jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Philipp Peslalz | Andreas Vorbach | Anton Bleisch | Elisa Liberini | Frank Kraus | Flavia Izzo | Heike Brötz‐Oesterhelt | Friedrich Götz | Bernd Plietker
Source: Chemistry - A European Journal (article, 2024-06-11)
Field: Chemistry — Organic Chemistry

Chemical Predictive Modelling and Natural Product‐based Divergent Synthesis – Design of Type B PPAPs with Nanomolar Activities against MRSA In response to the pressing global challenge of antibiotic resistance, time efficient design and synthesis of novel antibiotics are of immense need. Polycyclic polyprenylated acylphloroglucinols (PPAP) were previously reported to effectively combat a range of gram-positive bacteria. Although the exact mode of action is still not clear, we conceptualized a late-stage divergent synthesis approach to expand our natural product-based PPAP library by 30 additional entities to perform SAR studies against methicillin-resistant Staphylococcus aureus (MRSA). Although at this point only data from cellular assays are available and understanding of molecular drug-target interactions are lacking, the experimental data were used to generate 3D-QSAR models via an artificial intelligence training and to identify a common pharmacophore model. The experimentally validated QSAR model enabled the estimation of anti-MRSA activities of a virtual compound library consisting of more than 100,000 in-silico generated B PPAPs, out of which the 20 most promising candidates were synthesized. These novel PPAPs revealed significantly improved cellular activities against MRSA with growth inhibition down to concentrations less than 1 μm. Pharmacophore ; Natural product ; In silico ; Quantitative structure–activity relationship ; Combinatorial chemistry ; Computational biology ; Virtual screening ; Staphylococcus aureus ; Antibiotics ; Chemistry ; Methicillin-resistant Staphylococcus aureus ; Chemical space ; Stereochemistry ; Computer science ; Drug discovery ; Bacteria ; Biology ; Biochemistry ; Genetics Click Chemistry and Applications ; Multicomponent Synthesis of Heterocycles ; Chemical Synthesis and Analysis
Authors: Weikang Liu | Liang Liu | Zhuangzhi Li | Xinyi Wu | Xiaomin Li | Kepeng Song | Xiangxiang Zhao | G. D. Tang | Jihao Xie | Shuyun Wu | Bin Cheng | Bin Cui | Jifan Hu
Source: Carbon (article, 2024-03-16)
Field: Materials Science — Materials Chemistry

Observation of room-temperature ferromagnetism in copper-based graphene induced by stress engineering Ferromagnetism ; Graphene ; Copper ; Materials science ; Stress (linguistics) ; Condensed matter physics ; Metallurgy ; Composite material ; Nanotechnology ; Physics Graphene research and applications ; Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites
Authors: Christian Bailly
Source: Pharmacological Research - Reports (article, 2024-03-01)
Field: Chemistry — Organic Chemistry

Application of the Rumsfeld matrix to anticancer natural product target discovery For a long time, natural products (NP) contribute to the treatment of human pathologies, notably to cancer treatment with microbial and plant-derived products. The mechanism of action of new NP can be investigated using computational methods, boosted by artificial intelligence-assisted procedures, to guide target discovery. But there remain many bioactive NP with unknown targets and/or an incompletely understood or opaque mechanism of action. The most innovative compounds are those with a previously unknown chemical scaffold associated to an unknown mechanism of action, despite evidence of bioactivities in pharmacological assays. This challenging “unknown unknown” category of compounds requires major efforts to elucidate their mechanism of action, with the possibility to identify unprecedented first-in-class approaches to treat advanced cancers. There are also chemically well-known NP for which novel properties and medicinal applications are revealed without an associated target. Such compounds belong to the “known unknown” group, as it is the case for the anticancer drug etoposide and its potent anti-inflammatory action exploited to treat lymphohistiocytosis. The situation is different with new scaffolds for which a potential mechanism or molecular target can be predicted on the basis of functional analogies with other molecules. The “known unknown” and “unknown known” products can be classified using a Rumsfeld ignorance matrix by categorizing them into four subgroups. A Johari window-type classification of NP and associated targets is proposed. The matrix can help compound management and identification of research gaps to generate insights for further study. The review retraces a scientific excursion into the unknown of NP pharmacology. • Natural products (NP) remain an essential source of inspiration in drug discovery. • Defining the molecular targets of bioactive NP is necessary to guide drug design. • A known-unknown matrix applied to NP target discovery is presented. • The Rumsfeld ignorance matrix organizes information on NP structure and targets. • The Johary window-type approach helps to visualize knowledges on NP mechanism of action. Natural product ; Product (mathematics) ; Natural (archaeology) ; Matrix (chemical analysis) ; Computer science ; Political science ; Chemistry ; History ; Stereochemistry ; Mathematics Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Microbial Natural Products and Biosynthesis
Authors: Magesh Ganesan | Jeremy P. Krogman | Tatyana Konovalova | Leilani Lotti Díaz | Chia‐Wei Hsu
Source: ChemRxiv (preprint, 2024-06-30)
Field: Materials Science — Materials Chemistry

Non-noble metal driven catalysis : An exploration of the materials and applications Noble metals, which form a significant proportion of catalysts at present, are not sustainable, which necessitates the need for non-noble metal alternatives. This report contains a thorough analysis of data obtained from the CAS Content CollectionTM, pertaining to the recent development of non-noble metal catalysts, their usage, and applications. We herein identify the research trends since 2012 in four subfields: electrocatalysts, homogeneous catalysts, photocatalysts, and biocatalysts. Within the subfields, we present: the most reported catalysts; emerging catalyst substances; significant reactions, and applications; map of the cooccurrence of catalyst substances with their reactions and applications. With this article, we aim to provide a data driven overview of the complex publication landscape and unique scientific insights for a deeper understanding of the field. Noble metal ; Catalysis ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion
Authors: Yang Bai | Jianyan Feng | Peng Wang | Xin Chen | Xiaomin Luo | Peng Zhang | Xuechuan Wang
Source: Advanced Engineering Materials (article, 2024-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural Design of Multifunctional PET Non‐woven Fabric with Porous Dual‐Network for Infrared Stealth, Flame Retardant, and Electromagnetic Interference Shielding With rapid advances in electronic and communication technology, electromagnetic interference and other problems are becoming increasingly prominent. Thus, electromagnetic interference shielding materials have recently garnered extensive attention. In this study, a multi‐walled carbon nanotube/polyurethane/non‐woven electromagnetic shielding material (CPNW) is developed using impregnation and nonsolvent‐induced‐phase separation techniques. Utilizing a three‐dimensional nonwoven network as the substrate, the “nonwoven fabric‐polyurethane‐carbon nanotube” composite is impregnated and cured via the non‐solvent‐induced‐phase separation method, resulting in a distinctive porous dual‐network structure that ensures robust interfacial bonding between carbon nanotubes, nonwoven fabric, and polyurethane. At a carbon nanotube content of 10% (based on the mass of nonwoven fabric), CPNW exhibited an electromagnetic interference shielding effectiveness of 28.8 dB, a thermal conductivity of 0.127 W m −1 K −1 , and a burning time of 1 min and 15 s, demonstrating outstanding electromagnetic shielding, flame retardant, and infrared stealth capabilities. Overall, this study laid a theoretical groundwork for the development of multifunctional non‐woven electromagnetic shielding materials with widespread application potential in aerospace, military, artificial intelligence, and wearable electronics. Electromagnetic shielding ; Materials science ; Fire retardant ; Porosity ; Electromagnetic interference ; Composite material ; Interference (communication) ; Dual (grammatical number) ; Infrared ; Forensic engineering ; Acoustics ; Optics ; Engineering ; Electronic engineering ; Electrical engineering Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials ; Flame retardant materials and properties
Authors: Joerg Weber | Andreas Latza | Oleg Ryabchykov | Oliver Valet | Darina Storozhuk | Cicely Rathmell | Dieter Bingemann | D. J. Creasey
Source: Spectroscopy (article, 2024-07-01)
Field: Chemistry — Analytical Chemistry

Real-Time Chemometric Analysis of Multicomponent Bioprocesses Using Raman Spectroscopy Quantitative measurement is essential for modern bioreactor synthesis, which plays a crucial role in the production of foods and pharmaceuticals. These complex processes rely on living organisms, and require fast, high-performing inline analytics for effective process control and intervention. In this study, a glycerol-fed, lab-scale E. coli bioprocess producing representative pharmaceutical compounds was monitored offline with a portable, high-sensitivity Raman spectrometer. Sampling and high performance liquid chromatography (HPLC) analysis provided the ground truth calibration for a chemometric model developed using an easy-to-use, built-for-Raman chemometric software package. The chemometric model developed demonstrates the ability of this approach to offer spectroscopic prediction of feedstock, active pharmaceutical ingredients (API), and side product concentrations. This specific combination of compact spectrometer and AI-driven analysis software opens the door for the application of real-time, continuous, and non-invasive Raman spectroscopy in bioprocess monitoring, even for users without prior chemometric or Raman experience. Raman spectroscopy ; Chemometrics ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Spectroscopy ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Arpan Biswas | Mani Valleti | Rama K. Vasudevan | Maxim Ziatdinov | Sergei V. Kalinin
Source: Journal of Computing and Information Science in Engineering (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization Abstract Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. The associated notebooks allow to reproduce the reported analyses and apply them to other systems.2 Workflow ; Bayesian optimization ; Computer science ; Uncertainty quantification ; Embedding ; Domain (mathematical analysis) ; Fidelity ; Machine learning ; Theoretical computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Lei Miao | Xiaoang Yuan | Chun Tang | Changfeng Chen | Enlai Gao
Source: Extreme Mechanics Letters (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

A large atomic partition model for materials discovery Partition (number theory) ; Nanotechnology ; Materials science ; Engineering ; Computer science ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Avanish Mishra | Sumit A. Suresh | Saryu Fensin | Nithin Mathew | Edward M. Kober
Source: Physical Review Materials (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Learning from metastable symmetric-tilt grain boundaries using physics-based descriptors Grain boundaries (GBs) govern critical properties of polycrystalline materials. Although significant advancements have been made in characterizing minimum energy and ordered GBs, real GBs are seldom found in such well-defined states. This diversity of atomic arrangements in metastable states makes it challenging to establish structure-property relationships with physical insights. To address this challenge, we use data-driven methods to explore these relationships and examine the underlying physics. In this study, we utilize a large atomistic database $(\ensuremath{\sim}5000)$ of minimum energy and metastable states of symmetric-tilt copper GBs, combined with physically motivated local atomic environment (LAE) descriptors [strain functional descriptors (SFDs)], to predict GB properties and gain physical insights. Our regression models exhibit robust predictive capabilities using only 19 descriptors, generalizing to atomic environments in nanocrystals. A significant highlight of our work is the integration of an unsupervised method with SFDs to elucidate LAEs at GBs and their role in determining properties. The model, trained on these minimum energy and metastable GBs using SFDs, predicts the properties of unseen nanocrystals with good accuracy. Our research underscores the role of a physics-based representation of LAEs and the efficacy of data-driven methods in establishing GB structure-property relationships. Icon ; Metastability ; Grain boundary ; Energy (signal processing) ; Materials science ; Energy landscape ; Physics ; Crystallography ; Computer science ; Chemistry ; Microstructure ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Ion-surface interactions and analysis
Authors: Subhodeep Paul | Pallab Roy | Arpita Chatterjee | Pinjal Pandit | Rahul Mukherjee | Manojit Ghosh
Source: Journal of The Institution of Engineers (India) Series D (article, 2024-01-22)
Field: Materials Science — General Materials Science

Design and Analysis of Automotive Vehicle Components with Composite Materials Using ANSYS 18.1 Automotive industry ; Composite number ; Automotive engineering ; Engineering ; Manufacturing engineering ; Materials science ; Computer science ; Composite material ; Aerospace engineering Material Properties and Applications ; Mechanical Engineering and Vibrations Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Niklas Lenzen | Okyay Altay
Source: Smart Materials and Structures (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests Abstract Superelastic shape memory alloy (SMA) wires and rods possess unique deformation and energy dissipation capabilities. For the assessment of their stress response, commonly cyclic tensile tests are conducted. An important but subtle parameter in this procedure is the martensite evolution. In scenarios where conducting thermal experiments is impractical, inverse modeling from cyclic tests serves as a viable alternative. However, employing constitutive models in this process presents distinct challenges, such as parameter identification and calibration, or numerical stability issues. To address these challenges, this paper proposes a data-driven method based on a physics-informed deep operator network (DeepONet) to estimate the martensite evolution. Constraint with a stress equation, the network requires only strain–stress data for training and provides stress responses in addition to the martensite evolution. From the training data, the network learns to consider the effects included in the response. The DeepONet can be coupled with experiments to provide online estimates from noisy sensor-based strain inputs, while remaining numerically stable. Moreover, this approach avoids the need for separate parameter identification or calibration. This paper details this method and evaluates its performance through experiments conducted on superelastic SMA wires. Furthermore, as an alternative approach, training using a constitutive model is provided. Martensite ; Materials science ; Ultimate tensile strength ; Shape-memory alloy ; Operator (biology) ; Structural engineering ; Composite material ; Engineering ; Microstructure Machine Learning in Materials Science ; Shape Memory Alloy Transformations
Authors: Jian Li | Jin Li | Xing‐Hua Xia
Source: ACS Measurement Science Au (review, 2024-10-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmon Enhanced IR Spectroelectrochemistry Plasmon-enhanced infrared (IR) techniques have garnered significant interest for their ability to achieve greatly more sensitive IR detection than conventional surface enhanced IR techniques. However, the difficulty in electrically connecting antennas has limited their application in IR spectroelectrochemistry, a crucial field for catalysis, analysis, and energy storage. Recent technical advancements have enabled the successful application of electrochemical potentials to antennas, making plasmon-enhanced IR spectroelectrochemistry feasible. This perspective aims to summarize the latest strategies and offer insights into future improvements for better design of plasmon enhanced IR spectroelectrochemistry platforms and understanding of IR spectroelectrochemistry. Plasmon ; Infrared ; Surface plasmon ; Nanotechnology ; Materials science ; Surface plasmon resonance ; Electrochemistry ; Localized surface plasmon ; Optoelectronics ; Computer science ; Chemistry ; Electrode ; Nanoparticle ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical Analysis and Applications
Authors: Preeti Chauhan
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2024-07-15)
Field: Chemistry — Organic Chemistry

Advancements in Coordination Chemistry and Trends in the Chemical Industry: Applications and Implications Abstract: This research paper delves into the current advances in coordination chemistry alongside the evolving trends within the chemical industry, exploring their intersections and applications. Coordination chemistry has witnessed remarkable progress in recent years, driven by innovative synthetic methodologies and a deeper understanding of metal-ligand interactions. The study examines the synthesis and characterization of novel coordination complexes, highlighting their diverse structures and properties. Furthermore, it explores the role of coordination chemistry in various applications, ranging from catalysis and drug design to materials science and environmental remediation. Concurrently, the chemical industry is experiencing transformative shifts, embracing sustainable practices, and adapting to emerging regulatory frameworks. This paper investigates key trends shaping the chemical industry, such as green chemistry initiatives, circular economy principles, and the adoption of advanced manufacturing technologies. By synthesizing the latest research findings and industry developments, this study offers insights into the potential synergies between coordination chemistry advancements and the evolving landscape of the chemical industry, paving the way for innovative applications and sustainable solutions. Coordination complex ; Chemistry ; Nanotechnology ; Materials science ; Organic chemistry Inorganic and Organometallic Chemistry ; Catalysis and Oxidation Reactions ; Chemical Synthesis and Reactions
Authors: R. Hussein | Pamela R. Tsuruda | S R Seyed Mortezaei | Nicky Ferdyan | Christopher J. Wegerski | K. Srinivasan | Gillian L. Hirst | Neelufar Mozaffarian
Source: Annals of the Rheumatic Diseases (article, 2024-06-01)
Field: Chemistry — Organic Chemistry

POS0463 A NOVEL, ORAL, ALLOSTERIC INHIBITOR OF TYK2 DEMONSTRATES IN VITRO POTENCY, SELECTIVITY, AND IN VIVO EFFICACY IN MOUSE MODELS OF PSORIASIS In vivo ; Potency ; Allosteric regulation ; Psoriasis ; In vitro ; Pharmacology ; Chemistry ; Medicine ; Biology ; Receptor ; Biochemistry ; Immunology ; Biotechnology Synthesis and Reactions of Organic Compounds ; Synthesis and biological activity ; Monoclonal and Polyclonal Antibodies Research