jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huixia Luo | Peifeng Yu | Guowei Li | Kai Yan
Source: arXiv (Cornell University) (preprint, 2023-09-07)
Field: Materials Science — Materials Chemistry

Topological Quantum Materials for Energy Conversion and Storage Topological quantum materials (TQMs) have symmetry protected band structures with useful electronic properties that have applications in information, sensing, energy, and other technologies. In the past 10 years, the applications of TQMs in the field of energy conversion and storage mainly including water splitting, ethanol electro-oxidation, battery, supercapacitor, and relative energy-efficient devices have attracted increasing attention. The novel quantum states in TQMs provide a stable electron bath with high conductivity and carrier mobility, long lifetime, and determined spin states, making TQMs an ideal platform for understanding the surface reactions and looking for highly efficient materials for energy conversion and storage. In this Perspective, we present an overview of the recent progress regarding topological quantum catalysis. We describe the open problems, and the potential applications of TQMs in water splitting, batteries, supercapacitors, and other prospects in energy conversion and storage. Supercapacitor ; Energy storage ; Energy transformation ; Quantum ; Topology (electrical circuits) ; Battery (electricity) ; Nanotechnology ; Field (mathematics) ; Materials science ; Engineering physics ; Physics ; Electrical engineering ; Quantum mechanics ; Power (physics) ; Engineering ; Electrode Graphene research and applications ; Catalytic Processes in Materials Science ; Nanocluster Synthesis and Applications
Authors: Nima Sedaghat | Brianna Smart | J. Bryce Kalmbach | Erin L. Howard | Hamidreza Amindavar
Source: arXiv (Cornell University) (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Stellar Karaoke: deep blind separation of terrestrial atmospheric effects out of stellar spectra by velocity whitening We report a study exploring how the use of deep neural networks with astronomical Big Data may help us find and uncover new insights into underlying phenomena: through our experiments towards unsupervised knowledge extraction from astronomical Big Data we serendipitously found that deep convolutional autoencoders tend to reject telluric lines in stellar spectra. With further experiments we found that only when the spectra are in the barycentric frame does the network automatically identify the statistical independence between two components, stellar vs telluric, and rejects the latter. We exploit this finding and turn it into a proof-of-concept method for removal of the telluric lines from stellar spectra in a fully unsupervised fashion: we increase the inter-observation entropy of telluric absorption lines by imposing a random, virtual radial velocity to the observed spectrum. This technique results in a non-standard form of ``whitening'' in the atmospheric components of the spectrum, decorrelating them across multiple observations. We process more than 250,000 spectra from the High Accuracy Radial velocity Planetary Search (HARPS) and with qualitative and quantitative evaluations against a database of known telluric lines, show that most of the telluric lines are successfully rejected. Our approach, `Stellar Karaoke', has zero need for prior knowledge about parameters such as observation time, location, or the distribution of atmospheric molecules and processes each spectrum in milliseconds. We also train and test on Sloan Digital Sky Survey (SDSS) and see a significant performance drop due to the low resolution. We discuss directions for developing tools on top of the introduced method in the future. Spectral line ; Sky ; Physics ; Radial velocity ; Stars ; Computer science ; Astronomical spectroscopy ; Astrophysics ; Astronomy ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Swarat Choudhuri Prabin Kumar Mahato
Source: Tuijin Jishu/Journal of Propulsion Technology (article, 2023-12-10)
Field: Materials Science — Materials Chemistry

Exploring the Versatility of Transition Metal-Infused Graphene/CdS Nanocomposites for Cutting-Edge Nanotechnology Applications: An Innovative Synergy Beyond Boundaries This review paper highlights the potential of graphene nanocomposites for various applications in nanotechnology and focuses on the properties and applications of the graphene/CdS nanocomposite, which has shown enhanced properties and finds applications in diverse industries. This work discusses the incorporation of transition metals such as Ni, Cu, Zn, Mn, and Fe into graphene/CdS nanocomposites and their resultant properties, including increased surface activities, optronics, mechanical properties, thermal stability, and photocatalytic activity. This review provides valuable insights into the potential of transition metal-doped graphene/CdS nanocomposites for a wide range of applications. Graphene ; Nanotechnology ; Materials science ; Nanocomposite ; Enhanced Data Rates for GSM Evolution ; Engineering physics ; Engineering Quantum Dots Synthesis And Properties
Authors: Anna Hartmann | Sascha Feldmann | A. WIDERA | S Samokhvalova | Jean‐François Lutz | Regina Palkovits | Daniel Chen | Xiang Zhang | X Xiao | Zhiqiang Wang | Jie Zhao | Víctor González | Ida Fazlic | Marine Cotte | Frederik Vanmeert | Arthur Gestels | Simon F. De Meyer | Fréderique Broers | J Hermans | Anne F. Van Loon | Katrien Janssens | Paula J. Noble | Katrien Keune | Ryota Shimotsukue | Kenta Fujii | Yuji Sato | Yoji Nagashima | Kenichi Tanaka | Rongpei Shi | Yonghui Wu | Xin Zhao | Jianming Guo | Z Li | Boxuan Zhao | Rong Peng | Chong Li | En Tao Wang | B Wang | Christopher H.K. Cheng | Kuo Liu | Zeyu Zhang | Lan Fang | Fan Wang | Yang Deng | Jiang Zhu | A Li | Alessandro Colombano | Luca Dalponte | Sergio Dall’Angelo | Carmine Clemente | Mohannad Idress | Ahmad Ghazal | Wael E. Houssen | Massimo Capone | Abhishek Sirohiwal | Massimiliano Aschi | Dimitrios Pantazis | Isabella Daidone | I Daidone | Zn-Ion Batteries | X Gao | Y Dai | C Zhang | Y Zhang | W Zong | W Zhang | R Chen | J Zhu | X Hu | M Wang | Z Du | F Guo | H Dong | Y Liu | H He | S Zhao | F Zhao | J Li | I Parkin | C Carmalt | G He* E | ; Shen | G Xu | X Lin | F Yang | H Yang | W Chen | Y Wu | X Wu | Q Cheng | Y Li | * Li | ; G.-H Chen | Y.-P He | Y Yu | H Lv | S Li
Source: Angewandte Chemie International Edition (article, 2023-04-03)
Field: Chemistry — Organic Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 16/2023 In their Research Article (e202216478), Victor Gonzalez et al. report the detection and distribution of lead(II) formate INT ; Formate ; Chemistry ; Computer science ; Organic chemistry ; Programming language ; Catalysis Axial and Atropisomeric Chirality Synthesis ; Molecular spectroscopy and chirality ; Crystal structures of chemical compounds
Authors: Andrew Rohskopf | James Goff | Dionysios Sema | Kiarash Gordiz | Ngoc Cuong Nguyen | Asegun Henry | Aidan P. Thompson | Mitchell Wood
Source: arXiv (Cornell University) (preprint, 2023-06-04)
Field: Materials Science — Materials Chemistry

Exploring Model Complexity in Machine Learned Potentials for Simulated Properties Machine learning (ML) enables the development of interatomic potentials that promise the accuracy of first principles methods while retaining the low cost and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks can map these descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of choice of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor LGPS. We further investigate how these errors affect simulated properties with these models and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. Since linear and nonlinear regression models have different advantages and disadvantages, the results presented herein help researchers choose models for their particular application. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Such observations can be repeated by other researchers to determine the level of accuracy, and hence model complexity, needed for their particular systems of interest. Computer science ; Nonlinear system ; Property (philosophy) ; Atom (system on chip) ; Algorithm ; Energy (signal processing) ; Interatomic potential ; Linear regression ; Artificial intelligence ; Machine learning ; Statistical physics ; Mathematics ; Molecular dynamics ; Physics ; Chemistry ; Computational chemistry ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Anna Hartmann | Gavin Tan | Frank Glorius | Stereochemical Minireviews | Vanha N. Pham | Chawnshang Chang | Larissa Bessler | Lea‐Marie Vogt | Marc Lander | Cynthia Magro | P. Keller | Jonas Kühlborn | Jian Lin | Liuping Wang | Qing Xie | Qiong Luo | D.-L Peng | Claud Mullins | Anita Heller | Kamyar Yazdani | D Jordan | Mengliu Yang | Christopher R. Fullenkamp | D Calabrese | Rob Boer | Thomas A. Hilimire | Thomas J. Allen | Rahela Ambaras Khan | John S. Schneekloth | J Schneekloth | A Optoelectronics | P Murali | S Nayak | S Das | * Senanayak | ; Venkatasubbaiah* E | X.-R Liu | S Mao | Z.-Y Song | Y Chen | L.-P Wu | * Xu | ; Y.-X Wang | J.-H Zhao | P Wen | P.-F Li | S.-N Zhang | D.-C Wang | J.-M Li | Y.-W Dou | * H.-Y Li | * Ma | J Xu* E ; | D Gonalves | T Promlok | S Ivanov | T Tao | S.-M Rheinberger | Y Jo | R Yu | M Graf | D Wagner | F Crespy | L Wurm | S Da Silva | * K. ; W.-Q Jiang | Z.-S Zhang | J Li | * Z.-M Mcgrady
Source: Angewandte Chemie International Edition (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 11/2023 Covalent Organic FrameworksIn their Communication (e202217103), Xiao Feng et al. report a2 Dcovalent organic framework with alternatively arranged hydrophilic/hydrophobics egments for water harvesting with fast kinetics and low regeneration temperature. INT ; Kinetics ; Covalent bond ; Chemistry ; Chemical engineering ; Computer science ; Organic chemistry ; Physics ; Engineering ; Operating system Covalent Organic Framework Applications ; Conducting polymers and applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: R. A. Mohamed | M.M. El-Nahass | M. Y. El-Bakry | El‐Sayed A. El‐Dahshan | E. H. Aamer | D. M. Habashy
Source: Research Square (preprint, 2023-11-07)
Field: Materials Science — Polymers and Plastics

Investigation of experimental for optical properties of molybdenum trioxide (MoO3) thin films using neural networks <title>Abstract</title> Nowadays, transition metal oxide (TMO), the semi-conductor molybdenum trioxide (MoO<sub>3</sub>) is under investigation. MoO<sub>3</sub> is a good choice because of its many industrial applications and alluring qualities. It has a significant role as an industrial catalyst and has electrochromic properties. The first part is an experimental study of the optical behaviour of the above mentioned thin film onto various substrates. X-ray diffraction (XRD) analysis revealed an orthorhombic structure. Measurements were obtained of the average crystallite size and dislocation density, which were 132.8 nm and 5.7* 10<sup>− 4</sup> nm<sup>-2</sup>, respectively. With the use of spectrophotometric measurements of transmittance (T) and reflectance (R) at normal incidence in the 300–1400 nm wavelength range at 310 nm thickness, the optical behaviour of the mentioned film onto various substrates was examined. The dispersion and oscillator energies were measured using the single oscillator model employing a Wemple–DiDomenico relation and indirect optical transitions with corresponding h ע energy gaps were identified for glass and quartz substrates. The second part introduces estimation optical behaviour of the above mentioned thin film using ANN model. Experimental data is used as inputs. The ANN modelling outputs optical characterization provide excellent results. Error values support the success of the modelling process (MSE &gt; 10<sup>− 1</sup>). Moreover, the theoretical equation describing the experimental results was obtained depending on the ANN model which represents the relation between the inputs and outputs. According to this research, the ANN model can be utilized as an efficient tool to simulate and predict the optical parameters of the above mentioned film. Additionally, it can establish strong connections between theoretical and experimental fields. Materials science ; Crystallite ; Molybdenum ; Molybdenum trioxide ; Thin film ; Electrochromism ; Transmittance ; Diffraction ; Optoelectronics ; Optics ; Nanotechnology ; Chemistry ; Physical chemistry ; Metallurgy Transition Metal Oxide Nanomaterials ; Analytical Chemistry and Sensors ; Conducting polymers and applications
Authors: Xinyi Chen | Bin Ai
Source:  (article, 2023-10-11)
Field: Chemistry — Spectroscopy

Chiroptical properties in rotated nanohole arrays Chiral nanohole arrays (NAs) are of significant research interest for their label-free enantiodiscrimination of biomolecules and drug compounds, even at the picogram level. This study systematically explored the impact of various parameters on chiral optical responses of NAs, enhancing our understanding of underlying mechanisms and optimization strategies. We designed dual-layer nanohole arrays with 3600 elements each, alternating Ag and Au layers. We manipulated incident angles (&Delta;<i>&theta;</i>, <i>&theta;</i><sub>1</sub>) and azimuthal angles (&Delta;<i>&phi;</i>, <i>&phi;</i><sub>1</sub>) using shadow sphere lithography (SSL) and introduced SiO<sub>2</sub> between the Au and Ag layers to enhance the response. MATLAB generated the NAs, subsequently simulated using the finite-difference time-domain (FDTD) program. Findings revealed central symmetry in circular dichroism (CD) value changes concerning &Delta;&theta; and &Delta;&phi;, with a more pronounced effect than variations in <i>&theta;</i><sub>1</sub> and <i>&phi;</i><sub>1</sub>. The inclusion of SiO<sub>2</sub> led to a notable 118% increase in the maximum |CD|<sub>max</sub> value, reaching 6.50&deg; for the 100 nm sandwiched NA (SNA150) with a radius of <i>r</i> = 150 nm. The maximum |g-factor| of the Ag-Au SNA150 increased with the <i>r</i>. An efficient model categorized NAs with similar CD responses, reducing simulations to 498 based on mirror, symmetry and a 60&deg; differential rotation property. This research provides a valuable resource for future machine learning analyses and predictions across diverse structural configurations, significantly advancing applications such as the detection of weak chiral optical molecules or proteins, compact polarization converters, and label-free chiral sensors, fostering innovation in nanohole array technology, particularly in biomedicine and optoelectronics. Finite-difference time-domain method ; RADIUS ; Physics ; Circular dichroism ; Anisotropy ; Optics ; Symmetry (geometry) ; Crystallography ; Geometry ; Molecular physics ; Chemistry ; Computer science ; Mathematics Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Advanced biosensing and bioanalysis techniques
Authors: Yangen Zhou | Zhongliang Tian | Yanqing Lai | Guihua Yu | Al Report | Anna Hartmann | Hanna Peleg | A Milo | C.-W Chu | Almas Nisaul Fauziah | Li‐Yi Yeh | S. Han | Abudukadi Tudi | W Zhang | X. T. Hou | Zhihua Yang | Shirui Pan | Z Li | Min Xie | Hui Cheng | Tianfu Wang | Jie Chen | Yanqin Lu | Jie Li | Jens Koßmann | Kai Zeng | K. Zhang | B Wang | Christian Weinberger | Markus Antonietti | Mateusz Odziomek | Nieves López‐Salas | Frank Rosowski | Yutao Li | F Huo | Limei Chen | Han Wang | Jizhou Wu | Pengfei Zhang | Ning Feng | Wei Li | Li Wang | Yuzhong Wang | X Wang | Xinyu Yang | Zunming Lu | Y. J. Mao | Chuan Yan | Lu Ding | Heongkyu Ju | Na-S Batteries | Rong Bai | Qing Lin | Xiaomian Li | Ling Feng | Shuangshuang Tan | Hu Liu | Min Ma | Xiaohu Wu | Yujun Shao | Rui Xie | Emily Hu | Yuxiang Yao | Yong Yu | S Cobb | Andhe Dharani | Ana Oliveira | Isabel Pereira | Erwin Reisner | Cheng Peng | Siyi He | Chen‐Yu Zhang | Linquan Bai | Zelin Hu | Chun Hu | Shuang‐Nan Zhang | Ying Yu | Y. Zhang | Linheng Li | Hehua Zhang | Ni Li | Shuzhen Shi | Simin Liu | Liang Hao | T Liu | Haoran Huang | K Li | D Jordan | Li Lin | Sherri McCarthy | John S. Schneekloth | Liliya A. Yatsunyk | L Yatsunyk | J Catalysis | L Zhou | S Meng | B Lin | J Cai | ; Wang | S Goodman
Source: Angewandte Chemie International Edition (article, 2023-06-16)
Field: Materials Science — Materials Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 26/2023 a nonequilibrium precipitation approach for the construction of a highly disordered crystal structure of layered double hydroxides as a model oxygen evolution catalyst. Non-equilibrium thermodynamics ; Precipitation ; Layered double hydroxides ; INT ; Catalysis ; Oxygen ; Chemistry ; Materials science ; Thermodynamics ; Chemical engineering ; Crystallography ; Physics ; Computer science ; Engineering ; Meteorology ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Polyoxometalates: Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Angela F. Harper | Kamil Iwanowski | Witt, William C. | M. C. Payne | Michele Simoncelli
Source: arXiv (Cornell University) (preprint, 2023-03-15)
Field: Materials Science — Materials Chemistry

Vibrational and thermal properties of amorphous alumina from first principles Amorphous alumina is employed ubiquitously as a high-dielectric-constant material in electronics, and its thermal-transport properties are of key relevance for heat management in electronic chips and devices. Experiments show that the thermal conductivity of alumina depends significantly on the synthesis process, indicating the need for a theoretical study to elucidate the atomistic origin of these variations. Here we employ first-principles simulations to characterize the atomistic structure, vibrational properties, and thermal conductivity of alumina at densities ranging from 2.28 g/cm3 to 3.49 g/cm3. Moreover, using an interatomic potential trained on first-principles data, we investigate how system size affects predictions of the thermal conductivity, showing that simulations containing 120 atoms can already reproduce the bulk limit of the conductivity. Finally, relying on the recently developed Wigner formulation of thermal transport, we shed light on the interplay between atomistic topological disorder and anharmonicity in the context of heat conduction, showing that the former dominates over the latter in determining the conductivity of alumina. Thermal conductivity ; Anharmonicity ; Thermal conduction ; Materials science ; Amorphous solid ; Context (archaeology) ; Dielectric ; Thermal ; Interatomic potential ; Condensed matter physics ; Molecular dynamics ; Chemical physics ; Thermodynamics ; Chemistry ; Composite material ; Physics ; Computational chemistry ; Optoelectronics ; Crystallography Thermal properties of materials ; Ferroelectric and Piezoelectric Materials ; Advanced Thermoelectric Materials and Devices
Authors: Joseph R. H. Manning | Lev Sarkisov
Source: ChemRxiv (preprint, 2023-06-09)
Field: Materials Science — Materials Chemistry

Unveiling the Synthesis Patterns of Nanomaterials: A Text Mining and Meta-Analysis Approach with ZIF-8 as a Case Study With the continuously growing number of scientific articles on synthesis of nanomaterials, it becomes impossible for researchers to grasp and comprehend the landscape of synthetic protocols available for a particular material. The aim of this study is to explore the feasibility of extracting the collective knowledge on synthesis of a particular material accumulated over the years from the published corpus of articles and organizing it in a systematic manner. Accordingly, we developed methods to perform detailed text mining on a single nanomaterial target for the purposes of methodology optimisation. Taking the common material ZIF-8 as a case study, we analysed 1600 synthesis protocols to identify trends in parameters, such as reagents, concentrations, and reaction time/temperature. We used this information to find the distribution of synthesis parameters and their relationships to one another, identifying the limits of common reaction parameters and revealing subtle details, such as insolubility of metal acetate reagents in alcoholic solvents, or the occurrence of amorphous oxides at low stoichiometric ratios. We then clustered similar synthesis protocols together, using their relative popularity to identify promising regions of the synthesis phase space for optimisation, reducing the need for brute force synthesis optimisation. The techniques developed here are a general tool accelerating the synthesis development of a wide range of nanomaterials by aggregating existing research trends, averting the need for laborious manual comparison of existing synthesis protocols or repetition of previously-developed techniques. Computer science ; Protocol (science) ; GRASP ; Nanomaterials ; Popularity ; Data science ; Nanotechnology ; Materials science ; Software engineering Machine Learning in Materials Science
Authors: Sergi Bernaus Oliva | Felix T. Bölle | A. T. Las | Xiaoxing Xia | Ivano E. Castelli
Source: Research Square (preprint, 2023-01-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reinforcement learning-based design of shape-changing metamaterials Anode ; Metamaterial ; Reinforcement learning ; Materials science ; Battery (electricity) ; Nanostructure ; Computer science ; Nanotechnology ; Transformation (genetics) ; Electrode ; Artificial intelligence ; Optoelectronics ; Power (physics) ; Physics Supercapacitor Materials and Fabrication ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Mads K. Plenge | Jack K. Pedersen | Alexander Bagger | Jan Rossmeisl
Source: ChemRxiv (preprint, 2023-11-15)
Field: Materials Science — Materials Chemistry

Catalysis of C-N Coupling on High-Entropy Alloys Catalyzing carbon-nitrogen (C-N) coupling using small abundant nitrogenous molecules is of growing interest to lessen the environmental impact of the industrial process. High-entropy alloys hold the potential to have surface sites optimized for each of the coupling intermediates and by varying the alloy composition of the catalyst it provides tunability to the adsorption energy distribution. We model the C-N coupling using adsorption energies of CO and NO and assume that *CO couples with an *NO-reduced intermediate. As more mechanistic inside is needed, we limit the model to the coverage of pairs of *CO and *NO, providing a necessary but not sufficient condition for catalytic activity. Two limiting cases for simulating the adsorption process are presented: one considering thermodynamic equilibrium and one considering out-of- equilibrium conditions. A Monte Carlo method connecting the two limits is suggested which reproduce trends observed in experiments. By varying the partial pressure of NO the shape of the pair coverage function in the composition space as well as its optima are altered. Our method finds a ternary AuCuPd optimum at equal CO/NO partial pressures and copper to be the optimum at low NO partial pressures. Adsorption ; Ternary operation ; Thermodynamics ; Catalysis ; Coupling (piping) ; Monte Carlo method ; Entropy (arrow of time) ; Limiting ; Partial pressure ; Molecule ; Isothermal process ; Chemistry ; Materials science ; Chemical physics ; Physical chemistry ; Physics ; Mathematics ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Materials Characterization Techniques ; Fullerene Chemistry and Applications