jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peng Zhang | Qian‐Cheng Luo | Zhenhua Zhu | Wanrong He | Nan Song | Junting Lv | Xuning Wang | Quan‐Guo Zhai | Yan‐Zhen Zheng | Jinkui Tang
Source: Angewandte Chemie International Edition (article, 2023-01-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Radical‐Bridged Heterometallic Single‐Molecule Magnets Incorporating Four Lanthanoceniums The syntheses and magnetic properties of organometallic heterometallic compounds [K(THF)<sub>6</sub> ]{Co<sup>I</sup> [(μ<sub>3</sub> -HAN)RE<sub>2</sub> Cp*<sub>4</sub> ]<sub>2</sub> } (1-RE) and [K(Crypt)]<sub>2</sub> {Co<sup>I</sup> [(μ<sub>3</sub> -HAN)RE<sub>2</sub> Cp*<sub>4</sub> ]<sub>2</sub> } (2-RE) containing hexaazatrinaphthylene radicals (HAN⋅<sup>3-</sup> ) and four rare earth (RE) ions are reported. 1-RE shows isolable species with ligand-based mixed valency as revealed by cyclic voltammetry (CV) thus leading to the isolation of 2-RE via one-electron chemical reduction. Strong electronic communication in mixed-valency supports stronger overall ferromagnetic behaviors in 2-RE than 1-RE containing Gd and Dy ions. Ac magnetic susceptibility data reveal 1-Dy and 2-Dy both exhibit slow magnetic relaxation. Importantly, larger coercive field was observed in the hysteresis of 2-Dy at 2.0 K, indicating the enhanced SMM behavior compared with 1-Dy. Ligand-based mixed-valency strategy has been used for the first time to improve the magnetic coupling in lanthanide (Ln) SMMs, thus opening up new ways to construct strongly coupled Ln-SMMs. Valency ; Crystallography ; Ferromagnetism ; Chemistry ; Lanthanide ; Coercivity ; Ion ; Ligand (biochemistry) ; Dysprosium ; Magnetic susceptibility ; Magnetization ; Materials science ; Inorganic chemistry ; Magnetic field ; Condensed matter physics ; Physics ; Organic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Organic and Molecular Conductors Research
Authors: Jian Luo | Qile Wu | Lin Zhou | Weixi Lu | Wenxing Yang | Jia Zhu
Source: Photonics Insights (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmon-induced hot carrier dynamics and utilization Plasmonics has aroused tremendous interest in photophysics, nanophotonics, and metamaterials.The extreme field concentration of plasmonics offers the ultimate spatial and temporal light control, singleparticle detection, and optical modulation.Plasmon decay of metal nanostructures into hot carriers extends the application into photocatalysis, photodetectors, photovoltaics, and ultrafast nanooptics.The generated hot electron-hole pairs are transferred into adjacent dielectrics, well known to be more efficient than the hot carrier generation in dielectrics by direct photoexcitations.However, plasmon-induced hot-carrier-based devices are far from practical applications due to the low quantum yield of hot carrier extraction.Emergent challenges include low hot carrier generation efficiency in metals, rapid energy loss of hot carriers, and severe charge recombination at the metal/dielectric interface.In this review, we provide a fundamental insight into the hot carrier generation, transport, injection, and diffusion into dielectrics based on the steady-state and time-resolved spectroscopic studies as well as theoretical calculations.Strategies to enhance hot carrier generation in metals and electron transfer into dielectrics are discussed in detail.Then, applications based on hot carrier transfer are introduced briefly.Finally, we provide our suggestions on future research endeavors.We believe this review will provide a valuable overall physical picture of plasmon-induced hot carrier applications for researchers. Plasmon ; Dynamics (music) ; Hot electron ; Materials science ; Nanotechnology ; Computer science ; Engineering physics ; Optoelectronics ; Physics ; Nuclear physics Gold and Silver Nanoparticles Synthesis and Applications ; Nanowire Synthesis and Applications ; Plasmonic and Surface Plasmon Research
Authors: Ana Tomić | Olja Šovljanski | Tamara Erceg
Source: Antibiotics (review, 2023-09-21)
Field: Materials Science — Biomaterials

Insight on Incorporation of Essential Oils as Antimicrobial Substances in Biopolymer-Based Active Packaging The increasing interest in microbiological food safety requires the development of sensitive and reliable analyses and technologies for preserving food products' freshness and quality. Different types of packaging systems are one of the solutions for controlling microbiological activity in foods. During the last decades, the development of biopolymer-based active packaging with essential oil incorporation systems has resulted in technologies with exceptional application potential, primarily in the food industry. There is no doubt that this principle can facilitate food status monitoring, reduce food waste, extend the shelf life, improve the overall quality of food, or indicate a larger problem during the storage, production, and distribution of foodstuffs. On the other hand, most antimicrobial packaging systems are in the development phase, while the sensitivity, selectivity, complexity, and, above all, safety of these materials are just some of the essential questions that need to be answered before they can be widely used. The incorporation of essential oils as antimicrobial substances in biopolymer-based active packaging holds significant promise for enhancing food safety, extending shelf life, and offering more sustainable packaging solutions. While challenges exist, ongoing research and innovation in this field are likely to lead to the development of effective and environmentally friendly packaging systems with enhanced antimicrobial properties. Active packaging ; Biopolymer ; Food packaging ; Biochemical engineering ; Food safety ; Shelf life ; Antimicrobial ; Food industry ; Environmentally friendly ; Essential oil ; Biotechnology ; Business ; Quality (philosophy) ; Food quality ; Risk analysis (engineering) ; Food science ; Chemistry ; Engineering ; Biology Nanocomposite Films for Food Packaging ; Essential Oils and Antimicrobial Activity ; Medicinal Plants and Neuroprotection
Authors: Clarisse Fournier | Sébastien Roux | Kenji Watanabe | Takashi Taniguchi | Stéphanie Buil | Julien Barjon | Jean‐Pierre Hermier | Aymeric Delteil
Source: Physical Review Applied (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

Two-Photon Interference from a Quantum Emitter in Hexagonal Boron Nitride Indistinguishable single photons are a key requirement for most applications in optical quantum information. However, this feature has remained elusive among two-dimensional materials, primarily due to insufficient coherence of emitters. Here the authors controllably generate a spectrally narrow and stable single-photon source in an h-BN crystal by electron-beam irradiation, and demonstrate Hong-Ou-Mandel interference of the emitted photons, which is a signature of indistinguishability. These results enable practical applications in integrated quantum photonics based on two-dimensional materials. Hexagonal boron nitride ; Common emitter ; Photon ; Interference (communication) ; Optoelectronics ; Materials science ; Quantum interference ; Quantum ; Hexagonal crystal system ; Optics ; Physics ; Nanotechnology ; Quantum mechanics ; Telecommunications ; Chemistry ; Computer science ; Graphene ; Crystallography Graphene research and applications ; 2D Materials and Applications ; Diamond and Carbon-based Materials Research
Authors: M.K.G. Abbas | S. Ramesh | Sara Faiz Hanna Tasfy | K.Y. Sara Lee
Source: Materials Today Communications (review, 2023-08-27)
Field: Materials Science — Ceramics and Composites

A state-of-the-art review on alumina toughened zirconia ceramic composites Alumina-zirconia composites (ATZs) are a class of advanced ceramics that have attracted significant attention due to their excellent mechanical properties. These properties make ATZs suitable for a wide range of applications. This review provides a comprehensive overview of the current state of understanding of ATZs. The review begins with a brief introduction to the properties of zirconia and alumina ceramics. The different methods for combining zirconia and alumina ceramics are then discussed. The effects of sintering conditions on the tetragonal phase stability, densification, mechanical properties, evolution of microstructural and grain size of the alumina toughened zirconia are also presented. Previous studies have reached a consensus on the influence of sintering temperature, sintering time, and alumina content on the properties of ATZ composites. Generally, elevating sintering temperatures and prolonging sintering times result in heightened densities and improved mechanical properties. However, the impact of alumina content is less straightforward, as divergent findings exist, some studies indicate that higher alumina contents correspond to diminished properties, while others reveal no significant effect. Additionally, the sintering method emerges as a pivotal determinant impacting the properties of ATZ ceramics. Notably, microwave sintering has demonstrated superiority over traditional methods, yielding heightened properties. Optimal density and mechanical properties of ATZ composites can be achieved through meticulous control of sintering conditions. By comprehending the factors influencing ATZ, the production of ATZ composites tailored to specific applications becomes attainable, offering the prospect of desired properties. Sintering ; Materials science ; Cubic zirconia ; Ceramic ; Composite material ; Alumina ceramic ; Phase (matter) Advanced ceramic materials synthesis ; Microwave Dielectric Ceramics Synthesis ; Nuclear materials and radiation effects
Authors: Sunghwan Choi
Source: Nature Communications (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Prediction of transition state structures of gas-phase chemical reactions via machine learning The elucidation of transition state (TS) structures is essential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite significant advances in computational approaches, TS searching remains a challenging problem owing to the difficulty of constructing an initial structure and heavy computational costs. In this paper, a machine learning (ML) model for predicting the TS structures of general organic reactions is proposed. The proposed model derives the interatomic distances of a TS structure from atomic pair features reflecting reactant, product, and linearly interpolated structures. The model exhibits excellent accuracy, particularly for atomic pairs in which bond formation or breakage occurs. The predicted TS structures yield a high success ratio (93.8%) for quantum chemical saddle point optimizations, and 88.8% of the optimization results have energy errors of less than 0.1 kcal mol<sup>-1</sup>. Additionally, as a proof of concept, the exploration of multiple reaction paths of an organic reaction is demonstrated based on ML inferences. I envision that the proposed approach will aid in the construction of initial geometries for TS optimization and reaction path exploration. Saddle point ; Computer science ; Quantum chemical ; Gas phase ; Statistical physics ; Path (computing) ; Saddle ; Phase transition ; Transition state ; Chemical reaction ; Algorithm ; Chemistry ; Materials science ; Molecule ; Physics ; Mathematical optimization ; Thermodynamics ; Mathematics ; Physical chemistry ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Haoyang Fu | Ke Li | Chenfei Zhang | Jianghong Zhang | Jiyuan Liu | Xi Chen | Guoliang Chen | Yongyang Sun | Shuzhou Li | Lan Ling
Source: ACS Nano (article, 2023-07-13)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Optimization of a Single-Atom Coordination Environment for Accelerated Fenton Catalysis Machine learning (ML) algorithms will be enablers in revolutionizing traditional methods of materials optimization. Here, we broaden the use of ML to assist the construction of Fenton-like single-atom catalysts (SACs) by developing a methodology including model building, training, and prediction. Our approach can efficiently extract synthesis parameters that exert a substantial influence on Fenton activity and accurately predict the phenol degradation rate <i>k</i> of SACs with a mean error of ±0.018 min<sup>-1</sup>. The extended synthesis window with accelerated learning enables the realization that the heating temperatures during SAC synthesis significantly influence the Fe-N coordination number, which ultimately dictates their performance. Through ML-guided optimization, a highly efficient SAC dominated by Fe-N<sub>5</sub> sites with exceptional Fenton activity (<i>k</i> = 0.158 min<sup>-1</sup>) is identified. Our work provides an example for ML-assisted optimization of single-atom coordination environments and illuminates the feasibility of ML in accelerating the development of high-performance catalysts. Catalysis ; Atom (system on chip) ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Computer science ; Engineering ; Organic chemistry ; Embedded system Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Artur M. Schweidtmann | Jan G. Rittig | Jana M. Weber | Martin Grohe | Manuel Dahmen | Kai Leonhard | Alexander Mitsos
Source: Computers & Chemical Engineering (article, 2023-02-24)
Field: Materials Science — Materials Chemistry

Physical pooling functions in graph neural networks for molecular property prediction Pooling ; Generalization ; Computer science ; Molecular graph ; Artificial intelligence ; Function (biology) ; Machine learning ; Computation ; Graph ; Theoretical computer science ; Mathematics ; Algorithm ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Martin Fitzner | Georg Wuitschik | Raffael Koller | Jean‐Michel Adam | Torsten Schindler
Source: ACS Omega (article, 2023-01-11)
Field: Materials Science — Materials Chemistry

Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction Pd-catalyzed C-N couplings are commonplace in academia and industry. Despite their significance, finding suitable reaction conditions leading to a high yield, for instance, remains a challenging and time-consuming task which usually requires screening over many sets of conditions. To help select promising reaction conditions in the vast space of reagent combinations, machine learning is an emerging technique with a lot of promise. In this work, we assess whether the reaction yield of C-N couplings can be predicted from databases of chemical reactions. We test the generalizability of models both on challenging data splits and on a dedicated experimental test set. We find that, provided the chemical space represented by the training set is not left, the models perform well. However, the applicability domain is quickly left even for simple reactions of the same type, as, for instance, present in our plate test set. The results show that yield prediction for new reactions is possible from the algorithmic side but in practice is hindered by the available data. Most importantly, more data that cover the diversity in reagents are needed for a general-purpose prediction of reaction yields. Our findings also expose a challenge to this field in that it appears to be extremely deceiving to judge models based on literature data with test sets which are split off the same literature data, even when challenging splits are considered. Generalizability theory ; Yield (engineering) ; Set (abstract data type) ; Test set ; Computer science ; Machine learning ; Chemical space ; Task (project management) ; Artificial intelligence ; Test data ; Data set ; Field (mathematics) ; Space (punctuation) ; Applicability domain ; Simple (philosophy) ; Algorithm ; Mathematics ; Chemistry ; Engineering ; Materials science ; Statistics ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Graph Neural Networks
Authors: Simon Axelrod | Eugene I. Shakhnovich | Rafael Gómez‐Bombarelli
Source: ACS Central Science (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits <i>trans</i>-<i>cis</i> isomerism in response to light. The thermal half-life of the <i>cis</i> isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and trade-offs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology. Azobenzene ; Intersystem crossing ; Isomerization ; Photoswitch ; Computer science ; Thermal ; Photochemistry ; Chemistry ; Physics ; Molecule ; Organic chemistry ; Atomic physics ; Thermodynamics Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Computational Drug Discovery Methods
Authors: Xiaqiong Fan | Yue Wang | Chuanxiu Yu | Yuanxia Lv | Hailiang Zhang | Qiong Yang | Ming Wen | Hongmei Lü | Zhimin Zhang
Source: Analytical Chemistry (article, 2023-03-13)
Field: Chemistry — Analytical Chemistry

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios. Raman spectroscopy ; Pooling ; Artificial intelligence ; Principal component analysis ; Pattern recognition (psychology) ; Sensitivity (control systems) ; Identification (biology) ; Artificial neural network ; Chemistry ; Noise (video) ; Component (thermodynamics) ; Fingerprint (computing) ; Biological system ; Deep learning ; Flexibility (engineering) ; Data set ; Computer science ; Optics ; Statistics ; Image (mathematics) ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Eleonora Calì | Melonie P. Thomas | Rama K. Vasudevan | Ji Wu | Oriol Gavalda‐Diaz | Katharina Marquardt | Eduardo Saiz | Dragos Neagu | Raymond R. Unocic | Stephen C. Parker | Beth S. Guiton | David J. Payne
Source: Nature Communications (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Real-time insight into the multistage mechanism of nanoparticle exsolution from a perovskite host surface In exsolution, nanoparticles form by emerging from oxide hosts by application of redox driving forces, leading to transformative advances in stability, activity, and efficiency over deposition techniques, and resulting in a wide range of new opportunities for catalytic, energy and net-zero-related technologies. However, the mechanism of exsolved nanoparticle nucleation and perovskite structural evolution, has, to date, remained unclear. Herein, we shed light on this elusive process by following in real time Ir nanoparticle emergence from a SrTiO<sub>3</sub> host oxide lattice, using in situ high-resolution electron microscopy in combination with computational simulations and machine learning analytics. We show that nucleation occurs via atom clustering, in tandem with host evolution, revealing the participation of surface defects and host lattice restructuring in trapping Ir atoms to initiate nanoparticle formation and growth. These insights provide a theoretical platform and practical recommendations to further the development of highly functional and broadly applicable exsolvable materials. Nucleation ; Nanoparticle ; Materials science ; Nanotechnology ; Perovskite (structure) ; Chemical physics ; Oxide ; Chemistry ; Crystallography Electronic and Structural Properties of Oxides ; Quantum Dots Synthesis And Properties ; Ferroelectric and Piezoelectric Materials
Authors: Yue Liu | Shuangyan Wang | Zhengwei Yang | Maxim Avdeev | Siqi Shi
Source: Science Bulletin (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Auto-MatRegressor: liberating machine learning alchemists Computer science ; Machine learning ; Artificial intelligence ; Meta learning (computer science) ; Hyperparameter ; Property (philosophy) ; Model selection ; Domain knowledge ; Domain (mathematical analysis) ; Data mining ; Task (project management) ; Algorithm ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods