jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bruno Jesus | Thierry Jauffrais | Erik Trampe | Vona Méléder | Lourenço Ribeiro | Joan M. Bernhard | Emmanuelle Geslin | Michael Kühl
Source: Environmental Microbiology (article, 2023-09-06)
Field: Materials Science — Biomaterials

Microscale imaging sheds light on species‐specific strategies for photo‐regulation and photo‐acclimation of microphytobenthic diatoms Intertidal microphytobenthic (MPB) biofilms are key sites for coastal primary production, predominantly by pennate diatoms exhibiting photo-regulation via non-photochemical quenching (NPQ) and vertical migration. Movement is the main photo-regulation mechanism of motile (epipelic) diatoms and because they can move from light, they show low-light acclimation features such as low NPQ levels, as compared to non-motile (epipsammic) forms. However, most comparisons of MPB species-specific photo-regulation have used low light acclimated monocultures, not mimicking environmental conditions. Here we used variable chlorophyll fluorescence imaging, fluorescent labelling in sediment cores and scanning electron microscopy to compare the movement and NPQ responses to light of four epipelic diatom species from a natural MPB biofilm. The diatoms exhibited different species-specific photo-regulation features and a large NPQ range, exceeding that reported for epipsammic diatoms. This could allow epipelic species to coexist in compacted light niches of MPB communities. We show that diatom cell orientation within MPB can be modulated by light, where diatoms oriented themselves more perpendicular to the sediment surface under high light vs. more parallel under low light, demonstrating behavioural, photo-regulatory response by varying their light absorption cross-section. This highlights the importance of considering species-specific responses and understanding cell orientation and photo-behaviour in MPB research. Diatom ; Biology ; Acclimatization ; Photoprotection ; Intertidal zone ; Ecological niche ; Algae ; Botany ; Ecology ; Photosynthesis ; Habitat Diatoms and Algae Research ; Marine and coastal ecosystems ; Aquatic Ecosystems and Phytoplankton Dynamics
Authors: Léo Djevahirdjian | Loïc Lechevallier | Marie‐Aline Martin‐Drumel | Olivier Pirali | Guillaume Ducournau | R. Kassi | S. Kassi
Source: Nature Communications (article, 2023-11-07)
Field: Chemistry — Spectroscopy

Frequency stable and low phase noise THz synthesis for precision spectroscopy We present a robust approach to generate a continuously tunable, low phase noise, Hz linewidth and mHz/s stability THz emission in the 0.1 THz to 1.4 THz range. This is achieved by photomixing two commercial telecom, distributed feedback lasers locked by optical-feedback onto a single highly stable V-shaped optical cavity. The phase noise is evaluated up to 1.2 THz, demonstrating Hz-level linewidth. To illustrate the spectral performances and agility of the source, low pressure absorption lines of methanol and water vapors have been recorded up to 1.4 THz. In addition, the hyperfine structure of a water line at 556.9 GHz, obtained by saturation spectroscopy, is also reported, resolving spectral features displaying a full-width at half-maximum of 10 kHz. The present results unambiguously establish the performances of this source for ultra-high resolution molecular physics. Spectroscopy ; Noise (video) ; Phase (matter) ; Phase noise ; Materials science ; Physics ; Optoelectronics ; Computer science ; Optics ; Artificial intelligence Spectroscopy and Laser Applications ; Terahertz technology and applications ; Advanced Chemical Physics Studies
Authors: Mingwan Leng | Nandu Koripally | Junjie Huang | Aikaterini Vriza | Kyeong Yeon Lee | Xiaozhou Ji | Chenxuan Li | Megan Hays | Qing Tu | Kim R. Dunbar | Jie Xu | Tse Nga Ng | Lei Fang
Source: Materials Horizons (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Synthesis and exceptional operational durability of polyaniline-inspired conductive ladder polymers Ladder-type structures can impart exceptional stability to polymeric electronic materials. This article introduces a new class of conductive polymers featuring a fully ladder-type backbone. A judicious molecular design strategy enables the synthesis of a low-defect ladder polymer, which can be efficiently oxidized and acid-doped to achieve its conductive state. The structural elucidation of this polymer and the characterization of its open-shell nature are facilitated with the assistance of studies on small molecular models. An autonomous robotic system is used to optimize the conductivity of the polymer thin film, achieving over 7 mS cm<sup>-1</sup>. Impressively, this polymer demonstrates unparalleled stability in strong acid and under harsh UV-irradiation, significantly surpassing commercial benchmarks like PEDOT:PSS and polyaniline. Moreover, it displays superior durability across numerous redox cycles as the active material in an electrochromic device and as the pseudocapacitive material in a supercapacitor device. This work provides structural design guidance for durable conductive polymers for long-term device operation. Polyaniline ; Durability ; Materials science ; Conductive polymer ; Electrical conductor ; Polymer ; Polymer science ; Nanotechnology ; Polymer chemistry ; Composite material Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors
Authors: Brad Boyce | Rémi Dingreville | Saaketh Desai | Elise Walker | Troy Shilt | Kimberly L. Bassett | Ryan R. Wixom | Aaron P. Stebner | Raymundo Arróyave | Jason Hattrick‐Simpers | James A. Warren
Source: Matter (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Machine learning for materials science: Barriers to broader adoption Data science ; Engineering ethics ; Computer science ; Nanotechnology ; Engineering ; Materials science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Electron and X-Ray Spectroscopy Techniques
Authors: Brinda Vallat | Gerardo Tauriello | Stefan Bienert | Juergen Haas | Benjamin Webb | Augustin Žídek | Wei Zheng | Ezra Peisach | Dennis W. Piehl | Ivan Anischanka | Ian Sillitoe | James Tolchard | Mihály Váradi | David Baker | Christine Orengo | Yang Zhang | Jeffrey C. Hoch | Genji Kurisu | Ardan Patwardhan | Sameer Velankar | S.K. Burley | Andrej Săli | Torsten Schwede | Helen M. Berman | John Westbrook
Source: Journal of Molecular Biology (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

ModelCIF: An Extension of PDBx/mmCIF Data Representation for Computed Structure Models ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. ModelCIF describes the specific set of attributes and metadata associated with macromolecular structures modeled by solely computational methods and provides an extensible data representation for deposition, archiving, and public dissemination of predicted three-dimensional (3D) models of macromolecules. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined 3D structures of macromolecules and associated metadata. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described. Computer science ; Metadata ; Interoperability ; Set (abstract data type) ; Representation (politics) ; Data science ; Information retrieval ; Software ; Data model (GIS) ; Data mining ; World Wide Web ; Programming language ; Artificial intelligence Enzyme Structure and Function ; Protein Structure and Dynamics ; Scientific Computing and Data Management
Authors: Jiatong Li | Yunqing Liu | Wenqi Fan | Xiao-Yong Wei | Hui Liu | Jiliang Tang | Qing Li
Source: arXiv (Cornell University) (preprint, 2023-06-11)
Field: Materials Science — Materials Chemistry

Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs. However, most of the existing methods heavily rely on domain experts, require excessive computational cost, or suffer from sub-optimal performance. On the other hand, Large Language Models (LLMs), like ChatGPT, have shown remarkable performance in various cross-modal tasks due to their powerful capabilities in natural language understanding, generalization, and in-context learning (ICL), which provides unprecedented opportunities to advance molecule discovery. Despite several previous works trying to apply LLMs in this task, the lack of domain-specific corpus and difficulties in training specialized LLMs still remain challenges. In this work, we propose a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning. MolReGPT leverages the principle of molecular similarity to retrieve similar molecules and their text descriptions from a local database to enable LLMs to learn the task knowledge from context examples. We evaluate the effectiveness of MolReGPT on molecule-caption translation, including molecule understanding and text-based molecule generation. Experimental results show that compared to fine-tuned models, MolReGPT outperforms MolT5-base and is comparable to MolT5-large without additional training. To the best of our knowledge, MolReGPT is the first work to leverage LLMs via in-context learning in molecule-caption translation for advancing molecule discovery. Our work expands the scope of LLM applications, as well as providing a new paradigm for molecule discovery and design. Computer science ; Leverage (statistics) ; Context (archaeology) ; Translation (biology) ; Artificial intelligence ; Data science ; Natural language processing ; Chemistry ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Preeti Thakur | Sonam Kumari | Satyendra Singh | Blaise Ravelo | Fayu Wan | Atul Thakur
Source: ChemBioEng Reviews (review, 2023-08-18)
Field: Materials Science — Materials Chemistry

Mg‐Zn Ferrite Nanoparticles: A Brief Review on Synthesis, Characterizations, and Applications Abstract Ferrites are of great interest for both fundamental science and technological applications due to their outstanding physical, chemical, and magnetic properties. Among various ferrites, Mg‐Zn ferrites is one of the best magnetic materials for researchers due to their unique properties such as high electric resistivity, Curie temperature, low coercivity, low eddy current, dielectric losses, low cost, and better environmental stability. This makes ferrites suitable for many applications such as power transformers, microwave devices, computer memories, logic devices, recording heads, loading coils, antenna rods, color monitoring tubes, telecommunications, hyperthermia, and so forth. Mg‐Zn ferrite nanoparticles have gained increased interest for many applications such as catalysts, antimicrobial potential, and sensors for the detection of combustible gases (CH 4 , CO, and CO 2 ). In this brief review, the strategies used in the synthesis of Mg‐Zn ferrite nanoparticles are summarized. The different structural, morphological, magnetic, and electric properties of these ferrites are analyzed. Materials science ; Ferrite (magnet) ; Nanoparticle ; Transformer ; Coercivity ; Curie temperature ; Microwave ; Dielectric ; Magnetic nanoparticles ; Optoelectronics ; Nanotechnology ; Engineering physics ; Electrical engineering ; Composite material ; Voltage ; Ferromagnetism ; Computer science ; Telecommunications ; Condensed matter physics Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Advancements in Battery Materials
Authors: Anastasia Konstantinova | Pavel Yudaev | А. В. Орлов | Oleg I. Loban | Nikolay Lukashov | Evgeniy M. Chistyakov
Source: Journal of Composites Science (article, 2023-10-07)
Field: Materials Science — Polymers and Plastics

Aryloxyphosphazene-Modified and Graphite-Filled Epoxy Compositions with Reduced Flammability and Electrically Conductive Properties A method has been developed for producing an epoxy composition based on a low-viscosity epoxy-resorcinol resin, a phosphazene-containing curing agent, isophoronediamine, and thermally expanded graphite as a filler. The degree of cure and the absence of side reactions during the curing process were confirmed using IR spectroscopy. The influence of the content of phosphazene-containing curing agent and filler on the physico-mechanical properties of the composition, its fire resistance, and antistatic properties were studied. Using the UL-94 HB horizontal burning test, it was found that the addition of 10 and 20 wt. % phosphazene-containing curing agent (relative to isophoronediamine) reduces the burning speed by 10 times compared to a sample without phosphazene. The addition of a filler to a composition containing phosphazene reduces the burning speed by 25 times compared to a composition without phosphazene and imparts antistatic properties to the epoxy composition, as evidenced by the specific volume electrical resistance of the order of 101 Ohm·m. Phosphazene-containing curing agent had no statistically significant effect on specific volume electrical resistivity (p &gt; 0.05). Tests of physico-mechanical and adhesive properties (tensile strength, compressive strength, water absorption, water solubility, abrasion resistance, and adhesive strength) of filled epoxy compositions with 10 and 20 wt. % phosphazene-containing curing agent demonstrated that these properties met the requirements for floor coverings in construction and parts of electrical devices. Phosphazene ; Composite material ; Materials science ; Antistatic agent ; Curing (chemistry) ; Epoxy ; Ultimate tensile strength ; Polymer Flame retardant materials and properties ; Epoxy Resin Curing Processes ; Synthesis and properties of polymers
Authors: Saurabh Deshpande | Raúl I. Sosa | Stéphane Bordas | Jakub Lengiewicz
Source: Frontiers in Materials (article, 2023-03-24)
Field: Materials Science — Materials Chemistry

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks–a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies. Convolution (computer science) ; Computer science ; Artificial neural network ; Statistical physics ; Mechanics ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Oil and Gas Production Techniques
Authors: Kimberly Y. Kartowikromo | Orobola E. Olajide | Ahmed Hamid
Source: Journal of Mass Spectrometry (review, 2023-08-24)
Field: Chemistry — Spectroscopy

Collision cross section measurement and prediction methods in omics Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed. Chemistry ; Metabolomics ; Analyte ; Ion-mobility spectrometry ; Lipidomics ; Biological system ; Fragmentation (computing) ; Mass spectrometry ; Biochemical engineering ; Computational biology ; Computer science ; Chromatography Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Alexander A. Guda | М. В. Киричков | Viktor Shapovalov | Alexey I. Muravlev | D. M. Pashkov | С. А. Гуда | Anton Pavlovich Bagliy | Sergey Soldatov | Sergey V. Chapek | А. В. Солдатов
Source: The Journal of Physical Chemistry C (article, 2023-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Analysis of Reaction Parameters in UV-Mediated Synthesis of Gold Nanoparticles Gold nanoparticles represent an important class of functional nanomaterials for optoelectronics, biomedical applications, and catalysis. Therefore, controllable synthesis of nanoparticles with specified size and shape is important. Though reduction of gold ions is quite a simple process and may be performed with many different protocols, the reproducibility of the results and transfer of protocols between independent research groups remains a challenging task. Machine learning analysis based on statistical approaches is hardly applicable to the published data, since most of the researchers report only successful syntheses. In this work, we apply uniform sampling of the reaction parameter space. The concentrations of gold precursor, reducing agent, and surfactant were varied via an improved Latin hypercube sampling, and each run was performed under in situ UV–vis control. Based on the resulting set of optical spectra, we address the relevant chemical questions about nanoparticle formation, their shape, and period of growth. Our work demonstrates a data driven approach applied to the space of reaction parameters in a limited available set of experiments. Colloidal gold ; Computer science ; Nanoparticle ; Nanotechnology ; Chemical space ; Materials science ; Chemistry ; Drug discovery Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Youngtae Park | Chang‐Kyu Hwang | Kihoon Bang | Doosun Hong | Hyobin Nam | Soonho Kwon | Byung Chul Yeo | Dohyun Go | Jihwan An | Byeong‐Kwon Ju | Sang Hoon Kim | Ji Young Byun | Seung Yong Lee | Jong Min Kim | Dong-Hun Kim | Sang Soo Han | Hyuck Mo Lee
Source: Applied Catalysis B: Environmental (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Machine learning filters out efficient electrocatalysts in the massive ternary alloy space for fuel cells Ternary operation ; Alloy ; Anode ; Cathode ; Materials science ; Catalysis ; Chemical engineering ; Oxygen reduction reaction ; Fuel cells ; Ternary alloy ; Nanotechnology ; Computer science ; Electrochemistry ; Metallurgy ; Electrode ; Chemistry ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Karol Baran | Adam Kloskowski
Source: The Journal of Physical Chemistry B (article, 2023-11-28)
Field: Materials Science — Materials Chemistry

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction Ionic liquids (ILs) provide a promising solution in many industrial applications, such as solvents, absorbents, electrolytes, catalysts, lubricants, and many others. However, due to the enormous variety of their structures, uncovering or designing those with optimal attributes requires expensive and exhaustive simulations and experiments. For these reasons, searching for an efficient theoretical tool for finding the relationship between the IL structure and properties has been the subject of many research studies. Recently, special attention has been paid to machine learning tools, especially multilayer perceptron and convolutional neural networks, among many other algorithms in the field of artificial neural networks. For the latter, graph neural networks (GNNs) seem to be a powerful cheminformatic tool yet not well enough studied for dual molecular systems such as ILs. In this work, the usage of GNNs in structure-property studies is critically evaluated for predicting the density, viscosity, and surface tension of ILs. The problem of data availability and integrity is discussed to show how well GNNs deal with mislabeled chemical data. Providing more training data is proven to be more important than ensuring that they are immaculate. Great attention is paid to how GNNs process different ions to give graph transformations and electrostatic information. Clues on how GNNs should be applied to predict the properties of ILs are provided. Differences, especially regarding handling mislabeled data, favoring the use of GNNs over classical quantitative structure-property models are discussed. Cheminformatics ; Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Graph ; Ionic liquid ; Variety (cybernetics) ; Convolutional neural network ; Theoretical computer science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ionic liquids properties and applications