jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prahlad K. Routh | Yang Liu | Nicholas Marcella | Boris Kozinsky | Anatoly I. Frenkel
Source: The Journal of Physical Chemistry Letters (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

Latent Representation Learning for Structural Characterization of Catalysts Supervised machine learning-enabled mapping of the X-ray absorption near edge structure (XANES) spectra to local structural descriptors offers new methods for understanding the structure and function of working nanocatalysts. We briefly summarize a status of XANES analysis approaches by supervised machine learning methods. We present an example of an autoencoder-based, unsupervised machine learning approach for latent representation learning of XANES spectra. This new approach produces a lower-dimensional latent representation, which retains a spectrum-structure relationship that can be eventually mapped to physicochemical properties. The latent space of the autoencoder also provides a pathway to interpret the information content "hidden" in the X-ray absorption coefficient. Our approach (that we named latent space analysis of spectra, or LSAS) is demonstrated for the supported Pd nanoparticle catalyst studied during the formation of Pd hydride. By employing the low-dimensional representation of Pd K-edge XANES, the LSAS method was able to isolate the key factors responsible for the observed spectral changes. Autoencoder ; XANES ; Representation (politics) ; Computer science ; Feature learning ; Pattern recognition (psychology) ; Artificial intelligence ; Unsupervised learning ; X-ray absorption fine structure ; Machine learning ; Spectral line ; Materials science ; Artificial neural network ; Physics ; Spectroscopy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Rafaela Maria Giappa | Emmanuel Tylianakis | Marco Di Gennaro | Konstantinos Gkagkas | George E. Froudakis
Source: International Journal of Hydrogen Energy (article, 2021-06-27)
Field: Chemistry — Inorganic Chemistry

A combination of multi-scale calculations with machine learning for investigating hydrogen storage in metal organic frameworks Hydrogen storage ; Ab initio ; Density functional theory ; Ab initio quantum chemistry methods ; Surface modification ; Metal-organic framework ; Binding energy ; Hydrogen ; Monte Carlo method ; Computational chemistry ; Materials science ; Chemistry ; Chemical physics ; Physical chemistry ; Molecule ; Atomic physics ; Physics ; Organic chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Zhongwei Zhang | Yangyu Guo | Marc Bescond | Jie Chen | Masahiro Nomura | Sébastian Volz
Source: APL Materials (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

Coherent thermal transport in nano-phononic crystals: An overview Nano-phononic crystals have attracted a great deal of research interest in the field of nanoscale thermal transport due to their unique coherent thermal transport behavior. So far, there have been many advances in the theory and simulation studies of coherent thermal transport in nano-phononic crystals. In this paper, we summarize the state-of-the-art studies in this field from the perspective of coherent thermal transport at low temperatures, minimum thermal conductivity, Anderson localization, in various nanosystems, and in the frame of machine learning driven studies. Each part is specifically presented under different simulation methodologies, in which the background theories are also summarized. Accordingly, the controversies between different methodologies in describing wave-like/coherent and particle-like/incoherent thermal phonons are discussed. Various effects on coherent thermal transport are reviewed, including interface roughness, mass disorder, structural randomness, aperiodic ordering, and temperature effect. Finally, an outlook on the future research on coherent thermal transport in nano-phononic crystals is given. This overview provides fundamental and advanced knowledge to understand the coherent thermal transport in nano-phononic crystals, which will be beneficial to the further understanding of the physical picture of thermal phonons and heat transfer related applications. Phonon ; Thermal conductivity ; Materials science ; Thermal ; Field (mathematics) ; Nanotechnology ; Engineering physics ; Condensed matter physics ; Physics ; Thermodynamics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Thermoelastic and Magnetoelastic Phenomena
Authors: Stas Zinchik | Shengli Jiang | Søren Friis | Fei Long | Lasse Høgstedt | Ví­ctor M. Zavala | Ezra Bar‐Ziv
Source: ACS Sustainable Chemistry & Engineering (article, 2021-10-12)
Field: Chemistry — Analytical Chemistry

Accurate Characterization of Mixed Plastic Waste Using Machine Learning and Fast Infrared Spectroscopy We present a combination of convolutional neural network (CNN) framework and fast MIR (mid-infrared spectroscopy) for classifying different types of dark plastic materials that are commonly found in mixed plastic waste (MPW) streams. Dark plastic materials present challenges in fast identification because of the low signal-to-noise ratio. The proposed CNN architecture (which we call PlasticNet) can reach an overall classification accuracy of 100% and can identify the constituent materials in a multiplastic blend with 100% accuracy. The fast MIR system can collect spectral data at a rate up to 400 Hz, and the CNN model can reach prediction speeds of 8200 Hz. Therefore, this method provides an avenue to be able to characterize MPW in a real-time high-throughput manner. Convolutional neural network ; Plastic waste ; Computer science ; Throughput ; Infrared ; Spectroscopy ; Characterization (materials science) ; Infrared spectroscopy ; Artificial intelligence ; Materials science ; Biological system ; Pattern recognition (psychology) ; Process engineering ; Chemistry ; Nanotechnology ; Optics ; Physics ; Engineering ; Telecommunications ; Waste management ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: R. E. Ryltsev | N. M. Chtchelkatchev
Source: Journal of Molecular Liquids (article, 2021-11-26)
Field: Materials Science — Materials Chemistry

Deep machine learning potentials for multicomponent metallic melts: Development, predictability and compositional transferability Predictability ; Transferability ; Artificial intelligence ; Metal ; Computer science ; Machine learning ; Materials science ; Metallurgy ; Mathematics ; Statistics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Yizhen Chen | Rachita Rana | Tyler Sours | Fernando D. Vila | Shaohong Cao | Thomas Blum | Jiyun Hong | Adam S. Hoffman | Chia‐Yu Fang | Zhennan Huang | Chunyan Shang | Chuanhao Wang | Jie Zeng | Miaofang Chi | Coleman X. Kronawitter | Simon R. Bare | Bruce C. Gates | Ambarish Kulkarni
Source: Journal of the American Chemical Society (article, 2021-11-22)
Field: Materials Science — Materials Chemistry

A Theory-Guided X-ray Absorption Spectroscopy Approach for Identifying Active Sites in Atomically Dispersed Transition-Metal Catalysts Atomically dispersed supported metal catalysts offer new properties and the benefits of maximized metal accessibility and utilization. The characterization of these materials, however, remains challenging. Using atomically dispersed platinum supported on crystalline MgO (chosen for its well-defined bonding sites) as a prototypical example, we demonstrate how systematic density functional theory calculations for assessing all the potentially stable platinum sites, combined with automated analysis of extended X-ray absorption fine structure (EXAFS) spectra, leads to unbiased identification of isolated, surface-enveloped platinum cations as the catalytic species for CO oxidation. The catalyst has been characterized by atomic-resolution imaging and EXAFS and high-energy resolution fluorescence detection X-ray absorption near edge spectroscopy. The proposed platinum sites are in agreement with experiment. This theory-guided workflow leads to rigorously determined structural models and provides a more detailed picture of the structure of the catalytically active site than what is currently possible with conventional EXAFS analyses. As this approach is efficient and agnostic to the metal, support, and catalytic reaction, we posit that it will be of broad interest to the materials characterization and catalysis communities. Extended X-ray absorption fine structure ; Catalysis ; Chemistry ; Platinum ; Transition metal ; Absorption spectroscopy ; Density functional theory ; X-ray absorption spectroscopy ; Spectroscopy ; Characterization (materials science) ; Metal ; X-ray spectroscopy ; Absorption (acoustics) ; XANES ; Chemical physics ; Nanotechnology ; Crystallography ; Materials science ; Computational chemistry ; Organic chemistry ; Optics Catalytic Processes in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Ganesh Sivaraman | Leighanne C. Gallington | Anand Narayanan Krishnamoorthy | Marius Stan | Gábor Cśanyi | Álvaro Vázquez‐Mayagoitia | Chris J. Benmore
Source: Physical Review Letters (article, 2021-04-14)
Field: Materials Science — Materials Chemistry

Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide Understanding the structure and properties of refractory oxides is critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active learner, which is initialized by x-ray and neutron diffraction measurements, and sequentially improves a machine-learning model until the experimentally predetermined phase space is covered. A multiphase potential is generated for a canonical example of the archetypal refractory oxide, HfO_{2}, by drawing a minimum number of training configurations from room temperature to the liquid state at ∼2900 °C. The method significantly reduces model development time and human effort. Refractory (planetary science) ; Computer science ; Oxide ; Work (physics) ; Phase (matter) ; Materials science ; Neutron diffraction ; Interatomic potential ; Diffraction ; Computational science ; Simulation ; Thermodynamics ; Physics ; Optics ; Molecular dynamics ; Composite material ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Puneet Mishra | Dário Passos
Source: Analytica Chimica Acta (article, 2021-04-16)
Field: Chemistry — Analytical Chemistry

Deep multiblock predictive modelling using parallel input convolutional neural networks In the domain of chemometrics, multiblock data analysis is widely performed for exploring or fusing data from multiple sources. Commonly used methods for multiblock predictive analysis are the extensions of latent space modelling approaches. However, recently, deep learning (DL) approaches such as convolutional neural networks (CNNs) have outperformed the single block traditional latent space modelling chemometric approaches such as partial least-square (PLS) regression. The CNNs based DL modelling can also be performed to simultaneously deal with the multiblock data but was never explored until this study. Hence, this study for the first time presents the concept of parallel input CNNs based DL modelling for multiblock predictive chemometric analysis. The parallel input CNNs based DL modelling utilizes individual convolutional layers for each data block to extract key features that are later combined and passed to a regression module composed of fully connected layers. The method was tested on a real visible and near-infrared (Vis-NIR) large data set related to dry matter prediction in mango fruit. To have the multiblock data, the visible (Vis) and near-infrared (NIR) parts were treated as two separate blocks. The performance of the parallel input CNN was compared with the traditional single block CNNs based DL modelling, as well as with a commonly used multiblock chemometric approach called sequentially orthogonalized partial least-square (SO-PLS) regression. The results showed that the proposed parallel input CNNs based deep multiblock analysis outperformed the single block CNNs based DL modelling and the SO-PLS regression analysis. The root means squared errors of prediction obtained with deep multiblock analysis was 0.818%, relatively lower by 4 and 20% than single block CNNs and SO-PLS regression, respectively. Furthermore, the deep multiblock approach attained ∼3% lower RMSE compared to the best known on the mango data set used for this study. The deep multiblock analysis approach based on parallel input CNNs could be considered as a useful tool for fusing data from multiple sources. Partial least squares regression ; Block (permutation group theory) ; Convolutional neural network ; Chemometrics ; Pattern recognition (psychology) ; Artificial intelligence ; Regression analysis ; Artificial neural network ; Regression ; Latent variable ; Computer science ; Chemistry ; Machine learning ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Soon Wei Daniel Lim | Joon‐Suh Park | Maryna L. Meretska | Ahmed H. Dorrah | Federico Capasso
Source: Nature Communications (article, 2021-07-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineering phase and polarization singularity sheets Optical phase singularities are zeros of a scalar light field. The most systematically studied class of singular fields is vortices: beams with helical wavefronts and a linear (1D) singularity along the optical axis. Beyond these common and stable 1D topologies, we show that a broader family of zero-dimensional (point) and two-dimensional (sheet) singularities can be engineered. We realize sheet singularities by maximizing the field phase gradient at the desired positions. These sheets, owning to their precise alignment requirements, would otherwise only be observed in rare scenarios with high symmetry. Furthermore, by applying an analogous procedure to the full vectorial electric field, we can engineer paraxial transverse polarization singularity sheets. As validation, we experimentally realize phase and polarization singularity sheets with heart-shaped cross-sections using metasurfaces. Singularity engineering of the dark enables new degrees of freedom for light-matter interaction and can inspire similar field topologies beyond optics, from electron beams to acoustics. Singularity ; Gravitational singularity ; Optical vortex ; Paraxial approximation ; Physics ; Polarization (electrochemistry) ; Scalar field ; Scalar (mathematics) ; Wavefront ; Topology (electrical circuits) ; Classical mechanics ; Isotropy ; Vortex ; Optics ; Geometry ; Quantum mechanics ; Mathematics ; Mechanics Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Mohammad Nahid Siddiqui | Halim Hamid Redhwi | Ioannis Tsagkalias | Evangelia C. Vouvoudi | Dimitris S. Achilias
Source: Polymers (article, 2021-10-23)
Field: Materials Science — Biomaterials

Development of Bio-Composites with Enhanced Antioxidant Activity Based on Poly(lactic acid) with Thymol, Carvacrol, Limonene, or Cinnamaldehyde for Active Food Packaging The new trend in food packaging films is to use biodegradable or bio-based polymers, such as poly(lactic acid), PLA with additives such as thymol, carvacrol, limonene or cinnamaldehyde coming from natural resources (i.e., thyme, oregano, citrus fruits and cinnamon) in order to extent foodstuff shelf-life and improve consumers' safety. Single, triple and quadruple blends of these active compounds in PLA were prepared and studied using the solvent-casting technique. The successful incorporation of the active ingredients into the polymer matrix was verified by FTIR spectroscopy. XRD and DSC data revealed that the crystallinity of PLA was not significantly affected. However, the Tg of the polymer decreased, verifying the plasticization effect of all additives. Multicomponent mixtures resulted in more intense plasticization. Cinnamaldehyde was found to play a catalytic role in the thermal degradation of PLA shifting curves to slightly lower temperatures. Release of thymol or carvacrol from the composites takes place at low rates at temperatures below 100 °C. A combined diffusion-model was found to simulate the experimental release profiles very well. Higher antioxidant activity was noticed when carvacrol was added, followed by thymol and then cinnamaldehyde and limonene. From the triple-component composites, higher antioxidant activity measured in the materials with thymol, carvacrol and cinnamaldehyde. Thymol ; Carvacrol ; Cinnamaldehyde ; Plasticizer ; Eugenol ; Active packaging ; Limonene ; Essential oil ; Materials science ; Lactic acid ; Antioxidant ; Chemistry ; Food packaging ; Organic chemistry ; Food science ; Catalysis biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution
Authors: Antonio Pulcinella | Daniele Mazzarella | Timothy Noël
Source: Chemical Communications (review, 2021-01-01)
Field: Chemistry — Organic Chemistry

Homogeneous catalytic C(sp<sup>3</sup>)–H functionalization of gaseous alkanes The conversion of light alkanes into bulk chemicals is becoming an important challenge as it effectively avoids the use of prefunctionalized alkylating reagents. The implementation of such processes is, however, hampered by their gaseous nature and low solubility, as well as the low reactivity of the C-H bonds. Efforts have been made to enable both polar and radical processes to activate these inert compounds. In addition, these methodologies also benefit significantly from the development of a suitable reactor technology that intensifies gas-liquid mass transfer. In this review, we critically highlight these developments, both from a conceptual and a practical point of view. The recent expansion of these mechanistically-different methods have enabled the use of various gaseous alkanes for the development of different bond-forming reactions, including C-C, C-B, C-N, C-Si and C-S bonds. Surface modification ; Catalysis ; Homogeneous ; Chemistry ; Organic chemistry ; Physical chemistry ; Physics Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Wanjia Xu | Xiu‐Mei Li | Liang Wang | Siyuan Li | Shengnan Chu | Jiachun Wang | Yijia Li | Jinxing Hou | Quan Luo | Junqiu Liu
Source: Frontiers in Chemistry (review, 2021-02-17)
Field: Materials Science — Biomaterials

Design of Cyclodextrin-Based Functional Systems for Biomedical Applications Cyclodextrins (CDs) are a family of α-1,4-linked cyclic oligosaccharides that possess a hydrophobic cavity and a hydrophilic outer surface with abundant hydroxyl groups. This unique structural characteristic allows CDs to form inclusion complexes with various guest molecules and to functionalize with different substituents for the construction of novel sophisticated systems, ranging from derivatives to polymers, metal-organic frameworks, hydrogels, and other supramolecular assemblies. The excellent biocompatibility, selective recognition ability, and unique bioactive properties also make these CD-based functional systems especially attractive for biomedical applications. In this review, we highlight the characteristics and advantages of CDs as a starting point to design different functional materials and summarize the recent advances in the use of these materials for bioseparation, enzymatic catalysis, biochemical sensing, biomedical diagnosis and therapy. Biocompatibility ; Supramolecular chemistry ; Cyclodextrin ; Nanotechnology ; Self-healing hydrogels ; Materials science ; Molecular recognition ; Combinatorial chemistry ; Cancer therapy ; Surface modification ; Polymer ; Molecule ; Chemistry ; Organic chemistry ; Polymer chemistry ; Cancer Supramolecular Self-Assembly in Materials ; Nanoparticle-Based Drug Delivery ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Seon-Chil Kim
Source: Applied Sciences (article, 2021-04-28)
Field: Materials Science — Materials Chemistry

Analysis of Shielding Performance of Radiation-Shielding Materials According to Particle Size and Clustering Effects In the field of medical radiation shielding, there is an extensive body of research on process technologies for ecofriendly shielding materials that could replace lead. In particular, the particle size and arrangement of the shielding material when blended with a polymer material affect shielding performance. In this study, we observed how the particle size of the shielding material affects shielding performance. Performance and particle structure were observed for every shielding sheet, which were fabricated by mixing microparticles and nanoparticles with a polymer material using the same process. We observed that the smaller the particle size was, the higher both the clustering and shielding effects in the high-energy region. Thus, shielding performance can be improved. In the low-dose region, the effect of particle size on shielding performance was insignificant. Moreover, the shielding sheet in which nanoparticles and microsized particles were mixed showed similar performance to that of the shielding sheet containing only microsized particles. Findings indicate that, when fabricating a shielding sheet using a polymer material, the smaller the particles in the high-energy region are, the better the shielding performance is. However, in the low-energy region, the effect of the particles is insignificant. Electromagnetic shielding ; Materials science ; Particle size ; Particle (ecology) ; Shielding effect ; Composite material ; Polymer ; Chemical engineering Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Radiation Dose and Imaging
Authors: Youhong Guo | Zhiwei Fang | Guihua Yu
Source: Polymer International (article, 2021-06-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional hydrogels for sustainable energy and environment Abstract This review gives an overview of the research presented in the lecture by Professor Guihua Yu for the 2020 Polymer International‐IUPAC Award for Creativity in Polymer Science. Hydrogels with intriguing physicochemical properties emerge as an attractive materials platform for sustainable energy storage and conversion as well as clean water production. Here we describe some highlights of key building blocks and synthetic approaches of hydrogel‐based materials in a tunable manner from bulk to the molecular scale. Various strategies for improving electronic/ionic conductivity, controlling swelling/deswelling behaviors and integrating multiple functionalities are discussed. Specific examples of recent achievements of hydrogels for applications in batteries, supercapacitors, electrocatalysis, solar evaporators and moisture harvesters are presented. These multifunctional hydrogels with highly tailorable architectures and physicochemical properties will become useful in next‐generation sustainable energy and water technologies. © 2021 Society of Industrial Chemistry. Self-healing hydrogels ; Supercapacitor ; Nanotechnology ; Materials science ; Energy storage ; Polymer ; Chemistry ; Polymer chemistry ; Physics ; Composite material Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Solar-Powered Water Purification Methods