jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Loukas Kollias | Roger Rousseau | Vassiliki‐Alexandra Glezakou | Matteo Salvalaglio
Source: ChemRxiv (preprint, 2021-12-23)
Field: Chemistry — Inorganic Chemistry

Understanding MOF nucleation from solution with Evolving Graphs Molecular modeling is ordinarily employed to understand the synthesis of complex materials. In this work, we investigate the collective assembly of building units that have been experimentally observed to initiate Metal-Organic Framework (MOF) nucleation. MOFs exhibit attractive characteristics such as remarkable surface area and diverse porosities, however, a mechanistic understanding of their synthesis and scale-up remains underexplored due to the complicated nature of the building block interactions. Here, we tackle this problem with large-scale molecular dynamics simulations under a variety of synthesis conditions and mixture compositions. We observe that the connectivity of building units, as well as their level of crystalline order and fractal dimension, largely vary depending on the synthesis conditions. However, these properties naturally emerge when interpreting the self-assembly process of MOF nuclei as the time-evolution of an undirected graph. The results show that solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly, such diversity is captured by key features of the graph representation. Principal Component Analysis (PCA) on graph properties successfully deconvolutes MOF self-assembly to be characterized by a small number of molecular descriptors, such as average coordination number between half-secondary building units (half-SBUs) and fractal dimension, which can be followed by time-resolved spectroscopy. We conclude that graph theory can be used to understand complex processes such as MOF nucleation by providing molecular descriptors accessible by both simulation and experiment. Nucleation ; Fractal dimension ; Molecular dynamics ; Graph ; Computer science ; Materials science ; Fractal ; Biological system ; Nanotechnology ; Chemical physics ; Theoretical computer science ; Chemistry ; Computational chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Vivek Singh | Lothar Houben | linda shimon | Sidney Cohen | Ofra Golani | Yishay Feldman | Michal Lahav | Milko E. van der Boom
Source: ChemRxiv (preprint, 2021-03-02)
Field: Chemistry — Physical and Theoretical Chemistry

The Egg-Shaped Crystals That Should Not Be: A Brain-like Texture Combined with Single Crystallinity We demonstrate here a unique metallo-organic material where the appearance and the internal crystal structure contradict. The egg-shaped (ovoid) crystals have a "brain-like" texture. Although these micro-sized crystals are monodispersed; like fingerprints the grainy surfaces are never alike. Remarkable, our X-ray and electron diffraction studies unexpectedly revealed that these structures are single crystals comprising a continuous coordination network of homochiral hexagonal and triangular channels. By applying different reaction condition, a series of isostructural and monodisperse crystals were obtained having a monodomain appearance. These results indicate that families of isostructural crystals following the established rules for crystal growth contain hidden gems to be discovered with fascinating multidomain morphologies. Isostructural ; Crystallography ; Crystallinity ; Texture (cosmology) ; Hexagonal crystal system ; Materials science ; Dispersity ; Diffraction ; Crystal structure ; Chemistry ; Optics ; Physics ; Computer science ; Artificial intelligence ; Polymer chemistry Crystallography and molecular interactions ; Methane Hydrates and Related Phenomena ; Supramolecular Self-Assembly in Materials
Authors: Rocío Mercado | Esben Jannik Bjerrum | Ola Engkvist
Source: ChemRxiv (preprint, 2021-07-01)
Field: Materials Science — Materials Chemistry

Exploring graph traversal algorithms in graph-based molecular generation Here we explore the impact of different graph traversal algorithms on molecular graph generation. We do this by training a graph-based deep molecular generative model to build structures using a node order determined via either a breadth- or depth-first search algorithm. What we observe is that using a breadth-first traversal leads to better coverage of training data features compared to a depth-first traversal. We have quantified these differences using a variety of metrics on a dataset of natural products. These metrics include: percent validity, molecular coverage, and molecular shape. We also observe that using either a breadth- or depth-first traversal it is possible to over-train the generative models, at which point the results with the graph traversal algorithm are identical Graph traversal ; Tree traversal ; Computer science ; Graph ; Algorithm ; Depth-first search ; Breadth-first search ; Generative grammar ; Theoretical computer science ; Generative model ; Node (physics) ; Artificial intelligence ; Search algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Luca Tonti | Fabián A. García Daza | Alessandro Patti
Source: Journal of Molecular Liquids (preprint, 2021-06-18)
Field: Materials Science — Materials Chemistry

Diffusion of globular macromolecules in liquid crystals of colloidal cuboids Liquid crystal ; Isotropy ; Diffusion ; Chemical physics ; Phase (matter) ; Macromolecular crowding ; Materials science ; Colloid ; Globular protein ; Macromolecule ; Monte Carlo method ; Cuboid ; Range (aeronautics) ; Physics ; Condensed matter physics ; Crystallography ; Chemistry ; Thermodynamics ; Optics ; Geometry ; Physical chemistry Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Theoretical and Computational Physics
Authors: Adam Włodarczyk | Mariusz Uchroński | Agata Podsiadły-Paszkowska | Joanna Irek | Bartłomiej M. Szyja
Source: Research Square (preprint, 2021-11-17)
Field: Materials Science — Materials Chemistry

Mixing ReaxFF Parameters for Transition Metal Oxides Using Force-Matching Method. Abstract We present the development of the method for the refitting the ReaxFF parameters for a system consisting of the mixed transition metal oxides. We have tested several methods allowing to calculate the differences between the vectors of the forces acting on atoms obtained from the reference DFT simulation and the parameters-dependent ReaxFF. We conclude that the footrule method yields the best parameters among the investigated options. We then validate the parameters using the system consisting of the hematite supported (TiO 2 ) n clusters. The results indicate the refitted parameters allow to obtain much better geometries of the clusters upon MD simulation on the ReaxFF level, and despite the short timescale lead to the stable structures. ReaxFF ; Transition metal ; Mixing (physics) ; Materials science ; Matching (statistics) ; Chemical physics ; Nanotechnology ; Computational chemistry ; Molecular dynamics ; Chemistry ; Organic chemistry ; Mathematics ; Physics ; Catalysis ; Quantum mechanics Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties ; High-pressure geophysics and materials
Authors: Yuanheng Wang | Jiajun Ren | Zhigang Shuai
Source: ChemRxiv (preprint, 2021-04-02)
Field: Chemistry — Physical and Theoretical Chemistry

Evaluating the Anharmonicity Contributions to the Molecular Excited State Internal Conversion Rates with Finite Temperature TD-DMRG In this work, we propose a new method to calculate the molecular nonradiative electronic relaxation rates based on the numerically exact time-dependent density matrix renormalization group theory (TD-DMRG). This method could go beyond the existing frameworks under the harmonic approximation (HA) of the potential energy surface (PES) so that the important anharmonic effect could be considered when large electronic energy is transferred into the vibrations to excite them to the high energy levels. The effectiveness and scalability of the method are verified in calculating the internal conversion (IC) rate of azulene by comparing it with the analytically exact results under HA. Furthermore, we investigate the validity of HA in a two-mode model with Morse potential. We find that HA is unsatisfactory unless only the lowest several vibrational states of the lower electronic state are involved in the transition process when the adiabatic excitation energy is relatively low. As the excitation energy increases, HA first underestimates and then overestimates the IC rates when the excited state PES shifts towards the dissociative side of the ground state PES. On the contrary, HA slightly overestimates the IC rates when the excited state PES shifts towards the repulsive side. In both cases, higher temperature enlarges the error of HA. Excited state ; Anharmonicity ; Density matrix renormalization group ; Adiabatic process ; Excitation ; Ground state ; Physics ; Atomic physics ; Work (physics) ; Chemistry ; Quantum mechanics ; Renormalization group Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants ; Thermal Radiation and Cooling Technologies
Authors: Fabian Bertoldo | Sajid Ali | Simone Manti | Kristian S. Thygesen
Source: arXiv (Cornell University) (preprint, 2021-10-05)
Field: Materials Science — Materials Chemistry

Quantum point defects in 2D materials: The QPOD database Atomically thin two-dimensional (2D) materials are ideal hosts of quantum defects as they offer easier control, manipulation and read-out of defect states compared to bulk systems. Here we introduce the Quantum Point Defect (QPOD) database of more than 1900 defect systems with 503 unique intrinsic point defects (vacancies and antisites) in 82 2D insulators. The Atomic Simulation Recipes (ASR) workflow framework was used to perform density functional theory (DFT) calculations of defect formation energies, charge transition levels, Fermi level positions, equilibrium defect and carrier concentrations, transition dipole moments, hyperfine coupling, and zero-field splitting. Excited states and photoluminescence spectra were calculated for selected high-spin defects. In this paper we describe the calculations and workflow behind the QPOD database, present an overview of its content, and discuss some general trends and correlations in the data. We analyse the degree of defect tolerance as well as intrinsic dopability of the host materials and identify promising defects for quantum technological applications. The database is freely available and can be browsed via a web-app interlinked with the Computational 2D Materials Database (C2DB). Crystallographic defect ; Photoluminescence ; Dipole ; Quantum dot ; Condensed matter physics ; Density functional theory ; Materials science ; Database ; Computer science ; Physics ; Nanotechnology ; Quantum mechanics ; Optoelectronics Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Machine Learning in Materials Science
Authors: Jiyu Hu | Yongqing Ma | Xucai Kan | Chaocheng Liu
Source: Research Square (preprint, 2021-02-19)
Field: Materials Science — Ceramics and Composites

Observation of Spin-Glass Behavior in Spinel Compound CoGa2O4 Spinel ; Spin glass ; Materials science ; Condensed matter physics ; Mineralogy ; Geology ; Physics ; Metallurgy Glass properties and applications ; Microwave Dielectric Ceramics Synthesis ; Luminescence Properties of Advanced Materials
Authors: Harbil Bediaga
Source:  (article, 2021-02-19)
Field: Materials Science — Materials Chemistry

On the Study of Crystal Structures by Simulation and Modeling This work is a review of techniques and models for the simulation of crystalline structures. On the one hand, molecular simulation techniques are explained, including quantum techniques such as ab initio, semi-empirical and DFT, and on the other hand classical techniques such as minimization, molecular dynamics and Monte Carlo. Applications of molecular simulation techniques are also analyzed: determination of crystalline structures, how to proceed to create a model and Software example. Molecular dynamics ; Statistical physics ; Computer science ; Monte Carlo method ; Ab initio ; Software ; Molecular model ; Computational science ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Rocío Mercado | Esben Jannik Bjerrum | Ola Engkvist
Source: ChemRxiv (preprint, 2021-09-20)
Field: Materials Science — Materials Chemistry

Exploring graph traversal algorithms in graph-based molecular generation Here we explore the impact of different graph traversal algorithms on molecular graph generation. We do this by training a graph-based deep molecular generative model to build structures using a node order determined via either a breadth- or depth-first search algorithm. What we observe is that using a breadth-first traversal leads to better coverage of training data features compared to a depth-first traversal. We have quantified these differences using a variety of metrics on a dataset of natural products. These metrics include: percent validity, molecular coverage, and molecular shape. We also observe that using either a breadth- or depth-first traversal it is possible to over-train the generative models, at which point the results with the graph traversal algorithm are identical Graph traversal ; Tree traversal ; Computer science ; Graph ; Algorithm ; Depth-first search ; Breadth-first search ; Generative grammar ; Theoretical computer science ; Generative model ; Node (physics) ; Artificial intelligence ; Search algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ayana Ghosh | Christopher T. Nelson | Mark P. Oxley | Xiaohang Zhang | Maxim Ziatdinov | Ichiro Takeuchi | Sergei V. Kalinin
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Direct mapping of polarization fields from STEM images: A Deep Learning based exploration of ferroelectrics Scanning transmission electron microscopy (STEM) has emerged as a powerful tool over the last decade for probing the atomic structures of complex materials with picometer precision. This has unlocked the gateway towards exploring functional properties such as ferroelectricity, ferroelasticity, and chemical phenomena on the atomic scale. The well-established approach 1-8 to quantitively analyze such images involve finding atom positions or atomic columns corresponding to A-site and B-site cations and account for atomic displacements to retrieve information about order parameter fields such as polarization. Polarization maps retrieved utilizing the traditional approach for a [100]psuedocubic BiFeO3 (BFO) film on SrTiO3 (STO) substrate is shown in Figure However, this process of parameterization of all atoms and consequently mapping of polarization fields is highly labor intensive. The ad-hoc Gaussian fitting to position the atomic column center and mis-tilts in measurements also lead to systematic errors in the polarization values.Hence, an alternate approach becomes crucial to not only bypass these shortcomings but also facilitate rapid transformation of STEM images for paving the pathway to develop physics-informed analyses and automated experiments. Polarization (electrochemistry) ; Content (measure theory) ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Mathematics ; Physical chemistry Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Geophysical and Geoelectrical Methods
Authors: Sun-Ting Tsai | Zachary A. Smith | Pratyush Tiwary
Source: arXiv (Cornell University) (preprint, 2021-04-27)
Field: Chemistry — Spectroscopy

SGOOP-d: Estimating Kinetic Distances and Reaction\nCoordinate Dimensionality for Rare Event Systems from Biased/Unbiased\nSimulations Understanding kinetics including reaction pathways and associated transition\nrates is an important yet difficult problem in numerous chemical and biological\nsystems especially in situations with multiple competing pathways. When these\nhigh-dimensional systems are projected on low-dimensional coordinates, which\nare often needed for enhanced sampling or for interpretation of simulations and\nexperiments, one can end up losing the kinetic connectivity of the underlying\nhigh-dimensional landscape. Thus in the low-dimensional projection metastable\nstates might appear closer or further than they actually are. To deal with this\nissue, in this work we develop a formalism that learns a multi-dimensional yet\nminimally complex reaction coordinate (RC) for generic high-dimensional\nsystems. When projected along this RC, all possible kinetically relevant\npathways can be demarcated and the true high-dimensional connectivity is\nmaintained. One of the defining attributes of our method lies in that it can\nwork on long unbiased simulations as well as biased simulations often needed\nfor rare event systems. We demonstrate the utility of the method by studying a\nrange of model systems including conformational transitions in a small peptide\nAce-Ala$_3$-Nme, where we show how two-dimensional and three-dimensional\nreaction coordinate found by our previously published spectral gap optimization\nmethod "SGOOP" [P. Tiwary and B. J. Berne, Proc. Natl. Acad. Sci. 113, 2839\n(2016)] can capture the kinetics for 23 and all 28 out of the 28 dominant\nstate-to-state transitions respectively.\n Reaction coordinate ; Metastability ; Curse of dimensionality ; Statistical physics ; Coordinate system ; Kinetic energy ; Rare events ; Formalism (music) ; Computer science ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Classical mechanics ; Artificial intelligence ; Biology Mass Spectrometry Techniques and Applications ; DNA and Nucleic Acid Chemistry ; Spectroscopy and Quantum Chemical Studies
Authors: Gopi Samudrala | Kallol Chakrabarty | Paul A. Baker | Bernabe S. Tucker | Yogesh K. Vohra | Shane A. Catledge
Source: Materials (article, 2021-03-13)
Field: Materials Science — Materials Chemistry

Selective Deposition of Hard Boron-Carbon Microstructures on Silicon Boron-rich B-C compounds with high hardness have been recently synthesized by the chemical vapor deposition (CVD) method. In this paper, we present our successful efforts in the selective growth of microstructures of boron-carbon compounds on silicon substrates. This was achieved by combining microfabrication techniques such as maskless lithography and sputter deposition with the CVD technique. Our characterization studies on these B-C microstructures showed that they maintain structural and mechanical properties similar to that of their thin-film counterparts. The methodology presented here paves the way for the development of microstructures for microelectromechanical system (MEMS) applications which require custom hardness and strength properties. These hard B-C microstructures are an excellent choice as support structures in MEMS-based devices. Microstructure ; Materials science ; Microfabrication ; Boron ; Microelectromechanical systems ; Chemical vapor deposition ; Silicon ; Nanotechnology ; Deposition (geology) ; Carbon fibers ; Characterization (materials science) ; Sputter deposition ; Thin film ; Sputtering ; Composite material ; Optoelectronics ; Fabrication ; Chemistry ; Organic chemistry Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Manoj Settipalli | Vitaly Proshchenko | Sanghamitra Neogi
Source: arXiv (Cornell University) (preprint, 2021-11-24)
Field: Materials Science — Materials Chemistry

Effect of Electron-Phonon and Electron-Impurity Scattering on Electronic Transport Properties of Silicon/Germanium Superlattices Semiconductor superlattices have been extensively investigated for thermoelectric applications, to explore the effects of compositions, interface structures, and lattice strain environments on the reduction of thermal conductivity, and improvement of efficiency. Most studies assumed that their electronic properties remain unaffected compared to their bulk counterparts. However, recent studies demonstrated that electronic properties of silicon (Si)/germanium (Ge) superlattices show significant variations depending on compositions and growth substrates. These studies used a constant relaxation time approximation, and ignored the effects of electron scattering processes. Here, we consider electron scattering with phonons and ionized impurities, and report first-principles calculations of electronic transport properties including the scattering rates. We investigate two classes of Si/Ge superlattices: superlattices with varied compositions grown on identical substrates and with identical compositions but grown on different substrates. We illustrate the relationship between the energy bands of the superlattices and the electron-phonon relaxation times. We model the electron-ionized impurity interaction potentials by accounting for the in-plane and the cross-plane structural anisotropy. Our analysis reveals that the inclusion of electron-phonon and electron-impurity scattering processes can lead to ~1.56-fold improved peak power-factors, compared to that of bulk Si. We observe that superlattices can also display dramatically reduced power-factors for specific lattice strain environments. Such reduction could cancel out thermoelectric efficiency improvements due to reduced thermal conductivities. Our study provides insight to predict variation of electronic properties due to changes in lattice strain environments, essential for designing superlattices with optimized electronic properties. Superlattice ; Condensed matter physics ; Materials science ; Germanium ; Scattering ; Phonon ; Thermoelectric effect ; Ionized impurity scattering ; Impurity ; Electron ; Phonon scattering ; Semiconductor ; Silicon ; Seebeck coefficient ; Thermal conductivity ; Chemistry ; Doping ; Optoelectronics ; Physics ; Optics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Semiconductor Quantum Structures and Devices