Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels Abstract Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high‐throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high‐throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties. Throughput ; Bayesian optimization ; Self-healing hydrogels ; Computer science ; Workflow ; Scope (computer science) ; Process (computing) ; Machine learning ; Process engineering ; Materials science ; Engineering ; Wireless Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Thomas Plé | Louis Lagardère | Jean‐Philip Piquemal
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects We introduce FENNIX (Force-Field-Enhanced Neural Network InteraXions), a hybrid approach between machine-learning and force-fields. We leverage state-of-the-art equivariant neural networks to predict local energy contributions and multiple atom-in-molecule properties that are then used as geometry-dependent parameters for physically-motivated energy terms which account for long-range electrostatics and dispersion. Using high-accuracy <i>ab initio</i> data (small organic molecules/dimers), we trained a first version of the model. Exhibiting accurate gas-phase energy predictions, FENNIX is transferable to the condensed phase. It is able to produce stable Molecular Dynamics simulations, including nuclear quantum effects, for water predicting accurate liquid properties. The extrapolating power of the hybrid physically-driven machine learning FENNIX approach is exemplified by computing: (i) the solvated alanine dipeptide free energy landscape; (ii) the reactive dissociation of small molecules. Embedding ; Equivariant map ; Atom (system on chip) ; Molecule ; Force field (fiction) ; Range (aeronautics) ; Field (mathematics) ; Chemical physics ; Artificial neural network ; Physics ; Molecular physics ; Statistical physics ; Materials science ; Quantum mechanics ; Computer science ; Mathematics ; Pure mathematics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Force Microscopy Techniques and Applications
Authors: Forrest A. L. Laskowski | Daniel B. McHaffie | Kimberly A. See
Source: Energy & Environmental Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Identification of potential solid-state Li-ion conductors with semi-supervised learning A semi-supervised machine learning pipeline is reported for the discovery of new Li-ion solid-state electrolytes. The approach is experimentally validated with the synthesis and characterization of a new superionic conductor predicted by the model. Fast ion conductor ; Pipeline (software) ; Conductor ; Identification (biology) ; Electrical conductor ; Solid-state ; Characterization (materials science) ; Ion ; Electrolyte ; Materials science ; Computer science ; Nanotechnology ; Engineering physics ; Chemistry ; Engineering ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Changlu Zhou | Ruihao Yuan | Baolong Su | Jiangkun Fan | Bin Tang | Pingxiang Zhang | Jinshan Li
Source: Journal of Material Science and Technology (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Creep rupture life prediction of high-temperature titanium alloy using cross-material transfer learning Materials science ; Creep ; Superalloy ; Titanium alloy ; Titanium ; Convolutional neural network ; Transfer of learning ; Aerospace ; Deformation (meteorology) ; Service life ; Alloy ; Predictive modelling ; Service (business) ; Metallurgy ; Artificial intelligence ; Machine learning ; Composite material ; Computer science Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: A.R. Khoei | Mohammad Reza Seddighian | A. Rezaei Sameti
Source: International Journal of Mechanical Sciences (article, 2023-11-25)
Field: Materials Science — Materials Chemistry

Machine learning-based multiscale framework for mechanical behavior of nano-crystalline structures Nano- ; Materials science ; Nanotechnology ; Computer science ; Mechanical engineering ; Artificial intelligence ; Engineering ; Composite material Microstructure and mechanical properties ; Composite Material Mechanics ; Force Microscopy Techniques and Applications
Authors: Dongfang Zhang | Jun Zhang | Bo Peng | Tao Wu | Zhiyang Jiao | Lu Yin | Guanghuan Li | Xiaofei Fan | Shuxing Shen | Aixia Gu | Jianjun Zhao
Source: Scientia Horticulturae (article, 2023-07-21)
Field: Chemistry — Analytical Chemistry

Hyperspectral model based on genetic algorithm and SA-1DCNN for predicting Chinese cabbage chlorophyll content Hyperspectral imaging ; Chlorophyll ; Partial least squares regression ; Chlorophyll a ; Data pre-processing ; Preprocessor ; Convolutional neural network ; Mathematics ; Artificial intelligence ; Horticulture ; Biology ; Botany ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Leaf Properties and Growth Measurement
Authors: Gian Marco Ghiandoni | Eike Caldeweyher
Source: Scientific Reports (article, 2023-03-13)
Field: Chemistry — Physical and Theoretical Chemistry

Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules Hydrogen bonding is an interaction of great importance in drug discovery and development as it may significantly affect chemical and biological processes including the interaction of small molecules with other molecules, proteins, and membranes. In particular, hydrogen bonding can impact drug-like properties such as target affinity and oral availability which are critical to developing effective pharmaceuticals, and therefore, numerous methods for the calculation of properties such as hydrogen-bond strengths, free energy of hydration, or water solubility have been proposed over time. However, the accessibility to efficient methods for the predictions of such properties is still limited. Here, we present the development of Jazzy, an open-source tool for the prediction of hydrogen-bond strengths and free energies of hydration of small molecules. Jazzy also allows the visualisation of hydrogen-bond strengths with atomistic resolution to support the design of compounds with desired properties and the interpretation of existing data. The tool is described in its implementation, parameter fitting, and validation against two data sets of experimental hydration free energies. Jazzy is also applied against two chemical series of bioactive compounds to show that hydrogen-bond strengths can be used to understand their structure-activity relationships. Results from the validations highlight the strengths and limitations of Jazzy, and suggest its suitability for interactive design, screening, and machine-learning featurisation. Hydrogen bond ; Molecule ; Small molecule ; Chemical physics ; Hydrogen ; Solubility ; Materials science ; Computational chemistry ; Chemistry ; Computer science ; Biochemical engineering ; Physical chemistry ; Organic chemistry Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Wei Lu | Nic A. Lee | Markus J. Buehler
Source: Proceedings of the National Academy of Sciences (article, 2023-07-24)
Field: Materials Science — Biomaterials

Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here, we provide a detailed analysis of the heterogeneous graph structures of spider webs and use deep learning as a way to model and then synthesize artificial, bioinspired 3D web structures. The generative models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) an analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation; 2) a discrete diffusion model with full neighbor representation; and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bioinspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose an algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles toward integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties. Computer science ; Generative model ; Scalability ; Representation (politics) ; Artificial intelligence ; Theoretical computer science ; Generative grammar ; Database Silk-based biomaterials and applications ; Insect and Arachnid Ecology and Behavior ; Paleontology and Evolutionary Biology
Authors: Yonglong Du | Daxu Zhang | Liang Wang | Weiyu Guo | Long Wang
Source: Journal of the European Ceramic Society (article, 2023-09-09)
Field: Materials Science — Ceramics and Composites

Damage mechanism characterisation of plain weave ceramic matrix composites under in-plane shear using in-situ X-ray micro-CT and deep-learning-based image segmentation Materials science ; Composite material ; Ceramic ; Shear (geology) ; In situ ; Ceramic matrix composite ; Matrix (chemical analysis) ; Plain weave ; Plane (geometry) ; Geometry ; Physics Advanced ceramic materials synthesis ; Advanced materials and composites ; Additive Manufacturing and 3D Printing Technologies
Authors: Yiqian Kong | Zongyi Li | Qi Liu | Juncheng Song | Yinghua Zhu | Jinping Lin | Lili Song | Xiangyang Li
Source: Talanta (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Artificial neural network-facilitated V2C MNs-based colorimetric/fluorescence dual-channel biosensor for highly sensitive detection of AFB1 in peanut Chemistry ; Fluorescence ; Biosensor ; Chromatography ; Nanotechnology ; Biochemistry ; Optics Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Mamta Chahar | Sarita Khaturia | Har Lal Singh | Vijendra Singh Solanki | Neha Agarwal | Dipak Kumar Sahoo | Virendra Kumar Yadav | Ashish Patel
Source: Frontiers in Environmental Science (article, 2023-08-04)
Field: Chemistry — Organic Chemistry

Recent advances in the effective removal of hazardous pollutants from wastewater by using nanomaterials—A review Environmental nanotechnology has developed rapidly over the past few decades due to the fast advancement of nanotechnology and nanomaterials (NMs). Due to their nanoscale size, NMs are receiving immense attention in research and development worldwide. Their nano size has led to better catalysis, high reactivity, and high adsorption capacity. In wastewater treatment, nanotechnology has significant potential to improve the performance and efficiency of water decontamination; more effectively, it provides a sustainable way to keep water supplies safe. Numerous studies have found that removing harmful components from wastewater by employing nanoparticles in conjunction with various treatment methods is effective. The purpose of the current investigation is to conduct a review of the envisioned applications of various NMs in the treatment of wastewater. These NMs include carbonaceous NMs, metal-containing nanoparticles, and nanocomposites, all of which will be reviewed and highlighted in depth. Hazardous waste ; Human decontamination ; Wastewater ; Nanotechnology ; Nanomaterials ; Environmental science ; Pollutant ; Sewage treatment ; Waste management ; Materials science ; Environmental engineering ; Chemistry ; Engineering Nanomaterials for catalytic reactions ; Graphene and Nanomaterials Applications ; Membrane Separation Technologies
Authors: Lei Tang | Jingyun Zou
Source: Nano-Micro Letters (review, 2023-10-18)
Field: Materials Science — Materials Chemistry

p-Type Two-Dimensional Semiconductors: From Materials Preparation to Electronic Applications Two-dimensional (2D) materials are regarded as promising candidates in many applications, including electronics and optoelectronics, because of their superior properties, including atomic-level thickness, tunable bandgaps, large specific surface area, and high carrier mobility. In order to bring 2D materials from the laboratory to industrialized applications, materials preparation is the first prerequisite. Compared to the n-type analogs, the family of p-type 2D semiconductors is relatively small, which limits the broad integration of 2D semiconductors in practical applications such as complementary logic circuits. So far, many efforts have been made in the preparation of p-type 2D semiconductors. In this review, we overview recent progresses achieved in the preparation of p-type 2D semiconductors and highlight some promising methods to realize their controllable preparation by following both the top-down and bottom-up strategies. Then, we summarize some significant application of p-type 2D semiconductors in electronic and optoelectronic devices and their superiorities. In end, we conclude the challenges existed in this field and propose the potential opportunities in aspects from the discovery of novel p-type 2D semiconductors, their controlled mass preparation, compatible engineering with silicon production line, high-κ dielectric materials, to integration and applications of p-type 2D semiconductors and their heterostructures in electronic and optoelectronic devices. Overall, we believe that this review will guide the design of preparation systems to fulfill the controllable growth of p-type 2D semiconductors with high quality and thus lay the foundations for their potential application in electronics and optoelectronics. Semiconductor ; Electronics ; Nanotechnology ; Heterojunction ; Materials science ; Semiconductor materials ; Semiconductor device ; Engineering physics ; Optoelectronics ; Compound semiconductor ; Electrical engineering ; Physics ; Engineering 2D Materials and Applications ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices