jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ming-Yi Cai | Wentao Wang
Source: International Journal of Electrochemical Science (article, 2024-11-27)
Field: Chemistry — Analytical Chemistry

Advances in electrochemical methods for the determination of ephedrine: Current status and future trends The accurate and reliable detection of ephedrine-type alkaloids has become increasingly critical due to their dual role in legitimate therapeutic applications and potential misuse in illicit drug production. The development of rapid and sensitive detection methods is particularly important for pharmaceutical quality control, anti-doping monitoring in sports, and law enforcement agencies conducting on-site screening, where traditional chromatographic techniques may be impractical due to their complexity and cost. This review examines recent developments in electrochemical methods for ephedrine detection, focusing on advances in electrode materials, surface modification strategies, and analytical methodologies. We discuss the fundamental aspects of ephedrine's electrochemical behavior, including electron transfer mechanisms, pH effects, and mass transport phenomena. Particular attention is given to innovative electrode modifications incorporating nanomaterials, conducting polymers, and molecular recognition elements that have dramatically enhanced detection capabilities. The emergence of novel composite materials, including carbon-metal oxide hybrids and molecularly imprinted polymers, has enabled unprecedented levels of sensitivity and selectivity. We evaluate various analytical approaches, from fundamental voltammetric techniques to sophisticated flow-based systems, highlighting their strengths and limitations. The integration of these methods into practical applications across pharmaceutical quality control, environmental monitoring, food safety, and clinical diagnostics is critically assessed. The review concludes with an examination of current challenges and emerging trends, including the development of portable detection systems and the integration of artificial intelligence for signal processing. Ephedrine ; Current (fluid) ; Electrochemistry ; Medicine ; Chemistry ; Pharmacology ; Physics ; Thermodynamics ; Physical chemistry ; Electrode Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Biochemical Analysis and Sensing Techniques
Authors: Siyuan 思远 Wu 吴 | Tiannian 天念 Zhu 朱 | Sijia 思佳 Tu 涂 | Ruijuan 睿娟 Xiao 肖 | Jie 洁 Yuan 袁 | Quansheng 泉生 Wu 吴 | Hong 泓 Li 李 | Hongming 红明 Weng 翁
Source: Chinese Physics B (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Literature classification and its applications in condensed matter physics and materials science by natural language processing The exponential growth of literature is constraining researchers’ access to comprehensive information in related fields. While natural language processing (NLP) may offer an effective solution to literature classification, it remains hindered by the lack of labelled dataset. In this article, we introduce a novel method for generating literature classification models through semi-supervised learning, which can generate labelled dataset iteratively with limited human input. We apply this method to train NLP models for classifying literatures related to several research directions, i.e., battery, superconductor, topological material, and artificial intelligence (AI) in materials science. The trained NLP ‘battery’ model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738, which indicates the accuracy and reliability of this scheme. Furthermore, our approach demonstrates that even with insufficient data, the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions. Computer science ; Artificial intelligence ; Reliability (semiconductor) ; Scheme (mathematics) ; Natural language processing ; Machine learning ; Physics Machine Learning in Materials Science ; Topic Modeling ; scientometrics and bibliometrics research
Authors: Gyoung S. Na
Source: Computational Materials Science (article, 2024-01-20)
Field: Materials Science — Materials Chemistry

One-shot heterogeneous transfer learning from calculated crystal structures to experimentally observed materials Transfer of learning ; Computer science ; Modality (human–computer interaction) ; Crystal (programming language) ; Transfer (computing) ; Artificial intelligence ; Biological system ; Machine learning ; Pattern recognition (psychology) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Jaewook Lee | Jinkyung Son | Juri Lim | In Kim | Seonwoo Kim | Namjung Cho | Woojin Choi | Dongil Shin
Source: Korean Journal of Chemical Engineering (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Transformer-Based Mechanical Property Prediction for Polymer Matrix Composites Ultimate tensile strength ; Materials science ; Flexural strength ; Polymer ; Composite material ; Transformer ; Modulus ; Computer science ; Flexural modulus Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Marta Pasquini | Marco Stenta
Source: Journal of Chemical Information and Modeling (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes Computational tools are revolutionizing our understanding and prediction of chemical reactivity by combining traditional data analysis techniques with new predictive models. These tools extract additional value from the reaction data <i>corpus</i>, but to effectively convert this value into actionable knowledge, domain specialists need to interact easily with the computer-generated output. In this application note, we demonstrate the capabilities of the open-source Python toolkit LinChemIn, which simplifies the manipulation of reaction networks and provides advanced functionality for working with synthetic routes. LinChemIn ensures chemical consistency when merging, editing, mining, and analyzing reaction networks. Its flexible input interface can process routes from various sources, including predictive models and expert input. The toolkit also efficiently extracts individual routes from the combined synthetic tree, identifying alternative paths and reaction combinations. By reducing the operational barrier to accessing and analyzing synthetic routes from multiple sources, LinChemIn facilitates a constructive interplay between artificial intelligence and human expertise. Computer science ; Python (programming language) ; Consistency (knowledge bases) ; Cheminformatics ; Synthetic data ; Domain (mathematical analysis) ; Data mining ; Artificial intelligence ; Machine learning ; Programming language ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Gyoung S. Na | Hyun Woo Kim
Source: Applied Soft Computing (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Metaheuristics-guided active learning for optimizing reaction conditions of high-performance methane conversion Converting greenhouse gases into value-added chemical compounds has been widely studied in chemical science and engineering for sustainable industry. In particular, nonoxidative coupling of methane (NOCM) that transforms methane into useful chemical compounds has received great interests because methane is a naturally abundant greenhouse gas. NOCM has received significant attention in sustainable industry for addressing the climate crisis. However, there are complex optimization problems in maximizing the efficiency of the nonoxidative methane conversion. Although conventional data-driven methods have been successfully applied to various engineering problems, a data-driven optimization of NOCM remains a challenging problem because expensive chemical experiments should be performed to collect prior data for model training. To avoid the expensive costs of chemical experiments, we propose an active learning method that performs training data augmentation and model re-training without pre-defined unlabeled experimental data. To this end, we combine active learning with metaheuristic algorithms to perform active learning with statistically augmented data. We applied the proposed method to a high-throughput screening task to discover new reaction conditions of high-performance NOCM, and the high-throughput screening error was significantly reduced by 69.11%. Methane ; Computer science ; Greenhouse gas ; Active learning (machine learning) ; Metaheuristic ; Biochemical engineering ; Process engineering ; Machine learning ; Artificial intelligence ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Process Optimization and Integration
Authors: Yi-Fan Hou | Quanhao Zhang | Pavlo O. Dral
Source: The Journal of Organic Chemistry (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Surprising Dynamics Phenomena in the Diels–Alder Reaction of C <sub>60</sub> Uncovered with AI We performed an extensive artificial intelligence-accelerated quasi-classical molecular dynamics investigation of the time-resolved mechanism of the Diels-Alder reaction of fullerene C<sub>60</sub> with 2,3-dimethyl-1,3-butadiene. In a substantial fraction (10%) of reactive trajectories, the larger C<sub>60</sub> noncovalently attracts the 2,3-dimethyl-1,3-butadiene long before the barrier so that the diene undergoes the series of complex motions including roaming, somersaults, twisting, and twisting somersaults around the fullerene until it aligns itself to pass over the barrier. These complicated processes could be easily missed in typically performed quantum chemical simulations with shorter and fewer trajectories. After the barrier is passed, the bonds take longer to form compared to the simplest prototypical Diels-Alder reaction of ethene with 1,3-butadiene despite high similarities in transition states and barrier widths evaluated with intrinsic reaction coordinate (IRC) calculations. C<sub>60</sub> is mainly responsible for these differences as its reaction with 1,3-butadiene is similar to the reaction with 2,3-dimethyl-1,3-butadiene: the only substantial difference being that the extra methyl groups double the probability of the prolonged alignment phase in dynamics. These additional calculations of C<sub>60</sub> with 1,3-butadiene could be performed via active learning more easily by reusing the data generated for the other two reactions, showing the potential for larger-scale exploration of the effects of different substrates in the same types of reactions. Alder ; Diels–Alder reaction ; Dynamics (music) ; Chemistry ; Sociology ; Organic chemistry ; Biology ; Ecology ; Catalysis Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Advanced Chemical Physics Studies
Authors: Viswanath G. Akkili | Jongchan Yoon | Kihyun Shin | Jeong Sanghyun | Ji‐Yun Moon | Jun‐Hui Choi | Seung-Il Kim | Ashish A. Patil | Frederick Aziadzo | Jeongbeen Kim | Su-Hyeon Kim | Dong‐Wook Shin | Jung‐Sub Wi | Hoon‐Hwe Cho | Joon Sik Park | Eui‐Tae Kim | Dong‐Eun Kim | Jaeyeong Heo | Graeme Henkelman | Kostya S. Novoselov | Choong‐Heui Chung | Jae‐Hyun Lee | Zonghoon Lee | Sangyeob Lee
Source: ACS Nano (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Amorphous Carbon Monolayer: A van der Waals Interface for High-Performance Metal Oxide Semiconductor Devices Ultrasmall-scale semiconductor devices (≤5 nm) are advancing technologies, such as artificial intelligence and the Internet of Things. However, the further scaling of these devices poses critical challenges, such as interface properties and oxide quality, particularly at the high-<i>k</i>/semiconductor interface in metal-oxide-semiconductor (MOS) devices. Existing interlayer (IL) methods, typically exceeding 1 nm thickness, are unsuitable for ultrasmall-scale devices. Here, we propose a one-atom-thick amorphous carbon monolayer (ACM) as the IL to address these issues for MOS devices. ACM is disordered, randomly arranged, and short of long-range periodicity with <i>sp</i><sup>2</sup> hybridized carbon network, offering impermeability, van der Waals (vdW) bonding, insulating behavior, and effective seeding layer. With these advantages, we have utilized ACM vdW IL (vIL) in Al<sub>2</sub>O<sub>3</sub>/H-Ge MOS capacitors. The interface trap density was suppressed by ∼2 orders of magnitude to 7.21 × 10<sup>10</sup> cm<sup>-2</sup> eV<sup>-1</sup>, with no frequency-dependent flat band shift. The slow trap density is decreased to 2 orders of magnitude, and the <i>C</i>-<i>V</i> hysteresis width is minimized by >75%, indicating enhanced oxide quality. These results are supported by high-resolution transmission electron microscopy and energy dispersive X-ray spectroscopy analysis, confirming the creation of an atomically well-defined interface in the Al<sub>2</sub>O<sub>3</sub>/H-Ge heterojunction with ACM vIL, even under high-temperature annealing conditions. Density functional theory calculations further clarify that ACM vIL preserves the hydrogen-passivated Ge surface without altering its electronic band structure. These results demonstrate that ACM vIL effectively improves the interface properties and enhances the oxide quality, enabling further advancements in ultrasmall-scale MOS devices. Monolayer ; Materials science ; van der Waals force ; Nanotechnology ; Semiconductor ; Interface (matter) ; Oxide ; Carbon fibers ; Silicon ; Engineering physics ; Optoelectronics ; Chemistry ; Molecule ; Physics ; Metallurgy ; Wetting Graphene research and applications ; Diamond and Carbon-based Materials Research ; Semiconductor materials and devices
Authors: Dasha Rodina | Jakub Vaith | Shauna M. Paradine
Source: Nature Communications (article, 2024-06-26)
Field: Chemistry — Organic Chemistry

Ligand control of regioselectivity in palladium-catalyzed heteroannulation reactions of 1,3-Dienes Olefin carbofunctionalization reactions are indispensable tools for constructing diverse, functionalized scaffolds from simple starting materials. However, achieving precise control over regioselectivity in intermolecular reactions remains a formidable challenge. Here, we demonstrate that using PAd<sub>2</sub><sup>n</sup>Bu as a ligand enables regioselective heteroannulation of o-bromoanilines with branched 1,3-dienes through ligand control. This approach provides regiodivergent access to 3-substituted indolines, showcasing excellent regioselectivity and reactivity across a range of functionalized substrates. To gain further insights into the origin of selectivity control, we employ a data-driven strategy, developing a linear regression model using calculated parameters for phosphorus ligands. This model identifies four key parameters governing regioselectivity in this transformation, paving the way for future methodology development. Additionally, density functional theory calculations elucidate key selectivity-determining transition structures along the reaction pathway, corroborating our experimental observations and establishing a solid foundation for future advancements in regioselective olefin difunctionalization reactions. Regioselectivity ; Olefin fiber ; Ligand (biochemistry) ; Combinatorial chemistry ; Chemistry ; Intermolecular force ; Catalysis ; Organic chemistry ; Molecule Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Sulfur-Based Synthesis Techniques
Authors: Abhijit Chatterjee
Source: The Journal of Chemical Physics (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

Unraveling the collinearity in short-range order parameters for lattice configurations arising from topological constraints In multicomponent lattice problems, for example, in alloys and at crystalline surfaces and interfaces, atomic arrangements exhibit spatial correlations that dictate the kinetic and thermodynamic phase behavior. These correlations emerge from interparticle interactions and are frequently reported in terms of the short-range order (SRO) parameter. Expressed usually in terms of pair distributions and other cluster probabilities, the SRO parameter gives the likelihood of finding atoms/molecules of a particular type in the vicinity of other atoms. This study focuses on fundamental constraints involving the SRO parameters that are imposed by the underlying lattice topology. Using a data-driven approach, we uncover the interrelationships between different SRO parameters (e.g., pairs, triplets, and quadruplets) on a lattice. The main finding is that while some SRO parameters are independent, the remaining are collinear, i.e., the latter are dictated by the independent ones through linear relationships. A kinetic and thermodynamic modeling framework based on these constraints is introduced. Collinearity ; Lattice (music) ; Statistical physics ; Kinetic energy ; Quadruplets ; Cluster (spacecraft) ; Topology (electrical circuits) ; Physics ; Range (aeronautics) ; Computer science ; Mathematics ; Quantum mechanics ; Materials science ; Combinatorics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Corrosion Behavior and Inhibition
Authors: Luigi Da Vià | Matthias Depoortere | Robert D. Willacy | Alastair J. Roberts | Pandian Sokkar | Mathieu Fossépré | A. Agnel Ruba | Magdalena Zwierzyna
Source: Organic Process Research & Development (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Enabling Data-Driven Solubility Modeling at GSK: Enhancing Purge Predictions for Mutagenic Impurities In the pharmaceutical industry, solubility is a critical parameter influencing various stages of drug development, from early discovery to commercial manufacturing. This work showcases a high-throughput solubility screening workflow and describes the steps required to standardize and curate data suitably to allow automated data flow. Using the high-quality data, we developed a quantitative structure–property relationship model using gradient boosting and molecular descriptors, requiring only a 2D molecular structure to generate predictions. The accuracy of the model is competitive with alternative approaches where additional physical data is not required. A key use case for solubility predictions made in this way is in developing control strategies for mutagenic impurities, allowing for a data-driven and consistent method for calculating the solubility contribution to purge calculations. Further perspective is given on the future of the application of the model as a solubility prediction algorithm and on the approach to data-driven methodologies supporting drug development in general, highlighting the potential for federated learning approaches which use technological approaches to overcome the barrier to cross-industry data sharing. Solubility ; Purge ; Impurity ; Chemistry ; Organic chemistry ; Waste management ; Engineering Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Rico Friedrich | Stefano Curtarolo
Source: The Journal of Chemical Physics (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

AFLOW-CCE for the thermodynamics of ionic materials Accurate thermodynamic stability predictions enable data-driven computational materials design. Standard density functional theory (DFT) approximations have limited accuracy with average errors of a few hundred meV/atom for ionic materials, such as oxides and nitrides. Thus, insightful correction schemes as given by the coordination corrected enthalpies (CCE) method, based on an intuitive parametrization of DFT errors with respect to coordination numbers and cation oxidation states, present a simple, yet accurate solution to enable materials stability assessments. Here, we illustrate the computational capabilities of our AFLOW-CCE software by utilizing our previous results for oxides and introducing new results for nitrides. The implementation reduces the deviations between theory and experiment to the order of the room temperature thermal energy scale, i.e., ∼25 meV/atom. The automated corrections for both materials classes are freely available within the AFLOW ecosystem via the AFLOW-CCE module, requiring only structural inputs. Parametrization (atmospheric modeling) ; Ionic bonding ; Density functional theory ; Stability (learning theory) ; Atom (system on chip) ; Nitride ; Thermal stability ; Thermodynamics ; Standard enthalpy of formation ; Statistical physics ; Materials science ; Computer science ; Applied mathematics ; Physical chemistry ; Chemistry ; Computational chemistry ; Physics ; Mathematics ; Ion ; Nanotechnology ; Quantum mechanics Machine Learning in Materials Science ; Ionic liquids properties and applications ; Thermal and Kinetic Analysis
Authors: Surbhi Agarwal | D. K. Dwivedi | Pooja Lohia | Manoj Kumar Gupta
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Yttrium-induced tunable bandgap for optical data storage applications Phase-change memory (PCM) relies on the characteristics of phase-change materials that exhibit slow resistance state changes and enable multilevel operation with minimal resistance drift. They are emerging as promising candidates for artificial intelligence applications inspired by neuroscience and require high volumes of data. However, achieving the necessary qualities, such as thermal stability and fast operation speed, simultaneously is still a major obstacle for PCM materials. The present study investigated the linear and nonlinear optical and electronic properties of Te<sub>(1-<i>x</i>)</sub>(GeSe<sub>0.5</sub>)Y <sub><i>x</i></sub> (<i>x</i> = 0.05, 0.1, 0.15) thin films deposited <i>via</i> a thermal evaporation technique by structural characterization (using XRD), surface morphology analysis (using SEM), and elemental composition analysis (using EDX). Transmission spectra ranging from 500 to 2500 nm were obtained using a UV-visible spectrophotometer to determine the optical properties. The refractive index (<i>n</i>) and extinction coefficient (<i>k</i>) were also determined, and Tauc's relationship was applied to assess the optical absorption data. The absorption coefficient (<i>α</i>) was determined utilizing the Urbach relation. The Wemple-DiDomenico model was employed to calculate the nonlinear refractive index. Furthermore, the dielectric properties, loss tangent, and surface/volume energy loss functions were determined. The optical energy bandgap of the thin films revealed the allowed indirect transitions. The observed enhancement of the optical parameters suggested that the investigated composition is appropriate for different photonic applications. Yttrium ; Ranging ; Materials science ; Band gap ; Nonlinear optical ; 3D optical data storage ; Optoelectronics ; Transmission (telecommunications) ; Spectral line ; Thin film ; Optical spectra ; Optics ; Analytical Chemistry (journal) ; Nonlinear system ; Chemistry ; Nanotechnology ; Physics ; Telecommunications ; Computer science Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Nonlinear Optical Materials Research