jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: O.A. Egorova | Roohollah Hafizi | David C. Woods | Graeme M. Day
Source: The Journal of Physical Chemistry A (article, 2020-09-03)
Field: Materials Science — Materials Chemistry

Multifidelity Statistical Machine Learning for Molecular Crystal Structure Prediction The prediction of crystal structures from first-principles requires highly accurate energies for large numbers of putative crystal structures. High accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or more structures can be present in the low energy region of interest, so that the associated computational costs are prohibitive. Here, we apply statistical machine learning to predict expensive hybrid functional DFT (PBE0) calculations using a multifidelity approach to re-evaluate the energies of crystal structures predicted with an inexpensive force field. The method uses an autoregressive Gaussian process, making use of less expensive GGA DFT (PBE) calculations to bridge the gap between the force field and PBE0 energies. The method is benchmarked on the crystal structure landscapes of three small, hydrogen-bonded organic molecules and shown to produce accurate predictions of energies and crystal structure ranking using small numbers of the most expensive calculations; the PBE0 energies can be predicted with errors of less than 1 kJ mol<sup>-1</sup> with between 4.2 and 6.8% of the cost of the full calculations. As the model that we have developed is probabilistic, we discuss how the uncertainties in predicted energies impact the assessment of the energetic ranking of crystal structures. Crystal structure prediction ; Density functional theory ; Crystal (programming language) ; Statistical physics ; Probabilistic logic ; Crystal structure ; Gaussian ; Force field (fiction) ; Field (mathematics) ; Computer science ; Algorithm ; Materials science ; Physics ; Computational chemistry ; Chemistry ; Artificial intelligence ; Mathematics ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Geun Ho Gu | Changhyeok Choi | Yeunhee Lee | Andres Bethavan Situmorang | Juhwan Noh | Yong‐Hyun Kim | Yousung Jung
Source: Advanced Materials (review, 2020-03-20)
Field: Materials Science — Materials Chemistry

Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation The chemical conversion of small molecules such as H<sub>2</sub> , H<sub>2</sub> O, O<sub>2</sub> , N<sub>2</sub> , CO<sub>2</sub> , and CH<sub>4</sub> to energy and chemicals is critical for a sustainable energy future. However, the high chemical stability of these molecules poses grand challenges to the practical implementation of these processes. In this regard, computational approaches such as density functional theory, microkinetic modeling, data science, and machine learning have guided the rational design of catalysts by elucidating mechanistic insights, identifying active sites, and predicting catalytic activity. Here, the theory and methodologies for heterogeneous catalysis and their applications for small-molecule activation are reviewed. An overview of fundamental theory and key computational methods for designing catalysts, including the emerging data science techniques in particular, is given. Applications of these methods for finding efficient heterogeneous catalysts for the activation of the aforementioned small molecules are then surveyed. Finally, promising directions of the computational catalysis field for further outlooks are discussed, focusing on the challenges and opportunities for new methods. Catalysis ; Biochemical engineering ; Density functional theory ; Nanotechnology ; Molecule ; Rational design ; Heterogeneous catalysis ; Materials science ; Computer science ; Small molecule ; Computational model ; Field (mathematics) ; Computational chemistry ; Chemistry ; Artificial intelligence ; Organic chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Shruti Venkatram | Rohit Batra | Lihua Chen | Chiho Kim | Madeline Shelton | Rampi Ramprasad
Source: The Journal of Physical Chemistry B (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

Predicting Crystallization Tendency of Polymers Using Multifidelity Information Fusion and Machine Learning The degree of crystallinity of a polymer is a critical parameter that controls a variety of polymer properties. A high degree of crystallinity is associated with excellent mechanical properties crucial for high-performing applications like composites. Low crystallinity promotes ion and gas mobility critical for battery and membrane applications. Experimental determination of the crystallinity for new polymers is time and cost intensive. A data-driven machine learning-based method capable of rapidly predicting the crystallinity could counter these disadvantages and be used to screen polymers for a myriad of applications in a fast, inexpensive fashion. In this work, we developed the first-of-its-kind, data-driven machine learning model to predict the most-likely polymer crystallinity trained on experimental data and theoretical group contribution methods. Since polymer data under consistent processing conditions are unavailable, we tackled process variability by using the "most-likely" polymer values which we refer to as the polymer's tendency to crystallize. Experimental data for polymers' tendency to crystallize is limited by number and diversity, and to tackle this, we augmented experimentation-based data with data using group contribution methods. Therefore, this work utilized two data sets, viz., a high-fidelity, experimental data set for 107 polymers and a more diverse, less accurate low-fidelity data set for 429 polymers which used group contribution methods. We used a multifidelity information fusion strategy to utilize all the information captured in the low-fidelity data set while still predicting at the high-fidelity accuracy. Although this model inherently assumed "typical" processing conditions and estimated the "most-likely" percent crystallinity value, it can help in the estimation of a polymer's tendency to crystallize in a far more cost-effective and efficient manner. Crystallinity ; Polymer ; Crystallization of polymers ; Materials science ; Fidelity ; Crystallization ; Computer science ; Machine learning ; Biological system ; Nanotechnology ; Artificial intelligence ; Engineering ; Chemical engineering ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Santosh Behara | Taher Poonawala | Tiju Thomas
Source: Computational Materials Science (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Crystal structure classification in ABO3 perovskites via machine learning Tetragonal crystal system ; Crystal structure ; Crystal structure prediction ; Electronegativity ; Orthorhombic crystal system ; Valence (chemistry) ; Materials science ; Ionic radius ; Machine learning ; Crystal (programming language) ; Crystallography ; Artificial intelligence ; Computer science ; Chemistry ; Ion X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystal Structures and Properties
Authors: Christopher Sutton | Sergey V. Levchenko
Source: Frontiers in Chemistry (review, 2020-12-03)
Field: Materials Science — Materials Chemistry

First-Principles Atomistic Thermodynamics and Configurational Entropy In most applications, functional materials operate at finite temperatures and are in contact with a reservoir of atoms or molecules (gas, liquid, or solid). In order to understand the properties of materials at realistic conditions, statistical effects associated with configurational sampling and particle exchange at finite temperatures must consequently be taken into account. In this contribution, we discuss the main concepts behind equilibrium statistical mechanics. We demonstrate how these concepts can be used to predict the behavior of materials at realistic temperatures and pressures within the framework of atomistic thermodynamics. We also introduce and discuss methods for calculating phase diagrams of bulk materials and surfaces as well as point defect concentrations. In particular, we describe approaches for calculating the configurational density of states, which requires the evaluation of the energies of a large number of configurations. The cluster expansion method is therefore also discussed as a numerically efficient approach for evaluating these energies. Statistical physics ; Configuration entropy ; Thermodynamics ; Cluster expansion ; Statistical mechanics ; Phase diagram ; Entropy (arrow of time) ; Physics ; Materials science ; Phase (matter) Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Crystallization and Solubility Studies
Authors: Jin Liu | Dong Cao | Haoxiang Xu | Daojian Cheng
Source: Nano Select (article, 2020-12-02)
Field: Materials Science — Materials Chemistry

From double‐atom catalysts to single‐cluster catalysts: A new frontier in heterogeneous catalysis Abstract In recent years, atomically dispersed metal catalysts with well‐defined structures have attracted great interest in heterogeneous catalysis due to their high atomic utilization efficiency, activity, stability, and selectivity. The rapid development of single‐atom catalysts (SACs) has simultaneously stimulated the emergence of diatomic catalysts (DACs) and single cluster catalysts (SCCs). Compared with SACs, DACs, and SCCs possess higher metal loading and more structurally flexible active sites, which provide great potential for achieving higher catalytic performance. DACs and SCCs have become a new field of heterogeneous catalysis. In this review, we first focus on the latest developments of DACs/SCCs, including synthesis methods and characterization approaches including experimental and theoretical tools. In addition, the relationship between structure and catalytic performance of DACs/SCCs are well discussed, including the effect of supports, synergistic metal atoms, and coordination environment. At last, the similarities and differences between SACs and DACs/SCCs are systematically summarized and analyzed. Catalysis ; Cluster (spacecraft) ; Nanotechnology ; Characterization (materials science) ; Atom (system on chip) ; Selectivity ; Materials science ; Metal ; Chemistry ; Computer science ; Organic chemistry ; Metallurgy ; Parallel computing Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion
Authors: Md Sharafat Hossain | Tianzhi Li | Yang Yu | Jason Yong | Je‐Hyeong Bahk | Efstratios Skafidas
Source: RSC Advances (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Recent advances in printable thermoelectric devices: materials, printing techniques, and applications Thermoelectric devices have great potential as a sustainable energy conversion technology to harvest waste heat and perform spot cooling with high reliability. However, most of the thermoelectric devices use toxic and expensive materials, which limits their application. These materials also require high-temperature fabrication processes, limiting their compatibility with flexible, bio-compatible substrate. Printing electronics is an exciting new technique for fabrication that has enabled a wide array of biocompatible and conformable systems. Being able to print thermoelectric devices allows them to be custom made with much lower cost for their specific application. Significant effort has been directed toward utilizing polymers and other bio-friendly materials for low-cost, lightweight, and flexible thermoelectric devices. Fortunately, many of these materials can be printed using low-temperature printing processes, enabling their fabrication on biocompatible substrates. This review aims to report the recent progress in developing high performance thermoelectric inks for various printing techniques. In addition to the usual thermoelectric performance measures, we also consider the attributes of flexibility and the processing temperatures. Finally, recent advancement of printed device structures is discussed which aims to maximize the temperature difference across the junctions. Fabrication ; Thermoelectric effect ; Electronics ; Thermoelectric materials ; Conformable matrix ; Materials science ; Biocompatible material ; Compatibility (geochemistry) ; Nanotechnology ; Printed electronics ; Limiting ; Thermoelectric generator ; Process engineering ; Mechanical engineering ; Electrical engineering ; Engineering ; Thermal conductivity ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Poorya Chavoshnejad | Mir Jalil Razavi
Source: Scientific Reports (article, 2020-05-07)
Field: Materials Science — Biomaterials

Effect of the Interfiber Bonding on the Mechanical Behavior of Electrospun Fibrous Mats Electrospun fibrous mats, characterized by their large surface-to-volume ratios, have unique and beneficial properties for various applications. The micro or nanoscale architectures of these structures significantly affects the mechanical properties of the material. The lack of knowledge for predicting the mechanical behavior of electrospun fibrous mats may prevent applications utilizing the se mats from reaching their full potential. In this paper, we propose a new computational model to predict the mechanical behavior of an electrospun fibrous mat by considering its microstructure and the percentage of the cross-points that are bonded. The model of the electrospun mat with randomly distributed fibers is considered in uniaxial and biaxial tension. Three cases are studied: (1) no interaction in cross-points of intersecting fibers, (2) half of the cross-points are bonded, (3) all of the cross-points are bonded. The results show that along with the mechanical properties of individual fibers, the fusion bonding of fibers is a critical parameter for tuning the mechanical properties of the bulk material. In a predefined porosity, the interfiber fusion enhanced the stiffness of the mat by 60%, which is independent of the loading mode and the mechanical property of individual fibers. For all ranges of porosities, bonding increases the stiffness of the mat; however, the bonding is more effective at stiffening when the porosity of the mat is low. Materials science ; Composite material ; Stiffness ; Porosity ; Fusion ; Microstructure ; Electrospinning ; Fiber ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Silk-based biomaterials and applications
Authors: Aurelia Li | Rocío Bueno-Pérez | Seth B. Wiggin | David Fairen‐Jiménez
Source: CrystEngComm (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Enabling efficient exploration of metal–organic frameworks in the Cambridge Structural Database A tutorial review for mining the ever growing number of metal–organic frameworks data in the Cambridge Structural Database, for MOF scientists of all backgrounds. Metal-organic framework ; Computer science ; Database ; Data science ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Magnetism in coordination complexes
Authors: Bernd K. Wittmann | Felix A. Wenzel | Stephan Wiesneth | Andreas T. Haedler | Markus Drechsler | Klaus Kreger | Jürgen Köhler | E. W. Meijer | Hans‐Werner Schmidt | Richard Hildner
Source: Journal of the American Chemical Society (article, 2020-04-11)
Field: Materials Science — Biomaterials

Enhancing Long-Range Energy Transport in Supramolecular Architectures by Tailoring Coherence Properties Efficient long-range energy transport along supramolecular architectures of functional organic molecules is a key step in nature for converting sunlight into a useful form of energy. Understanding and manipulating these transport processes on a molecular and supramolecular scale is a long-standing goal. However, the realization of a well-defined system that allows for tuning morphology and electronic properties as well as for resolution of transport in space and time is challenging. Here we show how the excited-state energy landscape and thus the coherence characteristics of electronic excitations can be modified by the hierarchical level of H-type supramolecular architectures. We visualize, at room temperature, long-range incoherent transport of delocalized singlet excitons on pico- to nanosecond time scales in single supramolecular nanofibers and bundles of nanofibers. Increasing the degree of coherence, i.e., exciton delocalization, via supramolecular architectures enhances exciton diffusivities up to 1 order of magnitude. In particular, we find that single supramolecular nanofibers exhibit the highest diffusivities reported for H-aggregates so far. Supramolecular chemistry ; Delocalized electron ; Exciton ; Chemistry ; Coherence (philosophical gambling strategy) ; Chemical physics ; Nanofiber ; Nanosecond ; Nanotechnology ; Supramolecular assembly ; Molecule ; Materials science ; Physics ; Optics ; Condensed matter physics Supramolecular Self-Assembly in Materials ; Photoreceptor and optogenetics research ; Luminescence and Fluorescent Materials
Authors: Miljan Milošević | Dušica Stojanović | Vladimir Simić | Mirjana Grković | Miloš Bjelović | Petar S. Uskoković | Miloš Kojić
Source: Scientific Reports (article, 2020-07-07)
Field: Materials Science — Biomaterials

Preparation and modeling of three‐layered PCL/PLGA/PCL fibrous scaffolds for prolonged drug release The authors present the preparation procedure and a computational model of a three-layered fibrous scaffold for prolonged drug release. The scaffold, produced by emulsion/sequential electrospinning, consists of a poly(D,L-lactic-co-glycolic acid) (PLGA) fiber layer sandwiched between two poly(ε-caprolactone) (PCL) layers. Experimental results of drug release rates from the scaffold are compared with the results of the recently introduced computational finite element (FE) models for diffusive drug release from nanofibers to the three-dimensional (3D) surrounding medium. Two different FE models are used: (1) a 3D discretized continuum and fibers represented by a simple radial one-dimensional (1D) finite elements, and (2) a 3D continuum discretized by composite smeared finite elements (CSFEs) containing the fiber smeared and surrounding domains. Both models include the effects of polymer degradation and hydrophobicity (as partitioning) of the drug at the fiber/surrounding interface. The CSFE model includes a volumetric fraction of fibers and diameter distribution, and is additionally enhanced by using correction function to improve the accuracy of the model. The computational results are validated on Rhodamine B (fluorescent drug l) and other hydrophilic drugs. Agreement with experimental results proves that numerical models can serve as efficient tools for drug release to the surrounding porous medium or biological tissue. It is demonstrated that the introduced three-layered scaffold delays the drug release process and can be used for the time-controlled release of drugs in postoperative therapy. Scaffold ; Electrospinning ; Materials science ; PLGA ; Nanofiber ; Biomedical engineering ; Composite material ; Chemical engineering ; Polymer ; Nanotechnology ; Nanoparticle Electrospun Nanofibers in Biomedical Applications ; Polymer Surface Interaction Studies ; Graphene and Nanomaterials Applications
Authors: Mahendra Meena | Shalini Pilania | Ajay Pal | Shiwani Mandhania | Bharat Bhushan | Satish Kumar | Gholamreza Gohari | Vinod Saharan͙
Source: Scientific Reports (article, 2020-12-14)
Field: Materials Science — Biomaterials

Cu-chitosan nano-net improves keeping quality of tomato by modulating physio-biochemical responses Minimizing the post-harvest losses in fruits and vegetables is one of the challenging tasks in agriculture. To address this issue, we report nano-net of Cu-chitosan nanoparticles (Cu-chitosan NPs) which has the ability to extend the shelf-life of stored tomato. The application of Cu-chitosan NPs (0.01-0.04%) significantly curtailed microbial decay (< 5 versus > 50% in control), physiological loss in weight (14.36 versus 28.13% in control), respiration rate (0.01173 versus 0.01879 g CO<sub>2</sub> kg<sup>-1</sup> h<sup>-1</sup>) and maintained fruit firmness (34.0 versus 17.33 N in control) during storage. Further, these NPs significantly retarded loss of titratable acidity, retained total soluble solids, total and reducing sugars, lycopene, ascorbic acid and inhibited polyphenol oxidase. Likewise, NPs effectively preserved L<sup>*</sup> (lightness), a* (red/green) and b* (blue/yellow) values and maintained organoleptic score. Scanning electron microscopy study confirmed that Cu-chitosan NPs orchestrate into an invisible-intangible nano-net over tomato surface which may plausibly act as a potential barrier at all possible openings (stem scar, cuticle wax, lenticels, and aquaporins) to control microbial infection, moisture loss, gas exchanges and respiration rate. Overall, nano-net extended keeping quality of tomatoes up to 21 days at room temperature (27 ± 2 °C, 55 ± 2% relative humidity). Chitosan ; Ascorbic acid ; Respiration rate ; Polyphenol oxidase ; Chemistry ; Postharvest ; Relative humidity ; Food science ; Horticulture ; Shelf life ; Respiration ; Botany ; Biochemistry ; Biology Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Plant Surface Properties and Treatments
Authors: Derek Yiren Ong | Dongyang Fan | Darren J. Dixon | Shunsuke Chiba
Source: Angewandte Chemie International Edition (article, 2020-04-24)
Field: Chemistry — Organic Chemistry

Transition‐Metal‐Free Reductive Functionalization of Tertiary Carboxamides and Lactams for α‐Branched Amine Synthesis Abstract A new method for the synthesis of α‐branched amines by reductive functionalization of tertiary carboxamides and lactams is described. The process relies on the efficient and controlled reduction of tertiary amides by a sodium hydride/sodium iodide composite, in situ treatment of the resulting anionic hemiaminal with trimethylsilyl chloride and subsequent coupling with nucleophilic reagents including Grignard reagents and tetrabutylammonium cyanide. The new method exhibits broad functional‐group compatibility, operates under transition‐metal‐free reaction conditions, and is suitable for various synthetic applications on both sub‐millimole and on multigram scales. Chemistry ; Reagent ; Surface modification ; Combinatorial chemistry ; Cyanide ; Hemiaminal ; Transition metal ; Sodium iodide ; Nucleophilic substitution ; Sodium cyanide ; Organic chemistry ; Catalysis Advanced Synthetic Organic Chemistry ; Asymmetric Hydrogenation and Catalysis ; Chemical synthesis and alkaloids
Authors: Dorian J. Mikolajczak | A. Berger | Beate Koksch
Source: Angewandte Chemie International Edition (review, 2020-01-06)
Field: Materials Science — Materials Chemistry

Catalytically Active Peptide–Gold Nanoparticle Conjugates: Prospecting for Artificial Enzymes The self-assembly of peptides onto the surface of gold nanoparticles has emerged as a promising strategy towards the creation of artificial enzymes. The resulting high local peptide density surrounding the nanoparticle leads to cooperative and synergistic effects, which result in rate accelerations and distinct catalytic properties compared to the unconjugated peptide. This Minireview summarizes contributions to and progress made in the field of catalytically active peptide-gold nanoparticle conjugates. The origin of distinct properties, as well as potential applications, are also discussed. Colloidal gold ; Peptide ; Nanoparticle ; Conjugate ; Enzyme ; Nanotechnology ; Chemistry ; Artificial enzyme ; Combinatorial chemistry ; Materials science ; Biophysics ; Biochemistry ; Biology Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques