jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jesús N. Pedroza‐Montero | Ignacio L. Garzón | Huziel E. Sauceda
Source: arXiv (Cornell University) (article, 2021-01-10)
Field: Materials Science — Materials Chemistry

On the forbidden graphene's ZO (out-of-plane optic) phononic band-analog vibrational modes in fullerenes The study of nanostructures' vibrational properties is at the core of nanoscience research, they are known to represent a fingerprint of the system as well as to hint the underlying nature of chemical bonds. In this work we focus on addressing how does the vibrational density of states (VDOS) of the carbon fullerene family ($C_n:~n=20\to720$ atoms) evolves from the molecular to the bulk material (graphene) behavior using density functional theory. We found that the fullerene's VDOS smoothly converges to the graphene characteristic shape-line with the only noticeable discrepancy in the frequency range of the out-of-plane optic (ZO) phonon band in graphene. From a comparison of both systems we obtain as main results that: 1)The pentagonal faces in the fullerenes impede the existence of the analog of the high frequency graphene's ZO phonons, 2)which in the context of phonons this could be interpreted as a compression (by 43\%) of the ZO phonon band by decreasing its maximum allowed radial-optic vibration frequency. 3)As a result, the deviation of fullerene's VDOS relative to graphene should result on important thermodynamical implications. The obtained insights can be extrapolated to other structures containing pentagonal rings such as nanostructure or as pentagonal defects in graphene. Graphene ; Phonon ; Fullerene ; Materials science ; Condensed matter physics ; Context (archaeology) ; Density functional theory ; Molecular vibration ; Band gap ; Nanotechnology ; Molecular physics ; Optoelectronics ; Physics ; Optics ; Quantum mechanics ; Raman spectroscopy Boron and Carbon Nanomaterials Research ; Thermal properties of materials ; Mechanical and Optical Resonators
Authors: Yutao Kuang | Jolene P. Reid
Source: ChemRxiv (preprint, 2021-12-03)
Field: Materials Science — Materials Chemistry

Transferrable Selectivity Profiles Enable Prediction in Synergistic Catalyst Space Organometallic intermediates participate in many multi-catalytic enantioselective transformations directed by a chiral catalyst, but the requirement of optimizing two catalyst components is a significant barrier to widely adopting this approach for chiral molecule synthesis. Algorithms can potentially accelerate the screening process by developing quantitative structure-function relationships from large experimental datasets. However, the chemical data available in this catalyst space is limited. We report a data-driven strategy that effectively translates selectivity relationships trained on enantioselectivity outcomes derived from one catalyst reaction systems where an abundance of data exists, to synergistic catalyst space. We describe three case studies involving different modes of catalysis (Brønsted acid, chiral anion, and secondary amine) that substantiate the prospect of this approach to predict and elucidate selectivity in reactions where more than one catalyst is involved. Ultimately, the success in applying our approach to diverse areas of asymmetric catalysis implies that this general workflow should find broad use in the study and development of new enantioselective, multi-catalytic processes. Catalysis ; Enantioselective synthesis ; Selectivity ; Combinatorial chemistry ; Chemical space ; Workflow ; Space (punctuation) ; Chemistry ; Computer science ; Biochemical engineering ; Computational chemistry ; Organic chemistry ; Drug discovery ; Database ; Engineering Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Joris Paret | Daniele Coslovich
Source: The Journal of Open Source Software (article, 2021-11-08)
Field: Materials Science — Materials Chemistry

partycls: A Python package for structural clustering partycls is a Python framework for cluster analysis of systems of interacting particles. By grouping particles that share similar structural or dynamical properties, partycls enables rapid and unsupervised exploration of the system's relevant features. It provides descriptors suitable for applications in condensed matter physics and integrates the necessary tools of unsupervised learning, such as dimensionality reduction, into a streamlined workflow. Through a simple and expressive interface, partycls allows one to open a trajectory file, perform a clustering based on the selected structural descriptor, and analyze and save the results with only a few lines of code. Python (programming language) ; Computer science ; Workflow ; Cluster analysis ; Dimensionality reduction ; Curse of dimensionality ; Unsupervised learning ; Open source ; Computational science ; Theoretical computer science ; Data mining ; Artificial intelligence ; Programming language ; Software ; Database Machine Learning in Materials Science ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: X. Feaugas | jiaqi Li | Malek Hallil | A. Metsue | A. Oudriss | J. Bouhattate
Source: Research Square (preprint, 2021-05-21)
Field: Materials Science — Metals and Alloys

Some Advances on Antagonist Effects of Grain Boundaries between the Trapping Process and the Fast Diffusion Path Investigated on Nickel Bicrystals Abstract Hydrogen-grain-boundaries interactions and their role in intergranular fracture are well accepted as one of the key features in understanding hydrogen embrittlement in a large variety of common engineer situations. These interactions implicate some fundamental processes classified as segregation, trapping and diffusion of the solute which can be studied as a function of grain boundary configuration. In the present study, we carried out an extensive analysis of four grain-boundaries based on the complementary of atomistic calculations and experimental data. We demonstrate that elastic deformation has an important contribution on the segregation energy which cannot be simply reduced to a volume change and need to consider the deviatoric part of strain. Additionally, some significant configurations of the segregation energy depend on the long-range elastic distortion and allows to rationalize the elastic contribution in three terms. By investigating the different energy barriers involved to reach all the segregation sites, the antagonist impact of grain boundaries on hydrogen diffusion and trapping process was elucidated. The segregation energy and migration energy are two fundamental parameters in order to classify the grain-boundaries as a trapping location or short circuit for diffusion. Grain boundary ; Materials science ; Diffusion ; Embrittlement ; Trapping ; Grain boundary diffusion coefficient ; Intergranular fracture ; Intergranular corrosion ; Elastic energy ; Grain boundary strengthening ; Effective diffusion coefficient ; Condensed matter physics ; Chemical physics ; Metallurgy ; Thermodynamics ; Corrosion ; Physics ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Material Properties and Failure Mechanisms
Authors: Lukáš Vlček | Shize Yang | Yongji Gong | Pulickel M. Ajayan | Wu Zhou | Matthew F. Chisholm | Maxim Ziatdinov | Rama K. Vasudevan | Sergei V. Kalinin
Source: npj Computational Materials (article, 2021-03-25)
Field: Materials Science — Materials Chemistry

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo x Re 1- x S 2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures. Randomness ; Mesoscopic physics ; Statistical physics ; Representation (politics) ; Dopant ; Range (aeronautics) ; Function (biology) ; Materials science ; Physics ; Computer science ; Doping ; Mathematics ; Condensed matter physics ; Statistics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Semiconductor materials and interfaces
Authors: Maxime Ducamp | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2021-01-20)
Field: Chemistry — Inorganic Chemistry

Systematic Study of the Thermal Properties of Zeolitic Frameworks We report a systematic study of the thermal and mechanical properties of 134 pure SiO2 zeolites through DFT-based calculations by making use of the quasi-harmonic approximation (out of a total of 242 known fully ordered zeolitic frameworks). The comparison of our results with reported experimental data for several zeolites revealed a very good accuracy and validated our simulation methodology. We observe a wide range of thermal expansion coefficients (from −5 to −35 MK−1), highlighting the great influence of the framework topology over this physical property, while demonstrating that all pure-silica zeolites exhibit negative thermal expansion (NTE). Our simulations also provide a path for the computation of the bulk modulus for each structure, as well as its pressure and temperature dependence. Results revealed a large gamut of bulk modulus values (from 8 to 134 GPa), showing that most frameworks display pressure-induced softening — but not all! Finally, this study provides some hints to the open question of experimental feasibility of zeolitic frameworks, showing that the experimentally synthesized structures appear to have a distinct distribution of thermal and mechanical properties. Thermal expansion ; Materials science ; Bulk modulus ; Thermal ; Modulus ; Range (aeronautics) ; Thermodynamics ; Softening ; Atmospheric temperature range ; Zeolite ; Topology (electrical circuits) ; Composite material ; Chemistry ; Mathematics ; Physics ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Jun Shen | Christian Heß
Source: TUbilio (Technical University of Darmstadt) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

High Surface Area VOₓ/TiO₂/SBA-15 Model Catalysts for Ammonia SCR Prepared by Atomic Layer Deposition The mode of operation of titania-supported vanadia (VOₓ) catalysts for NOx abatement using ammonia selective catalytic reduction (NH₃-SCR) is still vigorously debated. We introduce a new high surface area VOₓ/TiO₂/SBA-15 model catalyst system based on mesoporous silica SBA-15 making use of atomic layer deposition (ALD) for controlled synthesis of titania and vanadia multilayers. The bulk and surface structure is characterized by X-ray diffraction (XRD), UV-vis and Raman spectroscopy, as well as X-ray photoelectron spectroscopy (XPS), revealing the presence of dispersed surface VOx species on amorphous TiO₂ domains on SBA-15, forming hybrid Si–O–V and Ti–O–V linkages. Temperature-dependent analysis of the ammonia SCR catalytic activity reveals NOx conversion levels of up to ~60%. In situ and operando diffuse reflection IR Fourier transform (DRIFT) spectroscopy shows N–Hstretching modes, representing adsorbed ammonia and -NH₂ and -NH intermediate structures on Bronsted and Lewis acid sites. Partial Lewis acid sites with adjacent redox sites are proposed as the active sites and desorption of product molecules as the rate-determining step at low temperature. The high NOₓ conversion is attributed to the presence of highly dispersed VOₓ species and the moderate acidity of VOₓ supported on TiO₂/SBA-15. Catalysis ; X-ray photoelectron spectroscopy ; NOx ; Selective catalytic reduction ; Lewis acids and bases ; Raman spectroscopy ; Ammonia ; Atomic layer deposition ; Inorganic chemistry ; Mesoporous material ; Brønsted–Lowry acid–base theory ; Chemistry ; Chemical engineering ; Materials science ; Layer (electronics) ; Physical chemistry ; Nanotechnology ; Combustion ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Transition Metal Oxide Nanomaterials
Authors: Patrice Limelette
Source: Scientific Reports (article, 2021-12-21)
Field: Materials Science — Materials Chemistry

Thermopower, figure of merit and Fermi integrals The thermoelectric efficiency accounting for the conversion of thermal energy into electricity is usually given by the figure of merit which involves three transport coefficients, with the thermopower, the electrical and the thermal conductivities. These coefficients can be defined at a semi-classical level as a function of Fermi integrals which only allow analytical approximations in either highly degenerate or strongly non-degenerate regimes. Otherwise, the intermediate regime which is of interest in order to describe high thermoelectric performance requires numerical calculations. It is shown that these Fermi integrals can actually be calculated and that the transport coefficients can be reformulated accordingly. This allows for a new definition of the figure of merit which covers all the regimes of interest without numerical calculations. This formulation of the Fermi integrals also provides a good starting point in order to perform a power expansion leading to a new approximation relevant for the intermediate regime. It turns out that the transport coefficients can then be expanded by revealing their high temperatures asymptotic behaviors. These results shed new light on the thermoelectric properties of the materials and point out that the analysis of their high temperatures behaviors allow to characterize experimentally the energy dependence in the transport integrals. Figure of merit ; Degenerate energy levels ; Seebeck coefficient ; Physics ; Condensed matter physics ; Fermi energy ; Thermoelectric effect ; Point (geometry) ; Statistical physics ; Thermodynamics ; Quantum mechanics ; Mathematics ; Geometry ; Optics ; Electron Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Nisha Mehta | Thomas Fellowes | Jonathan White | Lars Goerigk
Source: ChemRxiv (preprint, 2021-01-05)
Field: Chemistry — Physical and Theoretical Chemistry

The CHAL336 Benchmark Set: How Well Do Quantum-Chemical Methods Describe Chalcogen-Bonding Interactions? We present the CHAL336 benchmark set—the most comprehensive database for the assessment of chalcogen-bonding (CB) interactions. After careful selection of suitable systems and identification of three high-level reference methods, the set comprises 336 dimers each consisting of up to 49 atoms and covers both σ- and π-hole interactions across four categories: chalcogen-chalcogen, chalcogen-π, chalcogen-halogen, and chalcogen-nitrogen interactions. In a subsequent study of DFT methods, we re- emphasize the need for using proper London-dispersion corrections when treating noncovalent interactions. We also point out that the deterioration of results and systematic overestimation of interaction energies for some dispersion-corrected DFT methods does not hint at problems with the chosen dispersion correction, but is a consequence of large density-driven errors. We conclude this work by performing the most detailed DFT benchmark study for CB interactions to date. We assess 98 variations of dispersion- corrected and -uncorrected DFT methods, and carry out a detailed analysis of 72 of them. Double-hybrid functionals are the most reliable approaches for CB interactions, and they should be used whenever computationally feasible. The best three double hybrids are SOS0-PBE0-2-D3(BJ), revDSD-PBEP86-D3(BJ), and B2NCPLYP-D3(BJ). The best hybrids in this study are ωB97M-V, PW6B95-D3(0), and PW6B95-D3(BJ). We do not recommend using any lower-rung DFT methods nor the popular B3LYP and MP2 approaches, which have been used to describe CB interactions in the past. We hope to inspire a change in computational protocols surrounding CB interactions that leads away from the commonly used, popular methods to the more robust and accurate ones recommended herein. We would also like to encourage method developers to use our set for the investigation and reduction of density-driven errors in new density functional approximations. Chalcogen ; Benchmark (surveying) ; Set (abstract data type) ; Dispersion (optics) ; Basis set ; Density functional theory ; Non-covalent interactions ; Computer science ; Computational chemistry ; Chemistry ; Materials science ; Bioinformatics ; Molecule ; Physics ; Crystallography ; Hydrogen bond ; Quantum mechanics ; Biology ; Organic chemistry Crystallography and molecular interactions ; Machine Learning in Materials Science ; Organic and Molecular Conductors Research
Authors: Tagir Akhmetshin | Arkadii Lin | Daniyar Mazitov | Evgenii Ziaikin | Timur Madzhidov | Alexandre Varnek
Source: ChemRxiv (preprint, 2021-12-06)
Field: Materials Science — Materials Chemistry

HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce a novel open-source architecture HyFactor which is inspired by previously reported DEFactor architecture and based on the hydrogen labeled graphs. Since the original DEFactor code was not available, its new implementation (ReFactor) was prepared in this work for the benchmarking purpose. HyFactor demonstrates its high performance on the ZINC 250K MOSES and ChEMBL data set and in molecular generation tasks, it is considerably more effective than ReFactor. The code of HyFactor and all models obtained in this study are publicly available from our GitHub repository: https://github.com/Laboratoire-de-Chemoinformatique/hyfactor Code refactoring ; Computer science ; Benchmarking ; Graph ; Source code ; Architecture ; Code (set theory) ; Set (abstract data type) ; Programming language ; Software engineering ; Theoretical computer science ; Software Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Carlos León | Roderick Melnik
Source:  (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Studies of Shape Memory Graphene Nanostructures via Integration of Physics-based Modelling and Machine Learning In this contribution, we study the properties of new promising graphenebased materials with shape memory effects. While traditional shape memory alloys have been extensively studied, it is a challenge to preserve shape memory properties at the nanoscale. As a result, new materials have been explored, among which graphene oxide (GO) crystals with ordered epoxy groups where a recoverable strain of 14.5% has already been reported. We use such nanoscale GO structures as a benchmark example for our studies here. MBTR and SOAP representations are employed in a general-purpose ML model to analyze the effect of long-range interactions in GO. Finally, a physics-based ML model allows us to build interatomic potentials for 2D and 1D systems. The model predicts quantum mechanical effects due to the electronic confinement in narrow nanoribbons and shows the evolution of the local minimal energies associated with two-phase states. Graphene ; Materials science ; Nanoscopic scale ; Benchmark (surveying) ; Shape-memory alloy ; Nanostructure ; Nanotechnology ; Oxide ; Quantum ; Statistical physics ; Range (aeronautics) ; Computer science ; Physics ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Polymer composites and self-healing
Authors: Agnieszka Jastrzębska | Jarosław Woźniak
Source: Materials (article, 2021-02-13)
Field: Materials Science — Materials Chemistry

Special Issue: New Findings of MXenes: Preparation, Properties and Applications in Biotechnology and Catalysis The discovery of graphene drove intensive studies towards novel two-dimensional (2D) materials [...]. MXenes ; Catalysis ; Nanotechnology ; Graphene ; Materials science ; Biochemical engineering ; Chemistry ; Biotechnology ; Computational biology ; Engineering ethics ; Biology ; Engineering ; Organic chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Maksim Kuznetsov | Daniil Polykovskiy
Source: Proceedings of the AAAI Conference on Artificial Intelligence (preprint, 2021-05-18)
Field: Materials Science — Materials Chemistry

MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation We propose a hierarchical normalizing flow model for generating molecular graphs. The model produces new molecular structures from a single-node graph by recursively splitting every node into two. All operations are invertible and can be used as plug-and-play modules. The hierarchical nature of the latent codes allows for precise changes in the resulting graph: perturbations in the first layer cause global structural changes, while perturbations in the consequent layers change the resulting molecule only marginally. Proposed model outperforms existing generative graph models on the distribution learning task. We also show successful experiments on global and constrained optimization of chemical properties using latent codes of the model. Invertible matrix ; Computer science ; Graph ; Theoretical computer science ; Generative model ; Node (physics) ; Control flow graph ; Generative grammar ; Algorithm ; Mathematics ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics