jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joe Forth | Paul Y. Kim | Ganhua Xie | Xubo Liu | Brett A. Helms | Thomas P. Russell
Source: Advanced Materials (review, 2019-03-04)
Field: Materials Science — Materials Chemistry

Building Reconfigurable Devices Using Complex Liquid–Fluid Interfaces Liquid-fluid interfaces provide a platform both for structuring liquids into complex shapes and assembling dimensionally confined, functional nanomaterials. Historically, attention in this area has focused on simple emulsions and foams, in which surface-active materials such as surfactants or colloids stabilize structures against coalescence and alter the mechanical properties of the interface. In recent decades, however, a growing body of work has begun to demonstrate the full potential of the assembly of nanomaterials at liquid-fluid interfaces to generate functionally advanced, biomimetic systems. Here, a broad overview is given, from fundamentals to applications, of the use of liquid-fluid interfaces to generate complex, all-liquid devices with a myriad of potential applications. Materials science ; Nanotechnology ; Nanomaterials ; Coalescence (physics) ; Structuring ; Biomimetics ; Interface (matter) ; Wetting ; Composite material ; Physics Pickering emulsions and particle stabilization ; Micro and Nano Robotics ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Félix Musil | Michael J. Willatt | Mikhail Langovoy | Michele Ceriotti
Source: Journal of Chemical Theory and Computation (article, 2019-01-03)
Field: Materials Science — Materials Chemistry

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated based on subsampling of the same training data. The accuracy of the uncertainty prediction can be benchmarked by maximum likelihood estimation, which can also be used to correct for correlations between resampled models and to improve the performance of the uncertainty estimation by a cross-validation procedure. In the case of sparse Gaussian Process Regression models, this resampled estimator can be evaluated at negligible cost. We demonstrate the reliability of these estimates for the prediction of molecular and materials energetics and for the estimation of nuclear chemical shieldings in molecular crystals. Extension to estimate the uncertainty in energy differences, forces, or other correlated predictions is straightforward. This method can be easily applied to other machine-learning schemes and will be beneficial to make data-driven predictions more reliable and to facilitate training-set optimization and active-learning strategies. Computer science ; Estimator ; Machine learning ; Uncertainty quantification ; Resampling ; Gaussian process ; Kriging ; Process (computing) ; Reliability (semiconductor) ; Artificial intelligence ; Data mining ; Gaussian ; Statistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Graham Roberts | Simon Y. Haile | Rajat Sainju | Danny J. Edwards | Brian Hutchinson | Yuanyuan Zhu
Source: Scientific Reports (article, 2019-09-04)
Field: Materials Science — Materials Chemistry

Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well-defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanisms is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training on a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects. Artificial intelligence ; Convolutional neural network ; Segmentation ; Computer science ; Pattern recognition (psychology) ; Deep learning ; Workflow ; Feature (linguistics) ; Materials science Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jianjun Wang | Bo Wang | Long‐Qing Chen
Source: Annual Review of Materials Research (article, 2019-03-08)
Field: Materials Science — Materials Chemistry

Understanding, Predicting, and Designing Ferroelectric Domain Structures and Switching Guided by the Phase-Field Method Understanding mesoscale ferroelectric domain structures and their switching behavior under external fields is critical to applications of ferroelectrics. The phase-field method has been established as a powerful tool for probing, predicting, and designing the formation of domain structures under different electromechanical boundary conditions and their switching behavior under electric and/or mechanical stimuli. Here we review the basic framework of the phase-field model of ferroelectrics and its applications to simulating domain formation in bulk crystals, thin films, superlattices, and nanostructured ferroelectrics and to understanding macroscopic and local domain switching under electrical and/or mechanical fields. We discuss the possibility of utilizing the structure-property relationship learned from phase-field simulations to design high-performance relaxor piezoelectrics and electrically tunable thermal conductivity. The review ends with a summary of and an outlook on the potential new applications of the phase-field method of ferroelectrics. Ferroelectricity ; Materials science ; Field (mathematics) ; Phase boundary ; Phase (matter) ; Piezoelectricity ; Electric field ; Domain (mathematical analysis) ; Condensed matter physics ; Engineering physics ; Dielectric ; Optoelectronics ; Physics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Multiferroics and related materials
Authors: Min Jia | Zhuo Wang | Heting Li | Xinke Wang | Weijie Luo | Shulin Sun | Yan Zhang | Qiong He | Lei Zhou
Source: Light Science & Applications (article, 2019-01-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient manipulations of circularly polarized terahertz waves with transmissive metasurfaces The unrestricted control of circularly polarized (CP) terahertz (THz) waves is important in science and applications, but conventional THz devices suffer from issues of bulky size and low efficiency. Although Pancharatnam-Berry (PB) metasurfaces have shown strong capabilities to control CP waves, <i>transmission</i>-mode PB devices realized in the THz regime are less efficient, limiting their applications in practice. Here, based on Jones matrix analysis, we design a tri-layer structure (thickness of ~λ/5) and experimentally demonstrate that the structure can serve as a highly efficient <i>transmissive</i> meta-atom (relative efficiency of ~90%) to build PB metadevices for manipulating CP THz waves. Two ultrathin THz metadevices are fabricated and experimentally characterized with a <i>z</i>-scan THz imaging system. The first device can realize a photonic spin Hall effect with an experimentally demonstrated relative efficiency of ~90%, whereas the second device can generate a high-quality background-free CP Bessel beam with measured longitudinal and transverse field patterns that exhibit the nondiffracting characteristics of a Bessel beam. All the experimental results are in excellent agreement with full-wave simulations. Our results pave the way to freely manipulate CP THz beams, laying a solid basis for future applications such as biomolecular control and THz signal transportation. Terahertz radiation ; Bessel beam ; Optics ; Bessel function ; Photonics ; Optoelectronics ; Physics ; Metamaterial ; Beam steering ; Beam (structure) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Hassan Masood | Cui Ying Toe | Wey Yang Teoh | Vidhyasaharan Sethu | Rose Amal
Source: ACS Catalysis (article, 2019-11-07)
Field: Materials Science — Materials Chemistry

Machine Learning for Accelerated Discovery of Solar Photocatalysts Robust screening of materials on the basis of structure–property–activity relationships to discover active photocatalysts is a highly sought out aspect of photocatalysis research. Recent advancements in machine learning offer considerable opportunities to evolve photocatalysts discovery practices. Machine learning has largely facilitated various areas of science and engineering, including heterogeneous catalysis, but adaptation of it in photocatalysis research is still at an elementary stage. The scarcity of consistent training data is a major bottleneck, and we foresee the integration of photocatalysis domain knowledge in mainstream machine learning protocols as a viable solution. Here, we present a holistic framework incorporating machine learning and domain knowledge to set directions toward accelerated discovery of solar photocatalysts. This Perspective begins with a discussion on domain knowledge available in photocatalysis which could potentially be leveraged to liaise with machine learning methods. Subsequently, we present prevalent machine learning practices in heterogeneous catalysis tailored to assist discovery of photocatalysts in a purely data-driven fashion. Lastly, we conceptualize various strategies for complementing data-driven machine learning with photocatalysis domain knowledge. The strategies involve the following: (i) integration of theoretical and prior empirical knowledge during the training of machine learning models; (ii) embedding the knowledge in feature space; and (iii) utilizing existing material databases to constrain machine learning predictions. The aforementioned human-in-loop framework (leveraging both human and machine intelligence) could possibly mitigate the lack of interpretability and reliability associated with data-driven machine learning and reinforce complex model architectures irrespective of data scarcity. The concept could also offer substantial benefits to photocatalysis informatics by promoting a paradigm shift away from the Edisonian approach. Interpretability ; Machine learning ; Computer science ; Artificial intelligence ; Bottleneck ; Data science Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Data Quality and Management
Authors: Dema A. Almasri | Navid B. Saleh | Muataz Ali Atieh | Gordon McKay | S. Ahzi
Source: Scientific Reports (article, 2019-03-01)
Field: Materials Science — Biomaterials

Adsorption of phosphate on iron oxide doped halloysite nanotubes Excess phosphate in water is known to cause eutrophication, and its removal is imperative. Nanoclay minerals are widely used in environmental remediation due to their low-cost, adequate availability, environmental compatibility, and adsorption efficiency. However, the removal of anions with nanoclays is not very effective because of electrostatic repulsion from clay surfaces with a net negative charge. Among clay minerals, halloysite nanotubes (HNTs) possess a negatively charged exterior and a positively charged inner lumen. This provides an increased affinity for anion removal. In this study, HNTs are modified with nano-scale iron oxide (Fe<sub>2</sub>O<sub>3</sub>) to enhance the adsorption capacity of the nanosorbent. This modification allowed for effective distribution of these oxide surfaces, which are known to sorb phosphate via ligand exchange and by forming inner-sphere complexes. A detailed characterization of the raw and (Fe<sub>2</sub>O<sub>3</sub>) modified HNTs (Fe-HNT) is conducted. Influences of Fe<sub>2</sub>O<sub>3</sub> loading, adsorbent dosage, contact time, pH, initial phosphate concentration, and coexisting ions on the phosphate adsorption capacity are studied. Results demonstrate that adsorption on Fe-HNT is pH-dependent with fast initial adsorption kinetics. The underlying mechanism is identified as a combination of electrostatic attraction, ligand exchange, and Lewis acid-base interactions. The nanomaterial provides promising results for its application in water/wastewater treatment. Halloysite ; Adsorption ; Phosphate ; Iron oxide ; Chemical engineering ; Chemistry ; Environmental remediation ; Oxide ; Groundwater remediation ; Chitosan ; Ion exchange ; Inorganic chemistry ; Surface charge ; Nanomaterials ; Ion ; Organic chemistry ; Contamination Clay minerals and soil interactions ; Phosphorus and nutrient management ; Iron oxide chemistry and applications
Authors: Chandramouli Nyshadham | Matthias Rupp | Brayden Bekker | Alexander V. Shapeev | Tim Mueller | Conrad W. Rosenbrock | Gábor Cśanyi | David Wingate | Gus L. W. Hart
Source: npj Computational Materials (article, 2019-04-18)
Field: Materials Science — Materials Chemistry

Machine-learned multi-system surrogate models for materials prediction Abstract Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the computational cost. We demonstrate surrogate models that simultaneously interpolate energies of different materials on a dataset of 10 binary alloys (AgCu, AlFe, AlMg, AlNi, AlTi, CoNi, CuFe, CuNi, FeV, and NbNi) with 10 different species and all possible fcc, bcc, and hcp structures up to eight atoms in the unit cell, 15,950 structures in total. We find that the deviation of prediction errors when increasing the number of simultaneously modeled alloys is &lt;1 meV/atom. Several state-of-the-art materials representations and learning algorithms were found to qualitatively agree on the prediction errors of formation enthalpy with relative errors of &lt;2.5% for all systems. Binary number ; Atom (system on chip) ; Ab initio ; Enthalpy ; Materials science ; Fraction (chemistry) ; Standard enthalpy change of formation ; Computer science ; Thermodynamics ; Algorithm ; Mathematics ; Physics ; Chemistry ; Parallel computing Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Cezarina Cela Mardare | Achim Walter Hassel
Source: physica status solidi (a) (article, 2019-04-29)
Field: Materials Science — Polymers and Plastics

Review on the Versatility of Tungsten Oxide Coatings Tungsten oxide is a versatile material with many advantages such as large availability and low fabrications costs. Additionally, it is suitable to be produced in the form of thin films or coatings, which makes it very attractive for many applications. In this Feature Article, the material properties such as crystallography, chemical stability, and semiconducting properties are presented, followed by examples of coating technologies. Furthermore, the relation between its structure and some of the very important applications fields such as electro‐ and photochromism, as well as pH sensing are analyzed in detail. Its auspicious semiconducting properties make it attractive to be used as photo(electro)catalyst, therefore applications of thin films based on WO 3 are emphasized such as photoanodes for water splitting, in healthcare as antimicrobial material and for degradation of pollutants or for CO 2 reduction. The article is concluded by a short overview of the current status of research employing tungsten oxide. Materials science ; Nanotechnology ; Tungsten oxide ; Oxide ; Coating ; Tungsten ; Thin film ; Degradation (telecommunications) ; Catalysis ; Photochromism ; Metallurgy ; Computer science ; Chemistry Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Analytical Chemistry and Sensors
Authors: Rebecca L. Siegelman | Phillip J. Milner | Alexander C. Forse | Jung‐Hoon Lee | Kristen A. Colwell | Jeffrey B. Neaton | Jeffrey A. Reimer | Simon C. Weston | Jeffrey R. Long
Source: Journal of the American Chemical Society (article, 2019-07-26)
Field: Chemistry — Inorganic Chemistry

Water Enables Efficient CO<sub>2</sub> Capture from Natural Gas Flue Emissions in an Oxidation-Resistant Diamine-Appended Metal–Organic Framework Supported by increasingly available reserves, natural gas is achieving greater adoption as a cleaner-burning alternative to coal in the power sector. As a result, carbon capture and sequestration from natural gas-fired power plants is an attractive strategy to mitigate global anthropogenic CO<sub>2</sub> emissions. However, the separation of CO<sub>2</sub> from other components in the flue streams of gas-fired power plants is particularly challenging due to the low CO<sub>2</sub> partial pressure (∼40 mbar), which necessitates that candidate separation materials bind CO<sub>2</sub> strongly at low partial pressures (≤4 mbar) to capture ≥90% of the emitted CO<sub>2</sub>. High partial pressures of O<sub>2</sub> (120 mbar) and water (80 mbar) in these flue streams have also presented significant barriers to the deployment of new technologies for CO<sub>2</sub> capture from gas-fired power plants. Here, we demonstrate that functionalization of the metal-organic framework Mg<sub>2</sub>(dobpdc) (dobpdc<sup>4-</sup> = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) with the cyclic diamine 2-(aminomethyl)piperidine (2-ampd) produces an adsorbent that is capable of ≥90% CO<sub>2</sub> capture from a humid natural gas flue emission stream, as confirmed by breakthrough measurements. This material captures CO<sub>2</sub> by a cooperative mechanism that enables access to a large CO<sub>2</sub> cycling capacity with a small temperature swing (2.4 mmol CO<sub>2</sub>/g with Δ<i>T</i> = 100 °C). Significantly, multicomponent adsorption experiments, infrared spectroscopy, magic angle spinning solid-state NMR spectroscopy, and van der Waals-corrected density functional theory studies suggest that water enhances CO<sub>2</sub> capture in 2-ampd-Mg<sub>2</sub>(dobpdc) through hydrogen-bonding interactions with the carbamate groups of the ammonium carbamate chains formed upon CO<sub>2</sub> adsorption, thereby increasing the thermodynamic driving force for CO<sub>2</sub> binding. In light of the exceptional thermal and oxidative stability of 2-ampd-Mg<sub>2</sub>(dobpdc), its high CO<sub>2</sub> adsorption capacity, and its high CO<sub>2</sub> capture rate from a simulated natural gas flue emission stream, this material is one of the most promising adsorbents to date for this important separation. Chemistry ; Flue gas ; van der Waals force ; Natural gas ; Adsorption ; Metal-organic framework ; Chemical engineering ; Inorganic chemistry ; Environmental chemistry ; Organic chemistry ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Wujie Wang | Rafael Gómez-Bombarelli
Source: npj Computational Materials (article, 2019-12-18)
Field: Materials Science — Materials Chemistry

Coarse-graining auto-encoders for molecular dynamics Abstract Molecular dynamics simulations provide theoretical insight into the microscopic behavior of condensed-phase materials and, as a predictive tool, enable computational design of new compounds. However, because of the large spatial and temporal scales of thermodynamic and kinetic phenomena in materials, atomistic simulations are often computationally infeasible. Coarse-graining methods allow larger systems to be simulated by reducing their dimensionality, propagating longer timesteps, and averaging out fast motions. Coarse-graining involves two coupled learning problems: defining the mapping from an all-atom representation to a reduced representation, and parameterizing a Hamiltonian over coarse-grained coordinates. We propose a generative modeling framework based on variational auto-encoders to unify the tasks of learning discrete coarse-grained variables, decoding back to atomistic detail, and parameterizing coarse-grained force fields. The framework is tested on a number of model systems including single molecules and bulk-phase periodic simulations. Molecular dynamics ; Representation (politics) ; Hamiltonian (control theory) ; Generative grammar ; Dynamics (music) ; Computation ; Kinetic energy ; Generative model Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Electron Microscopy Techniques and Applications
Authors: Hui Cai | Yiyi Gu | Yu‐Chuan Lin | Yiling Yu | David B. Geohegan | Kai Xiao
Source: Applied Physics Reviews (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Synthesis and emerging properties of 2D layered III–VI metal chalcogenides Atomically thin layered III–VI metal chalcogenides are an emerging class of 2D materials that have attracted increasing attention in recent years due to their remarkable physical properties and technological applications. Thanks to the recently developed theoretical and experimental methods, a number of exciting discoveries for these materials have revealed their new phases, a unique “Mexican hat”-shaped electronic band structure, and superior optical and electronic properties that distinguish them from other 2D materials such as transition metal dichalcogenides. This review summarizes the novel properties, structures, and synthesis strategies for these materials and emphasizes the most cutting-edge and seminal achievements in this rapidly growing field in order to provide input for future research works. We first present the rich crystal structure and phases that have been found in these materials, with an emphasis on the possibility of phase engineering. Then, we discuss the synthesis strategies for 2D layered III–VI metal chalcogenides from the top-down, bottom-up, and template-based chemical conversion approaches. We focus on the highly controlled synthesis methods that provide fine-tuning of the thickness, phase, edge structure, and other morphological characteristics. Third, we discuss the properties and applications of these materials, focusing on their unique electronic structure including the Mexican hat-shaped valence band, their superior nonlinear optical properties, high-performance electronic devices, promising photoelectrochemical properties, and emerging quantum properties such as quantum emission, exciton condensation, ferromagnetism, and topological quantum phase transition. Finally, we provide our perspective on the current challenges and future directions in this field. Materials science ; Metal ; Nanotechnology ; Metallurgy 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties