jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dennis M. Dimiduk | Elizabeth A. Holm | Stephen R. Niezgoda
Source: Integrating materials and manufacturing innovation (article, 2018-08-27)
Field: Materials Science — Materials Chemistry

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering The fields of machining learning and artificial intelligence are rapidly expanding, impacting nearly every technological aspect of society. Many thousands of published manuscripts report advances over the last 5 years or less. Yet materials and structures engineering practitioners are slow to engage with these advancements. Perhaps the recent advances that are driving other technical fields are not sufficiently distinguished from long-known informatics methods for materials, thereby masking their likely impact to the materials, processes, and structures engineering (MPSE). Alternatively, the diverse nature and limited availability of relevant materials data pose obstacles to machine-learning implementation. The glimpse captured in this overview is intended to draw focus to selected distinguishing advances, and to show that there are opportunities for these new technologies to have transformational impacts on MPSE. Further, there are opportunities for the MPSE fields to contribute understanding to the emerging machine-learning tools from a physics basis. We suggest that there is an immediate need to expand the use of these new tools throughout MPSE, and to begin the transformation of engineering education that is necessary for ongoing adoption of the methods. Artificial intelligence ; Emerging technologies ; Transformational leadership ; Computer science ; Deep learning ; Field (mathematics) ; Engineering ethics ; Engineering ; Data science ; Political science ; Mathematics Machine Learning in Materials Science ; Mineral Processing and Grinding ; Electron and X-Ray Spectroscopy Techniques
Authors: Hao Wang | Xinglong Dong | Valentina Colombo | Qining Wang | Yanyao Liu | Wei Liu | Xinlong Wang | Xiao‐Ying Huang | Davide Μ. Proserpio | Angelo Sironi | Yu Han | Jing Li
Source: Advanced Materials (article, 2018-10-15)
Field: Chemistry — Inorganic Chemistry

Tailor‐Made Microporous Metal–Organic Frameworks for the Full Separation of Propane from Propylene Through Selective Size Exclusion Adsorptive separation of olefin/paraffin mixtures by porous solids can greatly reduce the energy consumption associated with the currently employed cryogenic distillation technique. Here, the complete separation of propane and propylene by a designer microporous metal-organic framework material is reported. The compound, Y<sub>6</sub> (OH)<sub>8</sub> (abtc)<sub>3</sub> (H<sub>2</sub> O)<sub>6</sub> (DMA)<sub>2</sub> (Y-abtc, abtc = 3,3',5,5'-azobenzene-tetracarboxylates; DMA = dimethylammonium), is rationally designed through topology-guided replacement of inorganic building units. Y-abtc is both thermally and hydrothermally robust, and possesses optimal pore window size for propane/propylene separation. It adsorbs propylene with fast kinetics under ambient temperature and pressure, but fully excludes propane, as a result of selective size exclusion. Multicomponent column breakthrough experiments confirm that polymer-grade propylene (99.5%) can be obtained by this process, demonstrating its true potential as an alternative sorbent for efficient separation of propane/propylene mixtures. Propane ; Microporous material ; Materials science ; Metal-organic framework ; Chemical engineering ; Separation (statistics) ; Polymer science ; Nanotechnology ; Organic chemistry ; Composite material ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal
Authors: Liujun Zou | Hoi Chun Po | Ashvin Vishwanath | T. Senthil
Source: Physical review. B./Physical review. B (article, 2018-08-28)
Field: Materials Science — Materials Chemistry

Band structure of twisted bilayer graphene: Emergent symmetries, commensurate approximants, and Wannier obstructions Motivated by the exciting discovery of correlated insulating behavior and superconductivity in magic-angle bilayer graphene, the authors discuss here the salient features of the band structure of this system. A remarkable feature is the appearance of isolated, nearly flat bands around the magic angles. The authors discuss the importance of emergent symmetries of this system and clarify some potential confusion regarding commensurate structures of twisted bilayer graphene. Furthermore, they discuss the band topology, which leads to obstructions to constructing well-localized Wannier functions of the nearly flat bands, and give directions towards overcoming the difficulties. Bilayer graphene ; Wannier function ; Homogeneous space ; Electronic band structure ; Confusion ; Graphene ; Condensed matter physics ; Physics ; Magic angle ; Salient ; Bilayer ; Theoretical physics ; Topology (electrical circuits) ; Quantum mechanics ; Geometry ; Mathematics ; Computer science ; Chemistry ; Spectral line ; Combinatorics Graphene research and applications ; Topological Materials and Phenomena ; 2D Materials and Applications
Authors: J.P. Correa-Baena | Kedar Hippalgaonkar | Jeroen van Duren | Shaffiq A. Jaffer | Vijay Chandrasekhar | Vladan Stevanović | Cyrus Wadia | Supratik Guha | Tonio Buonassisi
Source: Joule (article, 2018-06-12)
Field: Materials Science — Materials Chemistry

Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing Automation ; Supercomputer ; Engineering ; Computer science ; Manufacturing engineering ; Systems engineering ; Mechanical engineering ; Operating system Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Angelo Ziletti | Devinder Kumar | Matthias Scheffler | Luca M. Ghiringhelli
Source: Nature Communications (article, 2018-07-11)
Field: Materials Science — Materials Chemistry

Insightful classification of crystal structures using deep learning Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep learning neural network model for classification. Our approach is able to correctly classify a dataset comprising more than 100,000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal structure recognition of-possibly noisy and incomplete-three-dimensional structural data in big-data materials science. Deep learning ; Artificial neural network ; Construct (python library) ; Pattern recognition (psychology) ; Crystal structure ; Identification (biology) ; Deep neural networks ; Lattice (music) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Ruizhi Zhang | Francesco Gucci | Hongyu Zhu | Kan Chen | Michael J. Reece
Source: Inorganic Chemistry (article, 2018-09-26)
Field: Materials Science — Materials Chemistry

Data-Driven Design of Ecofriendly Thermoelectric High-Entropy Sulfides High-entropy compounds with compositional complexity can be designed as new thermoelectric materials. Here a data-driven model was developed, which chose suitable elements to reduce the enthalpy of formation and hence to increase the chance of single phase formation. Using this model, two high-entropy sulfides were designed, metallic Cu<sub>5</sub>SnMgGeZnS<sub>9</sub> and semiconducting Cu<sub>3</sub>SnMgInZnS<sub>7</sub>. They were then successfully fabricated as single-phase dense ceramics with homogeneously distributed cations, and their phase stability and atomic local structures were investigated using density functional theory calculations. Finally, a zT value of 0.58 at 773 K was obtained for Cu<sub>5</sub>Sn<sub>1.2</sub>MgGeZnS<sub>9</sub>, where additional Sn was used to tune the carrier concentration. This work provides a simple approach to find new high-entropy functional materials in the largely unexplored multielement chemical space. Chemistry ; Enthalpy ; Density functional theory ; Entropy (arrow of time) ; Thermodynamics ; Thermoelectric effect ; Ceramic ; Standard enthalpy of formation ; Nanotechnology ; Computational chemistry ; Physical chemistry ; Organic chemistry Advanced Thermoelectric Materials and Devices ; High Entropy Alloys Studies ; Chalcogenide Semiconductor Thin Films
Authors: Julien Loste | José‐Marie Lopez‐Cuesta | Laurent Billon | Hélène Garay | Maud Save
Source: Progress in Polymer Science (article, 2018-11-01)
Field: Chemistry — Organic Chemistry

Transparent polymer nanocomposites: An overview on their synthesis and advanced properties Nanocomposite ; Materials science ; Polymer ; Nanoparticle ; Polymer nanocomposite ; Polymerization ; Nanotechnology ; Casting ; In situ polymerization ; Composite material Advanced Polymer Synthesis and Characterization ; TiO2 Photocatalysis and Solar Cells ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Felix A. Faber | Anders S. Christensen | Bing Huang | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2018-03-20)
Field: Materials Science — Materials Chemistry

Alchemical and structural distribution based representation for universal quantum machine learning We introduce a representation of any atom in any chemical environment for the automatized generation of universal kernel ridge regression-based quantum machine learning (QML) models of electronic properties, trained throughout chemical compound space. The representation is based on Gaussian distribution functions, scaled by power laws and explicitly accounting for structural as well as elemental degrees of freedom. The elemental components help us to lower the QML model's learning curve, and, through interpolation across the periodic table, even enable "alchemical extrapolation" to covalent bonding between elements not part of training. This point is demonstrated for the prediction of covalent binding in single, double, and triple bonds among main-group elements as well as for atomization energies in organic molecules. We present numerical evidence that resulting QML energy models, after training on a few thousand random training instances, reach chemical accuracy for out-of-sample compounds. Compound datasets studied include thousands of structurally and compositionally diverse organic molecules, non-covalently bonded protein side-chains, (H<sub>2</sub>O)<sub>40</sub>-clusters, and crystalline solids. Learning curves for QML models also indicate competitive predictive power for various other electronic ground state properties of organic molecules, calculated with hybrid density functional theory, including polarizability, heat-capacity, HOMO-LUMO eigenvalues and gap, zero point vibrational energy, dipole moment, and highest vibrational fundamental frequency. Kernel (algebra) ; Gaussian ; Representation (politics) ; Dipole ; Atom (system on chip) ; Eigenvalues and eigenvectors ; Interpolation (computer graphics) ; Gaussian function ; Covalent bond ; Point (geometry) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Edyta Swider | Olga Koshkina | Jurjen Tel | Luis J. Cruz | I. Jolanda M. de Vries | Mangala Srinivas
Source: Acta Biomaterialia (review, 2018-04-10)
Field: Materials Science — Biomaterials

Customizing poly(lactic-co-glycolic acid) particles for biomedical applications Nanoparticles are increasingly important in the field of biomedicine. Particles made of polymers are in the spotlight, due to their biodegradability, biocompatibility, versatility. In this review, we aim to discuss the range of formulation techniques, manipulations, and applications of poly(lactic-co-glycolic acid) (PLGA) particles, to enable a researcher to effectively select or design the optimal particles for their application. We describe the various techniques of PLGA particle synthesis and their impact on possible applications. We focus on recent developments in the field of PLGA particles, and new synthesis techniques that have emerged over the past years. Overall, we show how the chemistry of PLGA particles can be adapted to solve pressing biological needs. PLGA ; Materials science ; Glycolic acid ; Nanotechnology ; Biocompatibility ; Nanomedicine ; Drug delivery ; Nanoparticle ; Janus particles ; Lactic acid ; Janus Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Polymer Surface Interaction Studies
Authors: Austin D. Sendek | Ekin D. Cubuk | Evan R. Antoniuk | Gowoon Cheon | Yi Cui | Evan J. Reed
Source: Chemistry of Materials (article, 2018-11-21)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid Li superionic conductors is of critical importance to the development of safe all-solid-state Li-ion batteries. With a predictive universal structure–property relationship for fast ion conduction not well understood, the search for new solid Li ion conductors has relied largely on trial-and-error computational and experimental searches over the last several decades. In this work, we perform a guided search of materials space with a machine learning (ML)-based prediction model for material selection and density functional theory molecular dynamics (DFT-MD) simulations for calculating ionic conductivity. These materials are screened from over 12 000 experimentally synthesized and characterized candidates with very diverse structures and compositions. When compared to a random search of materials space, we find that the ML-guided search is 2.7 times more likely to identify fast Li ion conductors, with at least a 44 times improvement in the log-average of room temperature Li ion conductivity. The F1 score of the ML-based model is 0.50, 3.5 times better than the F1 score expected from completely random guesswork. In a head-to-head competition against six Ph.D. students working in the field, we find that the ML-based model doubles the F1 score of human experts in its ability to identify fast Li-ion conductors from atomistic structure with a 1000-fold increase in speed, clearly demonstrating the utility of this model for the research community. In addition to having high predicted Li-ion conductivity, all materials reported here lack transition metals to enhance stability against reduction by the Li metal anode and are predicted to exhibit low electronic conduction, high stability against oxidation, and high thermodynamic stability, making them promising candidates for solid-state electrolyte applications on these several essential metrics. Ion ; Fast ion conductor ; Conductivity ; Electrical conductor ; Materials science ; Ionic conductivity ; Density functional theory ; Ionic bonding ; Chemical physics ; Machine learning ; Nanotechnology ; Computer science ; Chemistry ; Computational chemistry ; Physical chemistry ; Electrolyte Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Wendong Wang | Jaakko V. I. Timonen | Andreas Carlson | Dirk‐Michael Drotlef | Cathy Zhang | Stefan Kolle | Alison Grinthal | Tak‐Sing Wong | Benjamin D. Hatton | Sung Hoon Kang | Stephen Kennedy | Joshua Chi | Robert Thomas Blough | Metin Sitti | Lakshmi Shree Kulumani Mahadevan | Joanna Aizenberg
Source: Nature (article, 2018-06-22)
Field: Materials Science — Materials Chemistry

Multifunctional ferrofluid-infused surfaces with reconfigurable multiscale topography Ferrofluid ; Microscale chemistry ; Materials science ; Magnetic field ; Nanotechnology ; Capillary action ; Anisotropy ; Magnetic nanoparticles ; Chemical physics ; Nanoparticle ; Mechanics ; Optics ; Physics ; Composite material Pickering emulsions and particle stabilization ; Micro and Nano Robotics ; Surface Modification and Superhydrophobicity
Authors: Andrea Grisafi | David M. Wilkins | Gábor Cśanyi | Michele Ceriotti
Source: Physical Review Letters (article, 2018-01-19)
Field: Materials Science — Materials Chemistry

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase. Homogeneous space ; Computer science ; Scalar (mathematics) ; Rotational invariance ; Generality ; Statistical physics ; Symmetry (geometry) ; Theoretical physics ; Artificial intelligence ; Physics ; Algorithm ; Theoretical computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Bing Cao | Lawrence A. Adutwum | Anton O. Oliynyk | Erik J. Luber | Brian C. Olsen | Arthur Mar | Jillian M. Buriak
Source: ACS Nano (article, 2018-07-20)
Field: Materials Science — Materials Chemistry

How To Optimize Materials and Devices <i>via</i> Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics Most discoveries in materials science have been made empirically, typically through one-variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based systems are, however, neither simple nor uncorrelated. In a device such as an organic photovoltaic, for example, the level of complexity is high due to the sheer number of components and processing conditions, and thus, changing one variable can have multiple unforeseen effects due to their interconnectivity. Design of Experiments (DoE) is ideally suited for such multivariable analyses: by planning one's experiments as per the principles of DoE, one can test and optimize several variables simultaneously, thus accelerating the process of discovery and optimization while saving time and precious laboratory resources. When combined with machine learning, the consideration of one's data in this manner provides a different perspective for optimization and discovery, akin to climbing out of a narrow valley of serial (one-variable-at-a-time) experimentation, to a mountain ridge with a 360° view in all directions. Interconnectivity ; Variable (mathematics) ; Computer science ; Design of experiments ; Multivariable calculus ; Process (computing) ; Photovoltaics ; Photovoltaic system ; Artificial intelligence ; Machine learning ; Engineering ; Control engineering ; Mathematics ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Nanowire Synthesis and Applications
Authors: Han Wei | Shuaishuai Zhao | Qingyuan Rong | Hua Bao
Source: International Journal of Heat and Mass Transfer (article, 2018-08-25)
Field: Materials Science — Materials Chemistry

Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods Thermal conductivity ; Composite number ; Materials science ; Support vector machine ; Computer science ; Porous medium ; Heat equation ; Work (physics) ; Machine learning ; Artificial neural network ; Heat transfer ; Artificial intelligence ; Thermodynamics ; Porosity ; Composite material ; Mathematics ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Hydraulic Fracturing and Reservoir Analysis
Authors: Nikolay Kosinov | Chong Liu | Emiel J. M. Hensen | Evgeny A. Pidko
Source: Chemistry of Materials (review, 2018-05-07)
Field: Materials Science — Materials Chemistry

Engineering of Transition Metal Catalysts Confined in Zeolites Transition metal-zeolite composites are versatile catalytic materials for a wide range of industrial and lab-scale processes. Significant advances in fabrication and characterization of well-defined metal centers confined in zeolite matrixes have greatly expanded the library of available materials and, accordingly, their catalytic utility. In this review, we summarize recent developments in the field from the perspective of materials chemistry, focusing on synthesis, postsynthesis modification, (operando) spectroscopy characterization, and computational modeling of transition metal-zeolite catalysts. Zeolite ; Characterization (materials science) ; Catalysis ; Transition metal ; Fabrication ; Materials science ; Nanotechnology ; Metal ; Chemical engineering ; Chemistry ; Organic chemistry ; Metallurgy ; Engineering Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies