jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Virginia Carnevali | Lorenzo Agosta | Vladislav Sláma | Nikoalos Lempesis | Andrea Vezzosi | Ursula Röthlisberger
Source: arXiv (Cornell University) (preprint, 2024-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nano-size nature of the $α$-FAPbI$_3$ by means of large-scale ab initio simulations Formamidinium-lead-iodide (FAPbI$_3$) has established itself as the state of the art for high solar-energy conversion efficiency in perovskite-based solar cells. At room temperature, FAPbI$_3$ has a peculiar crystal structure with tetragonal symmetry where the PbI$_6$ framework is distorted from the perfect cubic structure, while the FA molecules are randomly rotated. This is well known experimentally, but the theory is still deficient in describing FAPbI$_3$ with appropriate models in which the system size is adequately taken into account. Using ab initio molecular dynamics at 300 K and first-principle calculations, we prove that, in order to obtain a proper description of the system, three factors must be satisfied simultaneously: the band gap, the minimization of structural distortion, and the zeroing out of the dipole moment. We show that the net dipole moment zeroes out as the system size increases due to PbI6 octahedra distortions rather than FA rotations. We also show that the band gap oscillations in temperature are correlated to octahedra tilting. The optimum between simulations and experimental properties indicates that FAPbI$_3$ is properly described by a system size approaching the nano-scale. Alpha (finance) ; Ab initio ; Scale (ratio) ; Nano- ; Statistical physics ; Physics ; Mathematics ; Statistics ; Quantum mechanics Organic and Molecular Conductors Research ; Inorganic Chemistry and Materials ; Perovskite Materials and Applications
Authors: Shuangli Dong | Yao JI | Hong Ming | Weichao Wang | QinYuan ZHANG
Source: Scientia Sinica Technologica (article, 2024-08-29)
Field: Materials Science — Ceramics and Composites

Predicting spectroscopic properties of laser glasses: Theory and practice <p indent="0mm">Germanate optical glass, due to its wide infrared transmission range, low phonon energy, and excellent mechanical properties, has important applications in military platforms as infrared window materials and gain media for near and mid-infrared fiber lasers. Developing germanate glass compositions with specific physical and spectroscopic properties is one of the important challenges in this field. In this study, approximately 500 high-quality data were first obtained by a phase diagram approach. Based on this, a model was constructed using the random forest algorithm, replacing more than 20 glass compositions with 12 physical and chemical descriptors as features, accurately predicting properties such as full width at half maximum, emission cross-section, radiative lifetime, and radiative transition probability of the glass. This study optimizes and reduces the complexity of the model by reducing the number of descriptors, and enhances the forecasting and extrapolation abilities in extreme regions. This is of great significance for improving the interpretability and generality of the model and discovering novel laser glasses. Medicine Glass properties and applications ; Laser Material Processing Techniques ; Phase-change materials and chalcogenides
Authors: Anca Onaciu | Valentin Toma | R. Borsa | Vasile Chiş | G. STIUFIUC | Carina Culic | Constantin Mihai Lucaciu | Rareş Ştiufiuc
Source: Sensors (article, 2024-11-07)
Field: Chemistry — Organic Chemistry

Investigating Nanoscale Interactions of Host–Guest Complexes Formed Between CB[7] and Atenolol by Quantum Chemistry and Ultrasensitive Vibrational Spectroscopy In addition to the course of over 20 years of cucurbit-7-uril (CB[7]) in the pharmaceutical industry, the present study brings together the most recent observations from the perspective of ultrasensitive Raman spectroscopy and Density Functional Theory (DFT) related to the interaction of this molecule with atenolol (Ate) enantiomers during the formation of these host-guest complexes. Quantum chemistry calculations based on DFT were first used to understand the interaction geometry between CB[7] and Ate. These results were further confirmed by ultrasensitive vibrational spectroscopy. The spectral features associated with each enantiomer in the presence of CB[7] were analyzed by means of SERS, highlighting distinct interaction profiles. These experimental findings validated quantum chemical calculations, offering a comprehensive understanding of the host-guest interactions at the nanoscale level. Quantum chemistry ; Nanoscopic scale ; Spectroscopy ; Chemistry ; Host–guest chemistry ; Host (biology) ; Nanotechnology ; Quantum ; Atenolol ; Computational chemistry ; Chemical physics ; Physical chemistry ; Materials science ; Molecule ; Organic chemistry ; Supramolecular chemistry ; Physics ; Quantum mechanics Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Advanced NMR Techniques and Applications
Authors: Xin Ouyang | Xuemin Wang | Xinming Hu | Mengnan Xing | Chenxi Liu | Zongxu Pang
Source: Materiali in tehnologije (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

COMPOSITION DESIGN OF 316LN AUSTENITIC STAINLESS STEEL FOR LIQUID-HYDROGEN STORAGE BASED ON HIGH-FLUX PREPARATION Designing multi-element alloy compositions to achieve target performance was the first step in the development of modern materials, but the traditional trial-and-error experiment seriously restricted the development of new materials due to its low efficiency. In this investigation, the composition design of 316LN austenitic stainless steel for enhanced liquid-hydrogen storage using multi-crucible synchronous metallurgy in a high-flux experiment was proposed. Sixteen groups of as cast austenitic stainless steel samples with different compositions were smelted using high-flux material preparation. Then, through the observation of their microstructures, the chemical composition that best matched the target performance was finally selected. The results show that high-throughput experiments can greatly improve the efficiency of composition design optimization of new stainless steel products. At the same time, this investigation also analyzed the elemental composition of δ-ferrite and the method for effectively controlling the δ-ferrite content. In 316LN stainless steel, Cr and Mo were easily enriched in ferrite grains or grain boundaries, forming Cr and Mo enriched regions. This resulted in a gradient transition of Cr and Mo from the ferrite region to the austenite region, forming galvanic corrosion. In this study, the distribution of Cr, Mo and other elements in 316LN stainless steel was studied by means of a metallographic microscope, electron probe microanalysis, transmission electron microscope and scanning electron microscope. In addition, the relationship between the ferrite content and chemical composition was explored. Finally, it was determined that high-temperature, long-term sensitization treatment is an effective method for controlling the δ-ferrite content. Materials science ; Metallurgy ; Austenite ; Ferrite (magnet) ; Austenitic stainless steel ; Microstructure ; Scanning electron microscope ; Alloy ; Chemical composition ; Corrosion ; Composite material Nuclear Materials and Properties ; Metallurgy and Material Science ; Hydrogen Storage and Materials
Authors: М.М. Pylypenko | О.V. Yefimov | Tetiana Potanina
Source: InterConf (article, 2024-10-19)
Field: Materials Science — Materials Chemistry

Pure metals for creation the nuclear structural materials The paper presents the main results of research on the development of structural materials for equipment elements of existing and prospective nuclear power plants. The further development of nuclear energy largely depends on the development of advanced structural materials for new generation reactors and the improvement of materials for operating nuclear power plants. The high requirements for structural materials regarding the content of impurity elements can be met only when high-purity metals are used as initial components and new technologies for their production are used. The prospects of using new methods and approaches for evaluating and improving the physico-mechanical and physico-chemical properties (microhardness, corrosion resistance, radiation resistance, etc.) of structural materials for nuclear technologies were considered. Business ; Materials science Graphite, nuclear technology, radiation studies
Authors: Yu Jiang | Jiang Li | Dan Li | Yunke Ma | Shucun Zhou | Yu Wang | Daohong Zhang | Xinlu Zheng | Ziwei Ye | Zeeshan Akmal | Chun He | Jinlong Zhang | Lingzhi Wang | Jiawei Hao Lan | Liwei Wang | Dandan Cheng | Hua Yu | * Wang | Lin Guo | Yong Jiang | Hao Fu | Zhong Liang | Qian Zhang | Yaping Du | ; Tyler | J Hannah | S Saurabh
Source: Chemical Society Reviews (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Chem. Soc. Rev., 2024, 53, 551 DOI: 10.1039/D4CS90006E This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Chemistry Machine Learning in Materials Science ; Covalent Organic Framework Applications ; Chemistry and Chemical Engineering
Authors: Yu. K. Undalov
Source:  (article, 2024-12-11)
Field: Chemistry — Organic Chemistry

CALCULATION OF FORMULAS OF HOMOLOGOUS SERIES OF CHEMICAL COMPOUNDS (IN A GENERALIZED FORM): FIVE-COMPONENT SYSTEMS (Aa+ – Ff+ – [BrdDwb](r + w)bd+ – Cc–) AND (Li+ – Fe4+ – [Sr3La2]12+ – O2–) The authors consider the growing role of transport corridors and logistics interaction between the RIC countries -Russia, India and China, which is especially becoming relevant in the light of the expansion of BRICS to the BRICS + format and India's attempt to build, together with the United States, its own logistics route between Africa, the Middle East and Europe. Homologous series ; Component (thermodynamics) ; Series (stratigraphy) ; Mathematics ; Combinatorics ; Chemistry ; Physics ; Crystallography ; Thermodynamics ; Geology Chemical Thermodynamics and Molecular Structure ; Adsorption, diffusion, and thermodynamic properties of materials ; Chemical and Physical Properties in Aqueous Solutions
Authors: Javier Emilio Alfonso Ramos | Carlo Adamo | Éric Brémond | Thijs Stuyver
Source: ChemRxiv (preprint, 2024-12-12)
Field: Materials Science — Materials Chemistry

Improving the reliability of, and confidence in, DFT functional benchmarking through active learning Validating the performance of exchange-correlation functionals is vital to ensure the reliability of DFT calculations. Typically, these validations involve benchmarking datasets. Currently, such datasets are typically assembled in an unprincipled manner, suffering from uncontrolled chemical bias, and limiting the transferability of benchmarking results to broader chemical space. In this work, a data-efficient solution, based on active learning, is explored to address this issue. Focusing -- as a proof of principle -- on pericyclic reactions, we start from the BH9 benchmarking dataset, and design a chemical space around this initial dataset by combinatorially combining reaction templates and substituents. Next, a surrogate model is trained to predict the standard deviation of the activation energies computed across a selection of 20 distinct DFT functionals. With this model, the designed chemical space is explored, enabling the identification of challenging regions, for which representative reactions are subsequently acquired. Remarkably, it turns out that the function mapping molecular structure to DFT functional divergence is readily learnable; convergence is reached upon the acquisition of less than 100 reactions. With our final model, a more challenging -- and arguably more representative -- pericyclic benchmarking dataset is curated, and we demonstrate that the functional performance has changed significantly compared to the original BH9 subset. Benchmarking ; Pericyclic reaction ; Chemical space ; Benchmark (surveying) ; Computer science ; Reliability (semiconductor) ; Space (punctuation) ; Divergence (linguistics) ; Function (biology) ; Transferability ; Convergence (economics) ; Machine learning ; Artificial intelligence ; Computational chemistry ; Chemistry ; Physics ; Drug discovery ; Thermodynamics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Ryosuke Mashiko | Makoto Naruse | Ryoichi Horisaki
Source: arXiv (Cornell University) (preprint, 2024-05-09)
Field: Materials Science — Materials Chemistry

Diffraction casting Optical computing is considered a promising solution for the growing demand for parallel computing in various cutting-edge fields, requiring high integration and high speed computational capacity. In this paper, we propose a novel optical computation architecture called diffraction casting (DC) for flexible and scalable parallel logic operations. In DC, a diffractive neural network (DNN) is designed for single instruction, multiple data (SIMD) operations. This approach allows for the alteration of logic operations simply by changing the illumination patterns. Furthermore, it eliminates the need for encoding and decoding the input and output, respectively, by introducing a buffer around the input area, facilitating end-to-end all-optical computing. We numerically demonstrate DC by performing all 16 logic operations on two arbitrary 256 bits parallel binary inputs. Additionally, we showcase several distinctive attributes inherent in DC, such as the benefit of cohesively designing the diffractive elements for SIMD logic operations, assuring high scalability and integration capability. Our study offers a novel design architecture for optical computers and paves the way for a next-generation optical computing paradigm. Diffraction ; Casting ; Materials science ; Metallurgy ; Optics ; Physics X-ray Diffraction in Crystallography ; Engineering Applied Research
Authors: Liang Wang | Qiang Liu | S. B. Liu | Xinjun Sun | Shu Ya Wu
Source: arXiv (Cornell University) (preprint, 2024-11-02)
Field: Materials Science — Materials Chemistry

Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction Molecular property prediction (MPP) is integral to drug discovery and material science, but often faces the challenge of data scarcity in real-world scenarios. Addressing this, few-shot molecular property prediction (FSMPP) has been developed. Unlike other few-shot tasks, FSMPP typically employs a pre-trained molecular encoder and a context-aware classifier, benefiting from molecular pre-training and molecular context information. Despite these advancements, existing methods struggle with the ineffective fine-tuning of pre-trained encoders. We attribute this issue to the imbalance between the abundance of tunable parameters and the scarcity of labeled molecules, and the lack of contextual perceptiveness in the encoders. To overcome this hurdle, we propose a parameter-efficient in-context tuning method, named Pin-Tuning. Specifically, we propose a lightweight adapter for pre-trained message passing layers (MP-Adapter) and Bayesian weight consolidation for pre-trained atom/bond embedding layers (Emb-BWC), to achieve parameter-efficient tuning while preventing over-fitting and catastrophic forgetting. Additionally, we enhance the MP-Adapters with contextual perceptiveness. This innovation allows for in-context tuning of the pre-trained encoder, thereby improving its adaptability for specific FSMPP tasks. When evaluated on public datasets, our method demonstrates superior tuning with fewer trainable parameters, improving few-shot predictive performance. Shot (pellet) ; Property (philosophy) ; Context (archaeology) ; One shot ; Computer science ; Algorithm ; Materials science ; Engineering ; Mechanical engineering ; Geology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Vinod Khatri | Nico Boback | Hassan Abdelwahab | Daniela Niemeyer | Tom Palmer | Anil Kumar Sahoo | Yannic Kerkhoff | Kai Ludwig | Dilara Balci | Jakob Trimpert | Rainer Haag | Tatyana L. Povolotsky | Roland R. Netz | Christian Drosten | Daniel Lauster | Sumati Bhatia
Source: ChemRxiv (preprint, 2024-12-25)
Field: Chemistry — Organic Chemistry

Polysialosides outperform sulfated analogs for the inhibition of SARS-CoV-2 Both polysialosides and polysulfates are known to interact with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. However, a comprehensive site by site analysis of their binding affinities and potential synergistic antiviral effects have not been performed. Here, we report on the synthesis of polysialosides with nanomolar binding affinities to spike proteins of SARS-CoV-2 in solution using microscale thermophoresis (MST). The dendritic polyglycerol based polysialosides dPG500(SA)0.55 and dPG500(SA)0.25, with a dissociation constant Kd of 4.78 nM and 10.85 nM, respectively, bind ~500 times stronger than the high density polysulfated analog dPG500(OSO3Na)0.55, to intact SARS-CoV-2 virus particles or isolated spike protein. In fact, the presence of sulfate groups in a heteromultivalent compound dPG500(SA)0.20(OSO3Na)0.20 weakens the binding to spike proteins. A polycarboxylated analog does not bind to SARS-CoV-2, ruling out that the interaction of polysialoside is simply driven by electrostatic interactions. Furthermore, we found potent nanomolar binding of dPG500(SA)0.55 to SARS-CoV-2 variant B.1.617 (Delta) and B.1.1.529 (Omicron) RBD. Using explicit-solvent all-atom molecular dynamics (MD) simulations and docking studies, we obtain atomistic details on the interaction of different functional groups with the SARS-CoV-2 RBD and their binding affinities. Our data support the conclusion that sialosides interact stronger with RBD than sulfates. Notably, our most affine binder dPG500(SA)0.55 inhibits SARS-CoV-2 (WT, D614G) replication up to 98.6% at low nanomolar concentrations. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Coronavirus disease 2019 (COVID-19) ; Sulfation ; 2019-20 coronavirus outbreak ; Virology ; Computational biology ; Chemistry ; Biology ; Medicine ; Internal medicine ; Biochemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Khadija Suleiman Adamu | Imrana Salisu
Source: UMYU Scientifica (article, 2024-12-30)
Field: Materials Science — Biomaterials

Optimization of Polyhydroxybutyrate Production by Bacillus Species Isolated from Dump Site Soil Using Multiple Linear Regression Analysis Petrochemical-based plastics cause considerable environmental degradation due to their non-biodegradable properties; however, biodegradable plastics, such as polyhydroxybutyrates (PHBs), provide a sustainable relief to the environment. This study used sugarcane bagasse as a carbon source to synthesize PHB by Bacillus species isolated from dumping site soil. Bacillus velezensis, which displays a high affinity to generate PHB based on the intensity of coloration upon radiation, was selected from 53 isolates. At 30°C, pH 7.5, and 48 hours of incubation, under the ideal conditions resulted in attaining the maximum PHB yield of 50.23% (w/w). Similarly, a predicted PHB yield of 66.05% (w/w) was estimated by a multiple linear regression (MLR) model utilizing ordinary least squares (OLS) regression, which identified temperature, pH, substrate concentration, and incubation time as critical variables. Scanning electron microscopy displayed intracellular PHB granules, and Fourier-transform infrared (FT-IR) analysis indicated the existence of unique PHB functional groups (C–H, CH₂, C=O, and C–O). Tests on biodegradability indicated that soil microorganisms can break down the produced PHB, highlighting the environmental benefits of this research. This study also demonstrates that sugarcane bagasse, an agricultural byproduct, is a cost-effective raw material for PHB synthesis. Additionally, the OLS model provided useful insights for optimizing yield, indicating promise for industrial applications. Future studies should examine other optimization statistical techniques like Response Surface Methodology (RSM) to boost PHB production in complex biological systems. These techniques could also address scalability, economic feasibility, and environmental benefits through cooperation with agro-waste companies and pilot-scale manufacturing activities. Polyhydroxybutyrate ; Linear regression ; Production (economics) ; Environmental science ; Regression analysis ; Pulp and paper industry ; Mathematics ; Waste management ; Soil science ; Statistics ; Biology ; Engineering ; Economics ; Bacteria biodegradable polymer synthesis and properties ; Biopolymer Synthesis and Applications ; Biofuel production and bioconversion
Authors: N.M. Sami | O.A. Abdel Moamen | E.M. Abou El Hussein | Rehab O. Abdel Rahman
Source: Research Square (preprint, 2024-10-01)
Field: Materials Science — Materials Chemistry

Radiological and hydraulic durability of alkali-borosilicate radioactive waste immobilization matrix prepared from industrial waste glass grit: Insights into the microstructure alterations Borosilicate glass ; Radioactive waste ; Durability ; Microstructure ; Radiological weapon ; Grit ; Alkali metal ; Waste management ; Materials science ; Matrix (chemical analysis) ; Metallurgy ; Radiochemistry ; Composite material ; Chemistry ; Engineering Nuclear materials and radiation effects ; Concrete and Cement Materials Research ; Graphite, nuclear technology, radiation studies
Authors: P.Madhu Raghava | P.V. Subhanjaneyulu | B.V.Amarnath Reddy | S. Nagarjuna Reddy
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Fabrication and Testing of Hybrid Polymer Composite Material by Using Carbon, Glass, Kevlar Fibers Composites which made up of fiber reinforcements are universally endorsed option for the automobiles with a lightweight. Due to the unique properties of fiber-reinforced composites, it became a perfect alternative for traditionally used materials. Most of the automobile industries prefer polymer matrix fiber-reinforced composites for the reason that its light weight helps in carbon emission reduction and Additive manufacturing (AM) produces a complex shaped product from its data, layer by layer, with high precision and much less material wastage. As compared to the conventional manufacturing process, there are many positive environmental advantages of additive manufacturing technologies. Most importantly, there is less waste of raw material and the use of new and smart materials. In this work, we first focused on creating the composite material utilizing the Hand Lay-Up method with materials such as Carbon Fiber/Epoxy Composite with Copper Oxide (CuO) Nanoparticles and Banana Glass Fiber. Following that, we readied ASTMD638 specimens for both compressive and tensile testing. We created an ASTMD638 model for the investigation's tensile and compressive tests using Solid Works design program, and we then saved the design model in STL format. This STL format was uploaded into the CURA software for slicing. The slicing model was then saved into G-code format, and it was printed in a fused deposition modeling machine. Lastly, we tested both specimens using a compressive test setup in a Universal Testing Machine model (INSTRON-3369) UTM, and the results showed 3D printed files. Keywords: Glass Fiber, Banana Fiber, Carbon Fiber, Copper Oxide Nano filler, 3D printing Machine,Corbon Fiber Filament. Kevlar ; Fabrication ; Materials science ; Composite material ; Composite number ; Polymer ; Glass fiber ; Polymer science Silicone and Siloxane Chemistry ; Material Properties and Applications ; Additive Manufacturing and 3D Printing Technologies