jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joseph Pugar | Christopher M. Childs | Christine S. Huang | Karl W. Haider | Newell R. Washburn
Source: The Journal of Physical Chemistry B (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Elucidating the Physicochemical Basis of the Glass Transition Temperature in Linear Polyurethane Elastomers with Machine Learning The glass transition temperature (<i>T</i><sub>g</sub>) is a fundamental property of polymers that strongly influences both mechanical and flow characteristics of the material. In many important polymers, configurational entropy of side chains is a dominant factor determining it. In contrast, the thermal transition in polyurethanes is thought to be determined by a combination of steric and electronic factors from the dispersed hard segments within the soft segment medium. Here, we present a machine learning model for the <i>T</i><sub>g</sub> in linear polyurethanes and aim to uncover the underlying physicochemical parameters that determine this. The model was trained on literature data from 43 industrially relevant combinations of polyols and isocyanates using descriptors derived from quantum chemistry, cheminformatics, and solution thermodynamics forming the feature space. Random forest and regularized regression were then compared to build a sparse linear model from six descriptors. Consistent with empirical understanding of polyurethane chemistry, this study indicates the characteristics of isocyanate monomers strongly determine the increase in <i>T</i><sub>g</sub>. Accurate predictions of <i>T</i><sub>g</sub> from the model are demonstrated, and the significance of the features is discussed. The results suggest that the tools of machine learning can provide both physical insights as well as accurate predictions of complex material properties. Glass transition ; Steric effects ; Polymer ; Elastomer ; Polyurethane ; Monomer ; Materials science ; Thermodynamics ; Molecular descriptor ; Statistical physics ; Biological system ; Chemistry ; Polymer science ; Computer science ; Quantitative structure–activity relationship ; Machine learning ; Composite material ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conservation Techniques and Studies
Authors: Oliver Wohlfahrt | Christoph Dellago | Marcello Sega
Source: The Journal of Chemical Physics (article, 2020-10-14)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> structure and thermodynamics of the RPBE-D3 water/vapor interface by neural-network molecular dynamics Aided by a neural network representation of the density functional theory potential energy landscape of water in the Revised Perdew-Burke-Ernzerhof approximation corrected for dispersion, we calculate several structural and thermodynamic properties of its liquid/vapor interface. The neural network speed allows us to bridge the size and time scale gaps required to sample the properties of water along its liquid/vapor coexistence line with unprecedented precision. Representation (politics) ; Non-equilibrium thermodynamics ; Molecular dynamics ; Artificial neural network ; Scale (ratio) ; Interface (matter) ; Energy (signal processing) ; Density functional theory ; Line (geometry) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Phase Equilibria and Thermodynamics
Authors: Prosun Halder | Jayant K. Singh
Source: Energy & Fuels (article, 2020-11-09)
Field: Chemistry — Inorganic Chemistry

High-Throughput Screening of Metal–Organic Frameworks for Ethane–Ethylene Separation Using the Machine Learning Technique A hybrid approach combining machine learning algorithms with molecular simulation is utilized to screen hypothetical metal–organic framework (h-MOF) database for the best material to separate ethane (C2H6) and ethylene (C2H4). In particular, we rationalized the relation between structural and chemical properties of h-MOF with the C2H6/C2H4 selectivity. 8% h-MOFs were chosen randomly from the h-MOF dataset as a training set. The simulations were conducted at 298 K and 1 bar using a multicomponent grand-canonical Monte Carlo method to obtain the C2H6/C2H4 selectivity. Based on the training set, the random forest (RF) model was developed to predict the selectivity of the rest of the h-MOFs. Among all the chemical and structural properties, void fraction plays a significant role in predicting the equilibrium C2H6/C2H4 selectivity. The trained machine learning model can reasonably predict the C2H6/C2H4 selectivity of the remaining h-MOF materials with an RF score of 0.89. Four h-MOFs have shown the best performance, which was compared with the previously discovered materials. The top four h-MOFs were further simulated at different pressures to obtain the adsorption isotherms. Further, the energy contribution of secondary building units and the local density profiles were analyzed to understand the enhanced interaction between h-MOF atoms and C2H6. Selectivity ; Metal-organic framework ; Adsorption ; Ethylene ; Monte Carlo method ; Chemistry ; Materials science ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Physics ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Hyuntae Lim | YounJoon Jung
Source: arXiv (Cornell University) (article, 2020-05-13)
Field: Materials Science — Materials Chemistry

MLSolv-A: A Novel Machine Learning-Based Prediction of Solvation Free Energies from Pairwise Atomistic Interactions Recent advances in machine learning and their applications have lead to the development of diverse structure-property relationship models for crucial chemical properties, and the solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between two atomistic features calculates their interactions. The results on 6,493 experimental measurements achieve outstanding performance and transferability for enlarging training data due to its solvent-non-specific nature. Analysis of the interaction map shows there is a great potential that our model reproduces group contributions on the solvation energy, which makes us believe that the model not only provides the predicted target property but also gives us more detailed physicochemical insights. Pairwise comparison ; Solvation ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Data science ; Chemistry ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Seyed Mohamad Moosavi | Aditya Nandy | Kevin Maik Jablonka | Daniele Ongari | Jon Paul Janet | Peter G. Boyd | Yongjin Lee | Berend Smit | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-05-07)
Field: Chemistry — Inorganic Chemistry

Understanding the Diversity of the Metal-Organic Framework Ecosystem By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are databases with over 500,000 predicted structures. This raises the question whether a new experimental or predicted structure adds new information. For MOF-chemists the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we show how machine learning can be used to quantify similarities of MOFs. This quantification allows us to use techniques from ecology to analyse the chemical diversity of these materials in terms of diversity metrics. In particular, we show that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions. This formalism provides us with a simple and powerful practical guideline to see whether a set of new structures will have the potential for new insights, or constitute a relatively small variation of existing structures. Chemical space ; Formalism (music) ; Computer science ; Diversity (politics) ; Metal-organic framework ; Biochemical engineering ; Chemistry ; Engineering ; Biology ; Bioinformatics ; Sociology Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Takayuki Nishiyama | Atsuto Seko | Isao Tanaka
Source: Physical Review Materials (article, 2020-12-29)
Field: Materials Science — Materials Chemistry

Application of machine learning potentials to predict grain boundary properties in fcc elemental metals Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculation. In this study, we apply machine learning potentials (MLPs) in a recently constructed repository to the prediction of the grain boundary energy in face-centered-cubic elemental metals, i.e., Ag, Al, Au, Cu, Pd, and Pt. The systematic application of machine learning potentials shows that they enable us to predict grain boundary structures and their energies accurately. The grain boundary energies predicted by the MLPs are in agreement with those calculated by DFT, although no grain boundary structures were included in training datasets of the present MLPs. Grain boundary ; Materials science ; Density functional theory ; Boundary (topology) ; Condensed matter physics ; Machine learning ; Metallurgy ; Computational chemistry ; Computer science ; Physics ; Microstructure ; Mathematics ; Mathematical analysis ; Chemistry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Antti Pihlajamäki | Joonas Hämäläinen | Joakim Linja | P. Nieminen | Sami Malola | Tommi Kärkkäinen | Hannu Häkkinen
Source: The Journal of Physical Chemistry A (article, 2020-05-15)
Field: Materials Science — Materials Chemistry

Monte Carlo Simulations of Au <sub>38</sub> (SCH <sub>3</sub> ) <sub>24</sub> Nanocluster Using Distance-Based Machine Learning Methods We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters. The ML potential is trained for Au<sub>38</sub>(SR)<sub>24</sub> by using previously published, density functional theory (DFT) based, molecular dynamics (MD) simulation data on two experimentally characterized structural isomers of the cluster and validated against independent DFT MD simulations. This method opens a door to efficient probing of the configuration space for further investigations of thermal-dependent electronic and optical properties of Au<sub>38</sub>(SR)<sub>24</sub>. Our ML implementation strategy allows for generalization and accuracy control of distance-based ML models for complex nanostructures having several chemical elements and interactions of varying strength. Nanoclusters ; Monte Carlo method ; Molecular dynamics ; Density functional theory ; Cluster (spacecraft) ; Statistical physics ; Generalization ; Thermal ; Materials science ; Computational science ; Physics ; Computer science ; Nanotechnology ; Computational chemistry ; Chemistry ; Mathematics ; Thermodynamics Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Sucheta Swetlana | Nikhil Khatavkar | Abhishek K. Singh
Source: Journal of Materials Science (article, 2020-08-31)
Field: Materials Science — Materials Chemistry

Development of Vickers hardness prediction models via microstructural analysis and machine learning Superalloy ; Vickers hardness test ; Materials science ; Microstructure ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Kugalur Shanmugam Ranjith | Ganji Seeta Rama Raju | Nilesh R. Chodankar | Seyed Majid Ghoreishian | Cheol Hwan Kwak | Yun Suk Huh | Young‐Kyu Han
Source: Nanomaterials (article, 2020-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electroactive Ultra-Thin rGO-Enriched FeMoO4 Nanotubes and MnO2 Nanorods as Electrodes for High-Performance All-Solid-State Asymmetric Supercapacitors A flexible asymmetric supercapacitor (ASC) with high electrochemical performance was constructed using reduced graphene oxide (rGO)-wrapped redox-active metal oxide-based negative and positive electrodes. Thin layered rGO functionality on the positive and the negative electrode surfaces has promoted the feasible surface-active sites and enhances the electrochemical response with a wide operating voltage window. Herein we report the controlled growth of rGO-wrapped tubular FeMoO<sub>4</sub> nanofibers (NFs) via electrospinning followed by surface functionalization as a negative electrode. The tubular structure offers the ultrathin-layer decoration of rGO inside and outside of the tubular walls with uniform wrapping. The rGO-wrapped tubular FeMoO<sub>4</sub> NF electrode exhibited a high specific capacitance of 135.2 F g<sup>-1</sup> in Na<sub>2</sub>SO<sub>4</sub> neutral electrolyte with an excellent rate capability and cycling stability (96.45% in 5000 cycles) at high current density. Meanwhile, the hydrothermally synthesized binder-free rGO/MnO<sub>2</sub> nanorods on carbon cloth (rGO-MnO<sub>2</sub>@CC) were selected as cathode materials due to their high capacitance and high conductivity. Moreover, the ASC device was fabricated using rGO-wrapped FeMoO<sub>4</sub> on carbon cloth (rGO-FeMoO<sub>4</sub>@CC) as the negative electrode and rGO-MnO<sub>2</sub>@CC as the positive electrode (rGO-FeMoO<sub>4</sub>@CC/rGO-MnO<sub>2</sub>@CC). The rationally designed ASC device delivered an excellent energy density of 38.8 W h kg<sup>-1</sup> with a wide operating voltage window of 0.0-1.8 V. The hybrid ASC showed excellent cycling stability of 93.37% capacitance retention for 5000 cycles. Thus, the developed rGO-wrapped FeMoO<sub>4</sub> nanotubes and MnO<sub>2</sub> nanorods are promising hybrid electrode materials for the development of wide-potential ASCs with high energy and power density. Materials science ; Supercapacitor ; Graphene ; Nanorod ; Electrode ; Oxide ; Carbon nanotube ; Electrolyte ; Electrochemistry ; Electrospinning ; Nanotechnology ; Cathode ; Capacitance ; Chemical engineering ; Anode ; Composite material ; Chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Lela Pintarić | Maja Somogyi Škoc | Vanja Ljoljić Bilić | Ivan Pokrovac | Ivan Kosalec | Iva Rezić
Source: Polymers (article, 2020-05-26)
Field: Materials Science — Materials Chemistry

Synthesis, Modification and Characterization of Antimicrobial Textile Surface Containing ZnO Nanoparticles In this research, a textile surface was modified by the sol-gel methodology with a new antimicrobial coating containing nanoparticles active against bacteria resistant to antibiotics. The effect of ultrasonic irradiation power (40 to 90 kHz), the concentration of reagents (nanoparticles, precursor and acids) and time (15 to 72 min) were investigated in relation to the structure, morphology and antimicrobial activity of coatings with zinc oxide nanoparticles. The relationship between the sonocatalytic performance and structure of the resultant modification was established by using various techniques, such as FTIR spectroscopy (FTIR) and scanning electron microscopy with an EDX detector (SEM-EDX), thin-layer chromatography (TLC) and antimicrobial effects were determined on selected model microorganisms. The homogeneity of layers with ZnO nanoparticles on samples was increased by increasing the ultrasonic irradiation power and time. The ultrasonic irradiation unify did not only unify both the structure and the morphology of samples, it also prevented the agglomeration of the nanoparticles. Moreover, under optimal conditions, an antimicrobial coating with ZnO nanoparticles, active against bacterial species <i>S. aureus</i> and <i>E. coli</i> was efficiently prepared. Results of the <i>Time-kill</i> methodology revieled excellent results starting after 6 hours of exposal to antimicrobialy functionalized cellulose polymer. Textile ; Characterization (materials science) ; Antimicrobial ; Surface modification ; Materials science ; Nanoparticle ; Nanotechnology ; Chemical engineering ; Chemistry ; Composite material ; Organic chemistry ; Engineering Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Conducting polymers and applications
Authors: Kaja I. Ritzau‐Reid | Christopher D. Spicer | Amy Gelmi | Christopher L. Grigsby | James F. Ponder | Victoria Bemmer | Adam Creamer | Ramón Vilar | Andrea Serio | Molly M. Stevens
Source: Advanced Functional Materials (article, 2020-08-14)
Field: Materials Science — Polymers and Plastics

An Electroactive Oligo‐EDOT Platform for Neural Tissue Engineering The unique electrochemical properties of the conductive polymer poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) make it an attractive material for use in neural tissue engineering applications. However, inadequate mechanical properties, and difficulties in processing and lack of biodegradability have hindered progress in this field. Here, the functionality of PEDOT:PSS for neural tissue engineering is improved by incorporating 3,4-ethylenedioxythiophene (EDOT) oligomers, synthesized using a novel end-capping strategy, into block co-polymers. By exploiting end-functionalized oligoEDOT constructs as macroinitiators for the polymerization of poly(caprolactone), a block co-polymer is produced that is electroactive, processable, and bio-compatible. By combining these properties, electroactive fibrous mats are produced for neuronal culture via solution electrospinning and melt electrospinning writing. Importantly, it is also shown that neurite length and branching of neural stem cells can be enhanced on the materials under electrical stimulation, demonstrating the promise of these scaffolds for neural tissue engineering. Materials science ; PEDOT:PSS ; Electrospinning ; Neural tissue engineering ; Tissue engineering ; Conductive polymer ; Polymer ; Nanotechnology ; Electroactive polymers ; Biomedical engineering ; Composite material Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Yuan Tian | Ruihao Yuan | Dezhen Xue | Yumei Zhou | Yunfan Wang | Xiangdong Ding | Jun Sun | Turab Lookman
Source: Advanced Science (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi-component systems from a high-dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi-component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1-<i>ω</i>)(Ba<sub>0.61</sub>Ca<sub>0.28</sub>Sr<sub>0.11</sub>TiO<sub>3</sub>)-<i>ω</i>(BaTi<sub>0.888</sub>Zr<sub>0.0616</sub>Sn<sub>0.0028</sub>Hf<sub>0.0476</sub>O<sub>3</sub>) with a relatively high transition temperature and triple point, as well as the NiTi-based pseudo-binary phase diagram (1-<i>ω</i>)(Ti<sub>0.309</sub>Ni<sub>0.485</sub>Hf<sub>0.20</sub>Zr<sub>0.006</sub>)-<i>ω</i>(Ti<sub>0.309</sub>Ni<sub>0.485</sub>Hf<sub>0.07</sub>Zr<sub>0.068</sub>Nb<sub>0.068</sub>) designed for high transition temperature (<i>ω</i> ⩽ 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today's computational methods. Phase diagram ; Component (thermodynamics) ; Computer science ; Phase (matter) ; Algorithm ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Monika Marosz | Bogdan Samojeden | Andrzej Kowalczyk | Małgorzata Rutkowska | Monika Motak | Urbano Díaz | A.E. Palomares | Lucjan Chmielarz
Source: Materials (article, 2020-05-22)
Field: Chemistry — Inorganic Chemistry

MCM-22, MCM-36, and ITQ-2 Zeolites with Different Si/Al Molar Ratios as Effective Catalysts of Methanol and Ethanol Dehydration MCM-22, MCM-36, and ITQ-2 zeolites with the intended Si/Al molar ratios of 15, 25, and 50 were synthetized and tested as catalysts for dehydration of methanol to dimethyl ether and dehydration of ethanol to diethyl ether and ethylene. The surface concentration of acid sites was regulated by the synthesis of zeolite precursors with different aluminum content in the zeolite framework, while the influence of porous structure on the overall efficiency of alcohol conversion was analyzed by application of zeolitic materials with different types of porosity-microporous MCM-22 as well as microporous-mesoporous MCM-36 and ITQ-2. The zeolitic samples were characterized with respect to their: chemical composition (ICP-OES), structure (XRD, FT-IR), texture (N<sub>2</sub> sorption), and surface acidity (NH<sub>3</sub>-TPD). Comparison of the catalytic activity of the studied zeolitic catalysts with other reported catalytic systems, including zeolites with the similar Si/Al ratio as well as γ-Al<sub>2</sub>O<sub>3</sub> (one of the commercial catalysts for methanol dehydration), shows a great potential of MCM-22, MCM-36, and ITQ-2 in the reactions of alcohols dehydration. Catalysis ; Zeolite ; Dehydration ; Methanol ; Microporous material ; Dimethyl ether ; Mesoporous material ; Chemistry ; Inorganic chemistry ; Diethyl ether ; Sorption ; Materials science ; Adsorption ; Organic chemistry Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science
Authors: Willian A. Paiva-Marques | Faustino Reyes Gómez | Osvaldo N. Oliveira | J. R. Mejía-Salazar
Source: Sensors (review, 2020-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral Plasmonics and Their Potential for Point-of-Care Biosensing Applications There has been growing interest in using strong field enhancement and light localization in plasmonic nanostructures to control the polarization properties of light. Various experimental techniques are now used to fabricate twisted metallic nanoparticles and metasurfaces, where strongly enhanced chiral near-fields are used to intensify circular dichroism (CD) signals. In this review, state-of-the-art strategies to develop such chiral plasmonic nanoparticles and metasurfaces are summarized, with emphasis on the most recent trends for the design and development of functionalizable surfaces. The major objective is to perform enantiomer selection which is relevant in pharmaceutical applications and for biosensing. Enhanced sensing capabilities are key for the design and manufacture of lab-on-a-chip devices, commonly named point-of-care biosensing devices, which are promising for next-generation healthcare systems. Biosensor ; Plasmon ; Nanotechnology ; Materials science ; Metamaterial ; Nanoparticle ; Computer science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics