jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ting Yu | Shixuan Su | Jing Hu | Jun Zhang | Yunlei Xianyu
Source: Advanced Materials (article, 2022-01-06)
Field: Materials Science — Materials Chemistry

A New Strategy for Microbial Taxonomic Identification through Micro‐Biosynthetic Gold Nanoparticles and Machine Learning Microorganisms can serve as biological factories for the synthesis of inorganic nanomaterials that can become useful as nanocatalysts, energy-harvesting-storage components, antibacterial agents, and biomedical materials. Herein, the development of biosynthesis of inorganic nanomaterials into a simple, stable, and accurate strategy for distinguishing microorganisms from multiple classification levels (i.e., kingdom, order, genus, and species) without gene amplification, biochemical testing, or target recognition is reported. Gold nanoparticles (AuNPs) biosynthesized by different microorganisms differ in color of the solution, and their features can be characterized, including the particle size, the surface plasmon resonance (SPR) spectrum, and the surface potential. The inter-relation between the features of micro-biosynthetic AuNPs and the classification of microorganisms are exploited at different levels through machine learning to establish a taxonomic model. This model agrees well with traditional classification methods that offers a new strategy for microbial taxonomic identification. The underlying mechanism of this strategy is related to the biomolecules produced by different microorganisms including glucose, glutathione, and nicotinamide adenine dinucleotide phosphate-dependent reductase that regulate the features of micro-biosynthetic AuNPs. This work broadens the application of biosynthesis of inorganic materials through micro-biosynthetic AuNPs and machine learning, which holds great promise as a tool for biomedical research. Surface plasmon resonance ; Microorganism ; Colloidal gold ; Biomolecule ; Nanomaterials ; Identification (biology) Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Diatoms and Algae Research
Authors: Chang Woo Myung | Amir Hajibabaei | Ji‐Hyun Cha | Miran Ha | Junu Kim | Kwang S. Kim
Source: Advanced Energy Materials (article, 2022-10-06)
Field: Materials Science — Materials Chemistry

Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives Abstract Metal halide perovskite (MHP) is a promising next generation energy material for various applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs show excellent mechanical, dielectric, photovoltaic, photoluminescence, and electronic properties, and such intriguing physical and chemical properties have drawn attention recently. However, there exists a chasm between the successful applications of MHPs and theoretical understandings. The difficulty arises from the intrinsic properties of MHPs, including structural disorder, ionic interactions, nonadiabatic effects, and composition diversity. Machine learning (ML) approaches have shown great promise as a tool to overcome the theoretical obstacles in many fields of science. In this perspective, the pending theoretical challenges from experiments are overviewed and promising ML approaches, including ab initio ML potentials, materials design/optimization models, and data mining strategies are proposed. Possible roles and pipelines of ML frameworks are highlighted to close the gap between experiment and theory in MHPs. Materials science ; Perovskite (structure) ; Nanotechnology ; Photovoltaic system ; Ab initio ; Halide ; Diode ; Photoluminescence ; Engineering physics ; Optoelectronics ; Physics ; Electrical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Jie Sun | Cunzhi Zhang | Zhonghua Yang | Yiheng Shen | Ming Hu | Qian Wang
Source: ACS Applied Materials & Interfaces (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

Four-Phonon Scattering Effect and Two-Channel Thermal Transport in Two-Dimensional Paraelectric SnSe In recent years, increased attention has been paid to the study of four-phonon interactions and diffusion transport in three-dimensional (3D) thermoelectric materials because they play an essential role in understanding the thermal transport process. In this work, we study four-phonon scattering and diffusion transport in two-dimensional (2D) thermoelectric materials using the paraelectric phase of 2D SnSe as an example. The inherent soft phonon modes are treated by the self-consistent phonon theory, which considers the temperature-induced renormalization of the phonons. Based on density functional theory and the Peierls-Boltzmann transport equation for phonons, we show that the four-phonon interactions can reduce the thermal conductivity of the 2D SnSe sheet by nearly 40% due to the collapse of soft optical modes, and the contribution of diffusion transport to the total thermal conductivity accounts for 14% at a high temperature of 800 K due to the short phonon mean free path approaching the Ioffe-Regel limit, suggesting the two-channel transport in this system. The results are further confirmed by using the machine learning-assisted molecular dynamics simulations. This work provides a new insight into the physical mechanisms for thermal transport in 2D systems with strong anharmonic effects. Phonon ; Boltzmann equation ; Condensed matter physics ; Thermal conductivity ; Materials science ; Thermoelectric effect ; Phonon scattering ; Scattering ; Thermoelectric materials ; Thermal conduction ; Anharmonicity ; Mean free path ; Physics ; Thermodynamics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Jinhua Zhu | Yingce Xia | Lijun Wu | Shufang Xie | Tao Qin | Wengang Zhou | Houqiang Li | Tie‐Yan Liu
Source: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

Unified 2D and 3D Pre-Training of Molecular Representations Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represented by a 3D conformation with 3-dimensional coordinates of all atoms. We note that most previous work handles 2D and 3D information separately, while jointly leveraging these two sources may foster a more informative representation. In this work, we explore this appealing idea and propose a new representation learning method based on a unified 2D and 3D pre-training. Atom coordinates and interatomic distances are encoded and then fused with atomic representations through graph neural networks. The model is pre-trained on three tasks: reconstruction of masked atoms and coordinates, 3D conformation generation conditioned on 2D graph, and 2D graph generation conditioned on 3D conformation. We evaluate our method on 11 downstream molecular property prediction tasks: 7 with 2D information only and 4 with both 2D and 3D information. Our method achieves state-of-the-art results on 10 tasks, and the average improvement on 2D-only tasks is 8.3%. Our method also achieves significant improvement on two 3D conformation generation tasks. Computer science ; Graph ; Representation (politics) ; Atom (system on chip) ; Training set ; Theoretical computer science ; Molecular graph ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Katelyn A. Kirchner | Daniel R. Cassar | Edgar Dutra Zanotto | Madoka Ono | Seong H. Kim | Karan Doss | Mikkel L. Bødker | Morten M. Smedskjær | Shinji Kohara | Longwen Tang | Mathieu Bauchy | Collin J. Wilkinson | Yongjian Yang | Rebecca S. Welch | Matthew Mancini | John C. Mauro
Source: Chemical Reviews (review, 2022-05-05)
Field: Materials Science — Ceramics and Composites

Beyond the Average: Spatial and Temporal Fluctuations in Oxide Glass-Forming Systems Atomic structure dictates the performance of all materials systems; the characteristic of disordered materials is the significance of spatial and temporal fluctuations on composition-structure-property-performance relationships. Glass has a disordered atomic arrangement, which induces localized distributions in physical properties that are conventionally defined by average values. Quantifying these statistical distributions (including variances, fluctuations, and heterogeneities) is necessary to describe the complexity of glass-forming systems. Only recently have rigorous theories been developed to predict heterogeneities to manipulate and optimize glass properties. This article provides a comprehensive review of experimental, computational, and theoretical approaches to characterize and demonstrate the effects of short-, medium-, and long-range statistical fluctuations on physical properties (e.g., thermodynamic, kinetic, mechanical, and optical) and processes (e.g., relaxation, crystallization, and phase separation), focusing primarily on commercially relevant oxide glasses. Rigorous investigations of fluctuations enable researchers to improve the fundamental understanding of the chemistry and physics governing glass-forming systems and optimize structure-property-performance relationships for next-generation technological applications of glass, including damage-resistant electronic displays, safer pharmaceutical vials to store and transport vaccines, and lower-attenuation fiber optics. We invite the reader to join us in exploring what can be discovered by going beyond the average. Statistical physics ; Relaxation (psychology) ; Chemistry ; Range (aeronautics) ; Oxide ; Nanotechnology ; Chemical physics ; Biological system ; Materials science ; Physics Glass properties and applications ; Material Dynamics and Properties ; Phase-change materials and chalcogenides
Authors: Hiba Zaid | Zainab T. Al‐Sharify | Mohammed Hazwan Hamzah | Salih Rushdi
Source: Journal of Engineering and Sustainable Development (review, 2022-11-04)
Field: Chemistry — Analytical Chemistry

OPTIMIZATION OF DIFFERENT CHEMICAL PROCESSES USING RESPONSE SURFACE METHODOLOGY- A REVIEW Several chemical and biological processes have been investigated and predicted using Response Surface Methodology (RSM) models. Response Surfaces Methodology is a useful instrument for designing laboratory-scale experiments that optimize and support the research outcomes with statistical analysis. It is a powerful statistical technique for complex variable study systems. The standard optimization (one component at a time) strategy is well-studied. However, it has significant drawbacks, such as requiring more experimental runs and time and failing to reveal the synergistic impact of processing parameters. It is a valuable instrument for process improvement. Recent research has shown, for instance, that RSM successfully optimizes biodiesel in fats and oils generated from diverse feedstocks. According to this study, Response Surface Methodology is the best cost-effective technique for maximizing environmentally friendly and sustainable methods applied to different experimental procedures. The current review reported RSM's application, theory, methodology, advantages, and limitations for different processes using different oil sources. Response surface methodology ; Design of experiments ; Biochemical engineering ; Computer science ; Process (computing) ; Process engineering ; Experimental data ; Process optimization ; Environmentally friendly ; Chemical process ; Machine learning ; Engineering ; Mathematics ; Statistics ; Environmental engineering Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Abdullah S. Altorbaq | Alejandro A. Krauskopf | Xiangning Wen | Ricardo A. Pérez‐Camargo | Yunlan Su | Dujin Wang | Alejandro J. Müller | Sanat K. Kumar
Source: Progress in Polymer Science (article, 2022-03-01)
Field: Materials Science — Polymers and Plastics

Crystallization kinetics and nanoparticle ordering in semicrystalline polymer nanocomposites Nucleation ; Crystallization ; Materials science ; Polymer ; Crystallization of polymers ; Crystallinity ; Nanocomposite ; Nanoparticle ; Chemical engineering ; Dispersion (optics) ; Polymer nanocomposite ; Polymer chemistry ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; biodegradable polymer synthesis and properties
Authors: Mark E. Carrington | Nakul Rampal | David G. Madden | Daniel O’Nolan | Nicola Casati | Giorgio Divitini | Jesús Á. Martín‐Illán | Michele Tricarico | Ritums Cepitis | Ceren Çamur | Teresa Curtin | Joaquín Silvestre‐Albero | Jin‐Chong Tan | Félix Zamora | S. N. Taraskin | Karena W. Chapman | David Fairen‐Jiménez
Source: Chem (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Sol-gel processing of a covalent organic framework for the generation of hierarchically porous monolithic adsorbents Covalent organic frameworks (COFs) have emerged as a versatile material platform for such applications as chemical separations, chemical reaction engineering, and energy storage. Their inherently low mechanical stability, however, frequently renders existing methods of pelletization ineffective, contributing to pore collapse, pore blockage, or insufficient densification of crystallites. Here, we present a process for the shaping and densifying of COFs into robust centimeter-scale porous monoliths without the need for templates, additives, or binders. This process minimizes mechanical damage from shear-induced plastic deformation and further provides a network of interparticle mesopores that we exploit in accessing analyte capacities above those achievable from the intrinsic COF structure. Using a lattice-gas model, we accurately capture the monolithic structure across the mesoporous range and tie pore architecture to performance in both gas-storage and -separation applications. Collectively, these results represent a substantial step in the practical applicability of COFs and other mechanically weak porous materials. Materials science ; Mesoporous material ; Porosity ; Nanotechnology ; Porous medium ; Template ; Chemical engineering ; Composite material ; Chemistry ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Yusheng Hou | Xue Feng | Liang Qiu | Zhe Wang | Ruqian Wu
Source: npj Computational Materials (article, 2022-05-26)
Field: Materials Science — Materials Chemistry

Multifunctional two-dimensional van der Waals Janus magnet Cr-based dichalcogenide halides Abstract Two-dimensional van der Waals Janus materials and their heterostructures offer fertile platforms for designing fascinating functionalities. Here, by means of systematic first-principles studies on van der Waals Janus monolayer Cr-based dichalcogenide halides Cr YX ( Y = S, Se, Te; X = Cl, Br, I), we find that CrS X ( X = Cl, Br, I) are the very desirable high T C ferromagnetic semiconductors with an out-of-plane magnetization. Excitingly, by the benefit of the large magnetic moments on ligand S 2− anions, the sought-after large-gap quantum anomalous Hall effect and sizable valley splitting can be achieved through the magnetic proximity effect in van der Waals heterostructures CrSBr/Bi 2 Se 3 /CrSBr and MoTe 2 /CrSBr, respectively. Additionally, we show that large Dzyaloshinskii–Moriya interactions give rise to skyrmion states in CrTe X ( X = Cl, Br, I) under external magnetic fields. Our work reveals that two-dimensional Janus magnet Cr-based dichalcogenide halides have appealing multifunctionalities in the applications of topological electronic and valleytronic devices. van der Waals force ; Janus ; Condensed matter physics ; Heterojunction ; Magnetization ; Ferromagnetism ; Monolayer ; Magnet ; Chemistry ; Materials science ; Physics ; Nanotechnology ; Magnetic field ; Quantum mechanics ; Molecule 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications
Authors: SM Bargeen Alam Turzo | Justin T. Seffernick | Amber D. Rolland | Micah Donor | Sten Heinze | James S. Prell | Vicki H. Wysocki | Steffen Lindert
Source: Nature Communications (article, 2022-07-28)
Field: Chemistry — Spectroscopy

Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCS<sub>IM</sub>). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCS<sub>IM</sub> from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction. Protein structure prediction ; Ion-mobility spectrometry ; Computer science ; Protein structure ; Projection (relational algebra) ; Mass spectrometry ; Algorithm ; Metric (unit) ; Biological system ; Data mining ; Chemistry ; Biology ; Chromatography ; Engineering Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Mucize SARIHAN
Source: Open Chemistry (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Simulation of gamma-ray shielding properties for materials of medical interest Abstract Radiation that is emitted from unstable nuclei during radioactive decay is an important phenomenon to be used in large fields, and thus, radiation shielding properties are important especially for gamma rays. Thus, in the present work, the radiation shielding properties in terms of linear attenuation coefficients and some other quantities for medical interest materials of water, fat and bone have been obtained. The results simulated by Phy-X/PSD online code the energy range of 10 −3 –10 5 MeV. Electromagnetic shielding ; Attenuation ; Radiation shielding ; Radiation ; Gamma ray ; Range (aeronautics) ; Medical radiation ; Materials science ; Computational physics ; Physics ; Radiochemistry ; Nuclear physics ; Optics ; Medical physics ; Chemistry ; Composite material Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Radioactivity and Radon Measurements
Authors: Tom A. Young | Tristan Johnston-Wood | Hanwen Zhang | Fernanda Duarte
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Reaction dynamics of Diels–Alder reactions from machine learned potentials Recent advances in the development of reactive machine-learned potentials (MLPs) promise to transform reaction modelling. However, such methods have remained computationally expensive and limited to experts. Here, we employ different MLP methods (ACE, NequIP, GAP), combined with automated fitting and active learning, to study the reaction dynamics of representative Diels-Alder reactions. We demonstrate that the ACE and NequIP MLPs can consistently achieve chemical accuracy (±1 kcal mol<sup>-1</sup>) to the ground-truth surface with only a few hundred reference calculations. These strategies are shown to enable routine <i>ab initio</i>-quality classical and quantum dynamics, and obtain dynamical quantities such as product ratios and free energies from non-static methods. For ambimodal reactions, product distributions were found to be strongly dependent on the QM method and less so on the type of dynamics propagated. Ab initio ; Quantum ; Product (mathematics) ; Computational chemistry ; Diels–Alder reaction ; Reaction dynamics ; Dynamics (music) ; Computer science ; Chemistry ; Statistical physics ; Physics ; Mathematics ; Quantum mechanics ; Organic chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Giovanni Bittante | Nageshvar Patel | Alessio Cecchinato | Paolo Berzaghi
Source: Journal of Dairy Science (review, 2022-01-05)
Field: Chemistry — Analytical Chemistry

Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9-30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum-the visible, infrared-A, or infrared-B range-may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results. Chemometrics ; Spectrometer ; Near-infrared spectroscopy ; Composition (language) ; Chemical composition ; Calibration ; Infrared spectroscopy ; Standard deviation ; Environmental science ; Analytical Chemistry (journal) ; Chemistry ; Remote sensing ; Mathematics ; Statistics ; Environmental chemistry ; Chromatography ; Geography ; Optics Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Meat and Animal Product Quality