jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liang Zeng | Taizheng Wu | Ting Ye | Tangming Mo | Rui Qiao | Guang Feng
Source: Nature Computational Science (article, 2021-11-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling galvanostatic charge–discharge of nanoporous supercapacitors Molecular modeling has been considered indispensable in studying the energy storage of supercapacitors at the atomistic level. The constant potential method (CPM) allows the electric potential to be kept uniform in the electrode, which is essential for a realistic description of the charge repartition and dynamics process in supercapacitors. However, previous CPM studies have been limited to the potentiostatic mode. Although widely adopted in experiments, the galvanostatic mode has rarely been investigated in CPM simulations because of a lack of effective methods. Here we develop a modeling approach to simulating the galvanostatic charge-discharge process of supercapacitors under constant potential. We show that, for nanoporous electrodes, this modeling approach can capture experimentally consistent dynamics in supercapacitors. It can also delineate, at the molecular scale, the hysteresis in ion adsorption-desorption dynamics during charging and discharging. This approach thus enables the further accurate modeling of the physics and electrochemistry in supercapacitor dynamics. Supercapacitor ; Nanoporous ; Hysteresis ; Materials science ; Molecular dynamics ; Capacitor ; Electrode ; Energy storage ; Capacitance ; Nanotechnology ; Electrochemistry ; Chemical physics ; Voltage ; Chemistry ; Physics ; Electrical engineering ; Thermodynamics ; Physical chemistry ; Power (physics) ; Computational chemistry ; Engineering ; Condensed matter physics Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Yannan Liu | Chuanshuang Chen | Jesús Valdez | Débora Motta Meira | Wanting He | Yong Wang | Cătălin Harnagea | Qiongqiong Lu | Tuğrul Güner | Hao Wang | Chenghao Liu | Qingzhe Zhang | Shengyun Huang | Aycan Yurtsever | Mohamed Chaker | Dongling Ma
Source: Nature Communications (article, 2021-02-23)
Field: Chemistry — Inorganic Chemistry

Phase-enabled metal-organic framework homojunction for highly selective CO2 photoreduction Conversion of clean solar energy to chemical fuels is one of the promising and up-and-coming applications of metal-organic frameworks. However, fast recombination of photogenerated charge carriers in these frameworks remains the most significant limitation for their photocatalytic application. Although the construction of homojunctions is a promising solution, it remains very challenging to synthesize them. Herein, we report a well-defined hierarchical homojunction based on metal-organic frameworks via a facile one-pot synthesis route directed by hollow transition metal nanoparticles. The homojunction is enabled by two concentric stacked nanoplates with slightly different crystal phases. The enhanced charge separation in the homojunction was visualized by in-situ surface photovoltage microscopy. Moreover, the as-prepared nanostacks displayed a visible-light-driven carbon dioxide reduction with very high carbon monooxide selectivity, and excellent stability. Our work provides a powerful platform to synthesize capable metal-organic framework complexes and sheds light on the hierarchical structure-function relationships of metal-organic frameworks. Homojunction ; Materials science ; Nanotechnology ; Metal-organic framework ; Photocatalysis ; Surface photovoltage ; Charge carrier ; Electrochemical reduction of carbon dioxide ; Chemical engineering ; Optoelectronics ; Doping ; Chemistry ; Catalysis ; Organic chemistry ; Spectroscopy Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Huangjie Lu | Jian Xie | Xinyu Wang | Yaxing Wang | Zi‐Jian Li | Kariem Diefenbach | Qing‐Jiang Pan | Yuan Qian | Jian‐Qiang Wang | Shuao Wang | Jian Lin
Source: Nature Communications (article, 2021-05-14)
Field: Chemistry — Inorganic Chemistry

Visible colorimetric dosimetry of UV and ionizing radiations by a dual-module photochromic nanocluster Radiation dosimeters displaying conspicuous response of irradiance are highly desirable, owing to the growing demand of monitoring high-energy radiation and environmental exposure. Herein, we present a case of dosimetry based on a discrete nanocluster, [Th<sub>6</sub>(OH)<sub>4</sub>(O)<sub>4</sub>(H<sub>2</sub>O)<sub>6</sub>](TPC)<sub>8</sub>(HCOO)<sub>4</sub>∙4DMF∙H<sub>2</sub>O (Th-SINAP-100), by judiciously incorporating heavy Th<sub>6</sub> polynuclear centers as radiation attenuator and organic linkers as photo-responsive sensor. Interestingly, dual-module photochromic transitions upon multiple external stimuli including UV, β-ray, and γ-ray are integrated into this single material. The striking color change, and more significantly, the visible color transition of luminescence in response to accumulating radiation dose allow an on-site quantitative platform for naked-eye detection of ionization radiations over a broad range (1-80 kGy). Single crystal X-ray diffraction and density functional theory calculations reveal that the dual-module photochromism can be attributed to the π(TPC) → π*(TPC) intermolecular charge transfer driven by enhanced π-π stacking interaction between the adjacent TPC moieties upon irradiation. Photochromism ; Dosimeter ; Ionizing radiation ; Dosimetry ; Materials science ; Irradiation ; Optoelectronics ; Naked eye ; Radiation ; Photochemistry ; Optics ; Chemistry ; Nanotechnology ; Fluorescence ; Physics ; Nuclear medicine Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Junsoo Park | Maxwell Dylla | Yi Xia | Max Wood | G. Jeffrey Snyder | Anubhav Jain
Source: Nature Communications (article, 2021-06-08)
Field: Materials Science — Materials Chemistry

When band convergence is not beneficial for thermoelectrics Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances charge conductivity while preserving the Seebeck coefficient. However, this advantage hinges on the assumption that interband scattering of carriers is weak or insignificant. With first-principles treatment of electron-phonon scattering in the CaMg<sub>2</sub>Sb<sub>2</sub>-CaZn<sub>2</sub>Sb<sub>2</sub> Zintl system and full Heusler Sr<sub>2</sub>SbAu, we demonstrate that the benefit of band convergence can be intrinsically negated by interband scattering depending on the manner in which bands converge. In the Zintl alloy, band convergence does not improve weighted mobility or the density-of-states effective mass. We trace the underlying reason to the fact that the bands converge at a one k-point, which induces strong interband scattering of both the deformation-potential and the polar-optical kinds. The case contrasts with band convergence at distant k-points (as in the full Heusler), which better preserves the single-band scattering behavior thereby successfully leading to improved performance. Therefore, we suggest that band convergence as thermoelectric design principle is best suited to cases in which it occurs at distant k-points. Condensed matter physics ; Scattering ; Convergence (economics) ; Thermoelectric effect ; Effective mass (spring–mass system) ; Seebeck coefficient ; Thermoelectric materials ; Charge carrier ; Electronic band structure ; Phonon scattering ; Physics ; Charge (physics) ; Phonon ; Materials science ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity
Authors: Jiang Guo | A. Santiago Ibanez-Lopez | Hanyu Gao | Victor Quach | Connor W. Coley | Klavs F. Jensen | Regina Barzilay
Source: Journal of Chemical Information and Modeling (review, 2021-06-11)
Field: Materials Science — Materials Chemistry

Automated Chemical Reaction Extraction from Scientific Literature Access to structured chemical reaction data is of key importance for chemists in performing bench experiments and in modern applications like computer-aided drug design. Existing reaction databases are generally populated by human curators through manual abstraction from published literature (e.g., patents and journals), which is time consuming and labor intensive, especially with the exponential growth of chemical literature in recent years. In this study, we focus on developing automated methods for extracting reactions from chemical literature. We consider journal publications as the target source of information, which are more comprehensive and better represent the latest developments in chemistry compared to patents; however, they are less formulaic in their descriptions of reactions. To implement the reaction extraction system, we first devised a chemical reaction schema, primarily including a central <i>product</i>, and a set of associated reaction roles such as <i>reactants</i>, <i>catalyst</i>, <i>solvent</i>, and so on. We formulate the task as a structure prediction problem and solve it with a two-stage deep learning framework consisting of <i>product extraction</i> and <i>reaction role labeling</i>. Both models are built upon Transformer-based encoders, which are adaptively pretrained using domain and task-relevant unlabeled data. Our models are shown to be both effective and data efficient, achieving an F1 score of 76.2% in product extraction and 78.7% in role extraction, with only hundreds of annotated reactions. Computer science ; Abstraction ; Schema (genetic algorithms) ; Information retrieval ; Artificial intelligence ; Data science Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Topic Modeling
Authors: Sensong An | Bowen Zheng | Mikhail Y. Shalaginov | Hong Tang | Hang Li | L. P. Zhou | Yunxi Dong | Mohammad Haerinia | Anu Agarwal | Clara Rivero‐Baleine | Myungkoo Kang | Kathleen Richardson | Tian Gu | Juejun Hu | Clayton Fowler | Hualiang Zhang
Source: Advanced Optical Materials (article, 2021-11-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces Abstract Metasurfaces have provided a novel and promising platform for realizing compact and high‐performance optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since near‐field coupling effects between elements will change when the element is surrounded by nonidentical structures. In this paper, a deep learning approach is proposed to predict the actual electromagnetic (EM) responses of each target meta‐atom placed in a large array with near‐field coupling effects taken into account. The predicting neural network takes the physical specifications of the target meta‐atom and its neighbors as input, and calculates its actual phase and amplitude in milliseconds. This approach can be used to optimize metasurfaces’ efficiencies when combined with optimization algorithms. To demonstrate the efficacy of this methodology, large improvements in efficiency for a beam deflector and a metalens over the conventional design approach are obtained. Moreover, it is shown that the correlations between a metasurface's performance and its design errors caused by mutual coupling are not bound to certain specifications (materials, shapes, etc.). As such, it is envisioned that this approach can be readily applied to explore the mutual coupling effects and improve the performance of various metasurface designs. Coupling (piping) ; Computer science ; Convolutional neural network ; Artificial neural network ; Boundary (topology) ; Field (mathematics) ; Phase (matter) ; Boundary element method ; Electronic engineering ; Topology (electrical circuits) ; Materials science ; Physics ; Finite element method ; Artificial intelligence ; Electrical engineering ; Mathematics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Christopher Kuenneth | William Schertzer | Rampi Ramprasad
Source: Macromolecules (article, 2021-06-29)
Field: Materials Science — Materials Chemistry

Copolymer Informatics with Multitask Deep Neural Networks Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs. So far, however, these data-driven efforts have largely focused on homopolymers. Here, we address the property prediction challenge for copolymers, extending the polymer informatics framework beyond homopolymers. Advanced polymer fingerprinting and deep-learning schemes that incorporate multitask learning and meta learning are proposed. A large data set containing over 18 000 data points of glass transition, melting, and degradation temperature of homopolymers and copolymers of up to two monomers is used to demonstrate the copolymer prediction efficacy. The developed models are accurate, fast, flexible, and scalable to more copolymer properties when suitable data become available. Copolymer ; Computer science ; Polymer ; Scalability ; Deep learning ; Materials informatics ; Informatics ; Task (project management) ; Monomer ; Set (abstract data type) ; Materials science ; Artificial neural network ; Artificial intelligence ; Nanotechnology ; Health informatics ; Systems engineering ; Engineering ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Jingxin Dong | Mingyi Zhao | Yuansheng Liu | Yansen Su | Xiangxiang Zeng
Source: Briefings in Bioinformatics (review, 2021-09-08)
Field: Materials Science — Materials Chemistry

Deep learning in retrosynthesis planning: datasets, models and tools In recent years, synthesizing drugs powered by artificial intelligence has brought great convenience to society. Since retrosynthetic analysis occupies an essential position in synthetic chemistry, it has received broad attention from researchers. In this review, we comprehensively summarize the development process of retrosynthesis in the context of deep learning. This review covers all aspects of retrosynthesis, including datasets, models and tools. Specifically, we report representative models from academia, in addition to a detailed description of the available and stable platforms in the industry. We also discuss the disadvantages of the existing models and provide potential future trends, so that more abecedarians will quickly understand and participate in the family of retrosynthesis planning. Retrosynthetic analysis ; Context (archaeology) ; Process (computing) ; Computer science ; Artificial intelligence ; Data science ; Geography ; Archaeology Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Georgina Sauzier | Wilhelm van Bronswijk | Simon W. Lewis
Source: The Analyst (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Chemometrics in forensic science: approaches and applications Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field. Chemometrics ; Forensic science ; Biochemical engineering ; Computer science ; Data science ; Chemistry ; Chromatography ; Engineering ; Geography ; Archaeology Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Forensic Fingerprint Detection Methods
Authors: Angga Hermawan | Tahta Amrillah | Anung Riapanitra | Wee‐Jun Ong | Shu Yin
Source: Advanced Healthcare Materials (review, 2021-07-28)
Field: Materials Science — Materials Chemistry

Prospects and Challenges of MXenes as Emerging Sensing Materials for Flexible and Wearable Breath‐Based Biomarker Diagnosis A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization. MXenes ; Wearable computer ; Biomarker ; Materials science ; Nanotechnology ; Computer science ; Biology ; Embedded system MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; 2D Materials and Applications
Authors: Md Abdullah Al Faruque | Md. Syduzzaman | Joy Sarkar | Kadir Bilişik | Maryam Naebe
Source: Nanomaterials (review, 2021-09-16)
Field: Materials Science — Materials Chemistry

A Review on the Production Methods and Applications of Graphene-Based Materials Graphene-based materials in the form of fibres, fabrics, films, and composite materials are the most widely investigated research domains because of their remarkable physicochemical and thermomechanical properties. In this era of scientific advancement, graphene has built the foundation of a new horizon of possibilities and received tremendous research focus in several application areas such as aerospace, energy, transportation, healthcare, agriculture, wastewater management, and wearable technology. Although graphene has been found to provide exceptional results in every application field, a massive proportion of research is still underway to configure required parameters to ensure the best possible outcomes from graphene-based materials. Until now, several review articles have been published to summarise the excellence of graphene and its derivatives, which focused mainly on a single application area of graphene. However, no single review is found to comprehensively study most used fabrication processes of graphene-based materials including their diversified and potential application areas. To address this genuine gap and ensure wider support for the upcoming research and investigations of this excellent material, this review aims to provide a snapshot of most used fabrication methods of graphene-based materials in the form of pure and composite fibres, graphene-based composite materials conjugated with polymers, and fibres. This study also provides a clear perspective of large-scale production feasibility and application areas of graphene-based materials in all forms. Graphene ; Nanotechnology ; Materials science ; Aerospace ; Fabrication ; Computer science ; Engineering ; Aerospace engineering Graphene research and applications ; Graphene and Nanomaterials Applications ; Supercapacitor Materials and Fabrication
Authors: Ao Li | Dezhong Xu | Lu Luo | Yalan Zhou | Yan Wen | Xin Leng | Dasong Dai | Yonghui Zhou | Hassan Ahmad | Jiuping Rao | Mizi Fan
Source: Nanotechnology Reviews (article, 2021-01-01)
Field: Materials Science — Biomaterials

Overview of nanocellulose as additives in paper processing and paper products Abstract The rapid economic growth and environmental concerns have led to high demands on paper and paper-based products in terms of variety, quantity, quality, and specialty. Enhancement and functionalization with additives are constantly required. Moving away from traditional petroleum-based additives, researchers have attempted to use “green” nanoadditives by introducing renewable environmentally friendly nanocellulose. This article studies the functions of nanocellulose as bio-additives (enhancer, retention and filtration reagent, and coating aid) in paper and paper products, and overviews the research development of nanocellulose-based additives and their applications in the paper industry for both efficient production and paper functionalization. The review shows that (1) a variety of nanocellulose-based bioadditives have been reported for various applications in paper and paper-based products, while commercially viable developments are to be advanced; (2) nanocellulose was mostly formulated with other polymer and particles as additives to achieve their synergistic effects; (3) major interests have concentrated on the nanocellulose in the specialty papers as representing more value added products and in the efficient utilization of recycled fibers, which remains most attractive and promising for future development. This report shall provide most useful database information for researchers and industries for paper recycling and enhancement, and paper-based products innovation and application. Nanocellulose ; Cosmetics ; Biochemical engineering ; Speciality chemicals ; Environmentally friendly ; Nanotechnology ; Process engineering ; Environmental science ; Materials science ; Chemistry ; Engineering ; Cellulose ; Chemical engineering ; Environmental engineering ; Organic chemistry Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Material Properties and Processing
Authors: Shi Jun Ang | Wujie Wang | Daniel Schwalbe‐Koda | Simon Axelrod | Rafael Gómez‐Bombarelli
Source: Chem (article, 2021-01-10)
Field: Materials Science — Materials Chemistry

Active learning accelerates ab initio molecular dynamics on reactive energy surfaces Ab initio ; Molecular dynamics ; Dynamics (music) ; Computational chemistry ; Chemical physics ; Materials science ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Aryan Deshwal | Cory M. Simon | Janardhan Rao Doppa
Source: Molecular Systems Design & Engineering (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Bayesian optimization of nanoporous materials In Bayesian optimization, we efficiently search for an optimal material by iterating between (i) conducting an experiment on a material, (ii) updating our knowledge, and (iii) selecting the next material for an experiment. Bayesian optimization ; Nanoporous ; Bayesian probability ; Computer science ; Materials science ; Artificial intelligence ; Nanotechnology Machine Learning in Materials Science ; Machine Learning and Algorithms ; Metal-Organic Frameworks: Synthesis and Applications