jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mads Mansø | Anne Ugleholdt Petersen | Zhihang Wang | Paul Erhart | Mogens Brøndsted Nielsen | Kasper Moth‐Poulsen
Source: Nature Communications (article, 2018-05-10)
Field: Materials Science — Materials Chemistry

Molecular solar thermal energy storage in photoswitch oligomers increases energy densities and storage times Molecular photoswitches can be used for solar thermal energy storage by photoisomerization into high-energy, meta-stable isomers; we present a molecular design strategy leading to photoswitches with high energy densities and long storage times. High measured energy densities of up to 559 kJ kg<sup>-1</sup> (155 Wh kg<sup>-1</sup>), long storage lifetimes up to 48.5 days, and high quantum yields of conversion of up to 94% per subunit are demonstrated in norbornadiene/quadricyclane (NBD/QC) photo-/thermoswitch couples incorporated into dimeric and trimeric structures. By changing the linker unit between the NBD units, we can at the same time fine-tune light-harvesting and energy densities of the dimers and trimers so that they exceed those of their monomeric analogs. These new oligomers thereby meet several of the criteria to be met for an optimum molecule to ultimately enter actual devices being able to undergo closed cycles of solar light-harvesting, energy storage, and heat release. Photoswitch ; Energy storage ; Thermal energy storage ; Materials science ; Thermal ; Chemistry ; Physics ; Photochemistry ; Thermodynamics Photochromic and Fluorescence Chemistry ; Organic Electronics and Photovoltaics ; Photoreceptor and optogenetics research
Authors: Wengong Jin | Regina Barzilay | Tommi Jaakkola
Source: DSpace@MIT (Massachusetts Institute of Technology) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Junction Tree Variational Autoencoder for Molecular Graph Generation © 2018 by authors.All right reserved. We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin. Autoencoder ; Molecular graph ; Computer science ; Embedding ; Margin (machine learning) ; Tree (set theory) ; Theoretical computer science ; Realization (probability) ; Graph ; Algorithm ; Artificial intelligence ; Machine learning ; Deep learning ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: William Yi Wang | Jinshan Li | Weimin Liu | Zi‐Kui Liu
Source: Computational Materials Science (review, 2018-11-13)
Field: Materials Science — Materials Chemistry

Integrated computational materials engineering for advanced materials: A brief review Computer science ; CALPHAD ; Scale (ratio) ; Systems engineering ; Nanotechnology ; Mechanical engineering ; Computational science ; Materials science ; Phase (matter) ; Engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Aluminum Alloys Composites Properties ; MXene and MAX Phase Materials
Authors: Kamal Choudhary | Brian DeCost | Francesca Tavazza
Source: Physical Review Materials (article, 2018-08-03)
Field: Materials Science — Materials Chemistry

Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems. These new descriptors allow differentiating between structural prototypes, which is not possible using the commonly used chemical-only descriptors. Specifically, we demonstrate that the combination of pairwise radial, nearest neighbor, bond-angle, dihedral-angle and core-charge distributions plays an important role in predicting formation energies, bandgaps, static refractive indices, magnetic properties, and modulus of elasticity for three-dimensional (3D) materials as well as exfoliation energies of two-dimensional (2D) layered materials. The training data consists of 24549 bulk and 616 monolayer materials taken from JARVIS-DFT database. We obtained very accurate ML models using gradient boosting algorithm. Then we use the trained models to discover exfoliable 2D-layered materials satisfying specific property requirements. Additionally, we integrate our formation energy ML model with a genetic algorithm for structure search to verify if the ML model reproduces the DFT convex hull. This verification establishes a more stringent evaluation metric for the ML model than what commonly used in data sciences. Our learnt model is publicly available on the JARVIS-ML website (https://www.ctcms.nist.gov/jarvisml) property predictions of generalized materials. Materials science ; Field (mathematics) ; Energy landscape ; Energy (signal processing) ; Force field (fiction) ; Nanotechnology ; Artificial intelligence ; Engineering physics ; Machine learning ; Computer science ; Engineering ; Physics ; Thermodynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Semih Sevim | Alessandro Sorrenti | Carlos Franco | Shuhei Furukawa | Salvador Pané | Andrew J. deMello | Josep Puigmartí‐Luis
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Materials Science — Biomaterials

Self-assembled materials and supramolecular chemistry within microfluidic environments: from common thermodynamic states to non-equilibrium structures Self-assembly is a crucial component in the bottom-up fabrication of hierarchical supramolecular structures and advanced functional materials. Control has traditionally relied on the use of encoded building blocks bearing suitable moieties for recognition and interaction, with targeting of the thermodynamic equilibrium state. On the other hand, nature leverages the control of reaction-diffusion processes to create hierarchically organized materials with surprisingly complex biological functions. Indeed, under non-equilibrium conditions (kinetic control), the spatio-temporal command of chemical gradients and reactant mixing during self-assembly (the creation of non-uniform chemical environments for example) can strongly affect the outcome of the self-assembly process. This directly enables a precise control over material properties and functions. In this tutorial review, we show how the unique physical conditions offered by microfluidic technologies can be advantageously used to control the self-assembly of materials and of supramolecular aggregates in solution, making possible the isolation of intermediate states and unprecedented non-equilibrium structures, as well as the emergence of novel functions. Selected examples from the literature will be used to confirm that microfluidic devices are an invaluable toolbox technology for unveiling, understanding and steering self-assembly pathways to desired structures, properties and functions, as well as advanced processing tools for device fabrication and integration. Supramolecular chemistry ; Microfluidics ; Nanotechnology ; Kinetic control ; Fabrication ; Toolbox ; Thermodynamic equilibrium ; Supramolecular assembly ; Computer science ; Chemistry ; Materials science ; Molecule ; Physics Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Polymer Surface Interaction Studies
Authors: Abdollah Motmaen Dadgar | Declan Scullion | Kyungnam Kang | Daniel V. Esposito | Eui‐Hyeok Yang | Irving P. Herman | M. A. Pimenta | Elton J. G. Santos | Abhay N. Pasupathy
Source: Chemistry of Materials (article, 2018-07-25)
Field: Materials Science — Materials Chemistry

Strain Engineering and Raman Spectroscopy of Monolayer Transition Metal Dichalcogenides We describe a facile technique based on polymer encapsulation to apply several percent controllable strains to monolayer and few-layer Transition Metal Dichalcogenides (TMDs). We use this technique to study the lattice response to strain via polarized Raman spectroscopy in monolayer WSe2 and WS2. The application of strain causes mode-dependent redshifts, with larger shift rates observed for in-plane modes. We observe a splitting of the degeneracy of the in-plane E' modes in both materials and measure the Gruneisen parameters. At large strain, we observe that the reduction of crystal symmetry can lead to a change in the polarization response of the A' mode in WS2. While both WSe2 and WS2 exhibit similar qualitative changes in the phonon structure with strain, we observe much larger changes in mode positions and intensities with strain in WS2. These differences can be explained simply by the degree of iconicity of the metal-chalcogen bond. Monolayer ; Raman spectroscopy ; Transition metal ; Materials science ; Strain (injury) ; Strain engineering ; Spectroscopy ; Nanotechnology ; Chemical physics ; Chemistry ; Metallurgy ; Optics ; Physics ; Organic chemistry ; Catalysis 2D Materials and Applications ; MXene and MAX Phase Materials ; Chalcogenide Semiconductor Thin Films
Authors: Tristan Bereau | Robert A. DiStasio | Alexandre Tkatchenko | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning Classical intermolecular potentials typically require an extensive parametrization procedure for any new compound considered. To do away with prior parametrization, we propose a combination of physics-based potentials with machine learning (ML), coined IPML, which is transferable across small neutral organic and biologically relevant molecules. ML models provide on-the-fly predictions for environment-dependent local atomic properties: electrostatic multipole coefficients (significant error reduction compared to previously reported), the population and decay rate of valence atomic densities, and polarizabilities across conformations and chemical compositions of H, C, N, and O atoms. These parameters enable accurate calculations of intermolecular contributions-electrostatics, charge penetration, repulsion, induction/polarization, and many-body dispersion. Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters-optimized once and for all across compounds. We validate IPML on various gas-phase dimers at and away from equilibrium separation, where we obtain mean absolute errors between 0.4 and 0.7 kcal/mol for several chemically and conformationally diverse datasets representative of non-covalent interactions in biologically relevant molecules. We further focus on hydrogen-bonded complexes-essential but challenging due to their directional nature-where datasets of DNA base pairs and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and as a first look, we consider IPML for denser systems: water clusters, supramolecular host-guest complexes, and the benzene crystal. Intermolecular force ; Population ; Molecule ; Multipole expansion ; Yield (engineering) ; Valence (chemistry) ; Charge (physics) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Alexander C. Forse | Phillip J. Milner | Jung‐Hoon Lee | Halle N. Redfearn | Julia Oktawiec | Rebecca L. Siegelman | Jeffrey D. Martell | Bhavish Dinakar | Leo B. Zasada | Miguel I. Gonzalez | Jeffrey B. Neaton | Jeffrey R. Long | Jeffrey A. Reimer
Source: Journal of the American Chemical Society (article, 2018-12-02)
Field: Chemistry — Inorganic Chemistry

Elucidating CO <sub>2</sub> Chemisorption in Diamine-Appended Metal–Organic Frameworks The widespread deployment of carbon capture and sequestration as a climate change mitigation strategy could be facilitated by the development of more energy-efficient adsorbents. Diamine-appended metal-organic frameworks of the type diamine-M<sub>2</sub>(dobpdc) (M = Mg, Mn, Fe, Co, Ni, Zn; dobpdc<sup>4-</sup> = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) have shown promise for carbon-capture applications, although questions remain regarding the molecular mechanisms of CO<sub>2</sub> uptake in these materials. Here we leverage the crystallinity and tunability of this class of frameworks to perform a comprehensive study of CO<sub>2</sub> chemisorption. Using multinuclear nuclear magnetic resonance (NMR) spectroscopy experiments and van-der-Waals-corrected density functional theory (DFT) calculations for 13 diamine-M<sub>2</sub>(dobpdc) variants, we demonstrate that the canonical CO<sub>2</sub> chemisorption products, ammonium carbamate chains and carbamic acid pairs, can be readily distinguished and that ammonium carbamate chain formation dominates for diamine-Mg<sub>2</sub>(dobpdc) materials. In addition, we elucidate a new chemisorption mechanism in the material dmpn-Mg<sub>2</sub>(dobpdc) (dmpn = 2,2-dimethyl-1,3-diaminopropane), which involves the formation of a 1:1 mixture of ammonium carbamate and carbamic acid and accounts for the unusual adsorption properties of this material. Finally, we show that the presence of water plays an important role in directing the mechanisms for CO<sub>2</sub> uptake in diamine-M<sub>2</sub>(dobpdc) materials. Overall, our combined NMR and DFT approach enables a thorough depiction and understanding of CO<sub>2</sub> adsorption within diamine-M<sub>2</sub>(dobpdc) compounds, which may aid similar studies in other amine-functionalized adsorbents in the future. Chemistry ; Carbamic acid ; Diamine ; Chemisorption ; Physisorption ; Adsorption ; Ethylenediamine ; Metal-organic framework ; Crystallinity ; van der Waals force ; Inorganic chemistry ; Organic chemistry ; Molecule ; Polymer chemistry ; Crystallography Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Kang Xia | Hao Gao | Cong Liu | Jianan Yuan | Jian Sun | Hui-Tian Wang | Dingyu Xing
Source: Science Bulletin (article, 2018-05-29)
Field: Materials Science — Materials Chemistry

A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search Tungsten ; Nitride ; Crystal structure ; Thermal stability ; Transition metal ; Thermal ; Band gap ; Crystal (programming language) Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; Machine Learning in Materials Science
Authors: Tomoki Yamashita | Nobuya Sato | Hiori Kino | Takashi Miyake | Koji Tsuda | Tamio Oguchi
Source: Physical Review Materials (article, 2018-01-09)
Field: Materials Science — Materials Chemistry

Crystal structure prediction accelerated by Bayesian optimization We propose a crystal structure prediction method based on Bayesian optimization. Our method is classified as a selection-type algorithm which is different from evolution-type algorithms such as an evolutionary algorithm and particle swarm optimization. Crystal structure prediction with Bayesian optimization can efficiently select the most stable structure from a large number of candidate structures with a lower number of searching trials using a machine learning technique. Crystal structure prediction using Bayesian optimization combined with random search is applied to known systems such as NaCl and ${\mathrm{Y}}_{2}{\mathrm{Co}}_{17}$ to discuss the efficiency of Bayesian optimization. These results demonstrate that Bayesian optimization can significantly reduce the number of searching trials required to find the global minimum structure by 30--40% in comparison with pure random search, which leads to much less computational cost. Bayesian optimization ; Crystal structure prediction ; Bayesian probability ; Particle swarm optimization ; Random search ; Algorithm ; Computer science ; Artificial intelligence ; Evolutionary algorithm ; Machine learning ; Mathematical optimization ; Materials science ; Crystal structure ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Mathieu Massicotte | Fabien Vialla | Peter Schmidt | Mark B. Lundeberg | Simone Latini | Sten Haastrup | Mark Danovich | Diana Davydovskaya | Kenji Watanabe | Takashi Taniguchi | Vladimir I. Fal’ko | Kristian S. Thygesen | Thomas G. Pedersen | Frank H. L. Koppens
Source: Nature Communications (article, 2018-04-18)
Field: Materials Science — Materials Chemistry

Dissociation of two-dimensional excitons in monolayer WSe2 Two-dimensional (2D) semiconducting materials are promising building blocks for optoelectronic applications, many of which require efficient dissociation of excitons into free electrons and holes. However, the strongly bound excitons arising from the enhanced Coulomb interaction in these monolayers suppresses the creation of free carriers. Here, we identify the main exciton dissociation mechanism through time and spectrally resolved photocurrent measurements in a monolayer WSe<sub>2</sub> p-n junction. We find that under static in-plane electric field, excitons dissociate at a rate corresponding to the one predicted for tunnel ionization of 2D Wannier-Mott excitons. This study is essential for understanding the photoresponse of 2D semiconductors and offers design rules for the realization of efficient photodetectors, valley dependent optoelectronics, and novel quantum coherent phases. Exciton ; Monolayer ; Dissociation (chemistry) ; Semiconductor ; Photocurrent ; Coulomb ; Ionization ; Impact ionization 2D Materials and Applications ; Graphene research and applications ; Chemical and Physical Properties of Materials
Authors: Matteo Lusi
Source: Crystal Growth & Design (article, 2018-04-30)
Field: Chemistry — Physical and Theoretical Chemistry

Engineering Crystal Properties through Solid Solutions The control of structures and properties in crystalline materials has many returns that justify the\nincreasing efforts in this direction. Traditionally, crystal engineering focused on the rational design of single\ncomponent molecular crystals or supramolecular compounds (i.e., cocrystals). More recently, reports on crystalline\nsolid solutions have become common in crystal engineering research. Crystalline solid solutions are characterized by\na structural disorder that enables the variation of stoichiometry in continuum. Often such variation corresponds to a\nvariation of structural and physicochemical properties, and offers an opportunity for the materials’ fine-tuning. In\nsome cases, though, new and unexpected properties emerge. As illustrated here, both behaviors make solid solutions\nparticularly relevant to the scope of crystal engineering. Crystal engineering ; Supramolecular chemistry ; Materials science ; Crystal (programming language) ; Rational design ; Stoichiometry ; Crystal structure ; Solid solution ; Scope (computer science) ; Component (thermodynamics) ; Nanotechnology ; Crystallography ; Chemistry ; Computer science ; Thermodynamics ; Physical chemistry ; Physics Crystallography and molecular interactions ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallization and Solubility Studies
Authors: Wenhui He | Raya Ifraemov | Arik Raslin | Idan Hod
Source: Advanced Functional Materials (article, 2018-02-12)
Field: Chemistry — Inorganic Chemistry

Room‐Temperature Electrochemical Conversion of Metal–Organic Frameworks into Porous Amorphous Metal Sulfides with Tailored Composition and Hydrogen Evolution Activity Abstract The conversion of metal–organic frameworks (MOFs) into inorganic nanomaterials is considered as an attractive means to produce highly efficient electrocatalysts for alternative‐energy related applications. Yet, traditionally employed MOF‐conversion conditions (e.g., pyrolysis) commonly involve multiple complex high‐temperature reaction processes, which often make it challenging to control the composition, pore structure, and active‐sites of the MOF‐derived catalysts. Herein, a general, simple, room‐temperature method is presented for a controlled electrochemical conversion of MOF (EC‐MOF) films into porous, amorphous metal sulfides (a‐MS x ). Detailed X‐ray photoelectron spectroscopy analysis and control over independent EC‐MOF parameters (e.g., scan‐rate and potential window) enable to gain insights on the MOF‐conversion mechanisms, and in turn to fine‐tune the porosity and composition of the obtained MS x . As a result, a highly active amorphous cobalt sulfide (a‐CoS x ) electrocatalyst can be designed for hydrogen evolution reaction in neutral pH. Furthermore, the adjustable nature of the EC‐MOF method allows to draw conclusions about the correlation between the concentration of catalytically active species ( sites) and the hydrogen evolution properties of the a‐CoS x . Given the method's generality and the diversity of available MOF structures, EC‐MOF provides a compelling platform for a rational design of a wide variety of active electrocatalytic materials. Materials science ; Electrocatalyst ; Amorphous solid ; Metal-organic framework ; Catalysis ; Chemical engineering ; Cobalt sulfide ; Porosity ; Nanomaterials ; Electrochemistry ; Nanotechnology ; Electrode ; Organic chemistry ; Chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Lucas Lindsay | Chengyun Hua | Xiulin Ruan | Sangyeop Lee
Source: Materials Today Physics (article, 2018-12-01)
Field: Materials Science — Materials Chemistry

Survey of ab initio phonon thermal transport Thermal conductivity ; Phonon ; Materials science ; Microelectronics ; Boltzmann equation ; Curse of dimensionality ; Statistical physics ; Thermal ; Nanotechnology ; Ab initio ; Engineering physics ; Computer science ; Condensed matter physics ; Physics ; Thermodynamics ; Quantum mechanics ; Artificial intelligence Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity