382 Works

A Standard Solid State Pseudopotentials (SSSP) library optimized for accuracy and efficiency (Version 1.0, data download)

Gianluca Prandini, Antimo Marrazzo, Ivano E. Castelli, Nicolas Mounet & Nicola Marzari
Despite the enormous success and popularity of density functional theory, systematic verification and validation studies are still very limited both in number and scope. Here, we propose a universal standard protocol to verify publicly available pseudopotential libraries, based on several independent criteria including verification against all-electron equations of state and plane-wave convergence tests for phonon frequencies, band structure, cohesive energy and pressure. Adopting these criteria we obtain two optimal pseudopotential sets, namely the Standard Solid...

In Silico Design of 2D and 3D Covalent Organic Frameworks for Methane Storage Applications

Rocio Mercado, Rueih-Sheng Fu, Aliaksandr V. Yakutovich, Leopold Talirz, Maciej Haranczyk & Berend Smit
Here we present 69,840 covalent organic frameworks (COFs) assembled in silico from a set of 666 distinct organic linkers into 2D-layered and 3D configurations. We investigate the feasibility of using these frameworks for methane storage by using grand-canonical Monte Carlo (GCMC) simulations to calculate their deliverable capacities (DCs). From these calculations, we predict that the best structure in the database is linker91_C_linker91_C_tbd, a structure composed of carbon-carbon bonded triazine linkers in the tbd topology. This...

Accurate Characterization of the Pore Volume in Microporous Crystalline Materials (Data Download)

Daniele Ongari, Peter G. Boyd, Senja Barthel, Matthew Witman, Maciej Haranczyk & Berend Smit
Project Abstract: Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable and it can also be obtained from the refined unit cell by a number of computational techniques. In this work we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium and probe center pore volume, by studying a database of more than 5000...

Mail-order metal-organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules

Richard L. Martin, Li-Chiang Lin, Kuldeep Jariwala, Berend Smit & Maciej Haranczyk
Metal–organic frameworks (MOFs), a class of porous materials, are of particular interest in gas storage and separation applications due largely to their high internal surface areas and tunable structures. MOF-5 is perhaps the archetypal MOF; in particular, many isoreticular analogues of MOF-5 have been synthesized, comprising alternative dicarboxylic acid ligands. In this contribution we introduce a new set of hypothesized MOF-5 analogues, constructed from commercially available organic molecules. We describe our automated procedure for hypothetical...

High-throughput computational screening of nanoporous adsorbents for CO 2 capture from natural gas

Efrem Braun, Alexander F. Zurhelle, Wouter Thijssen, Sondre Schnell, Li-Chiang Lin, Jihan Kim, Joshua A. Thompson & Berend Smit
With the growth of natural gas as an energy source, upgrading CO2-contaminated supplies has become increasingly important. Here we develop a single metric that captures how well an adsorbent performs the separation of CH4 and CO2, and we then use this metric to computationally screen tens of thousands of all-silica zeolites. We show that the most important predictors of separation performance are the CO2 heat of adsorption (Qst, CO2) and the CO2 saturation loading capacity....

Adatom-Induced Local Melting

Ngoc Linh Nguyen, Francesca Baletto & Nicola Marzari
We introduce and discuss the phenomenon of adatom-induced surface local melting, using extensive first-principles molecular dynamics simulations of Al(100) taken as a paradigmatic case of a non-premelting surface that nevertheless displays facile adatom diffusion with single and multiple exchange pathways. Here, a single adatom deposited on the surface is sufficient to nucleate a localized and diffusing liquid-like region that remains confined to the surface layer, but with an area that increases with temperature; in the...

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds (Data download)

Nicolas Mounet, Marco Gibertini, Philippe Schwaller, Davide Campi, Andrius Merkys, Antimo Marrazzo, Thibault Sohier, Ivano E. Castelli, Andrea Cepellotti, Giovanni Pizzi & Nicola Marzari
Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have been successfully synthesized or exfoliated. Here, we search for novel 2D materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van-der-Waals density-functional theory, validated...

Record removed

The data and metadata of this record was submitted for the review phase of the associated manuscript only and not intended for publication. The data intended for public use will be made available in version v3 of this record under doi: 10.24435/materialscloud:2018.0016/v3.

EAM potential for Hf-Nb-Ta-Zr high-entropy alloy

Soumyadipta Maiti & Walter Steurer
This is a LAMMPS readable EAM type potential file for Hf-Nb-Ta-Zr based high-entropy alloys (HEAs). In this potential file the elements are sequenced as Hf, Nb, Ta and Zr, respectively. This potential was previously used to model the HEA for the local lattice distortions due to short-range order clustering found in the alloy after long-term annealing at high-temperature. The input to build this potential is based on the physical properties of pure elements such as...

Applicability of tail-corrections in the molecular simulations of porous materials

Kevin Maik Jablonka, Daniele Ongari & Berend Smit
Molecular simulations with periodic boundary conditions require to define a certain cutoff distance beyond which pairwise dispersion interactions are neglected. For the simulation of homogeneous phases it is well-established to use tail-corrections, that can remedy this truncation of the potential. These corrections are built under the assumption that beyond the cutoff the radial distribution function is equal to one. In this work we shed some light on the discussion whether or not tail corrections should...

Termini effects on the optical properties of graphene nanoribbons

Claudia Cardoso, Andrea Ferretti & Deborah Prezzi
First principles the optical response of finite-length armchair-edged graphene nanoribbons (AGNRs) within the framework of many-body perturbation theory. As a result of the explicit inclusion of zigzag extremities, we identify low-energy and low-intensity excitations that are expected to be almost independent of the GNR length. These excitations coexist with bulk-like excitations, which have the same origin as the ones characterizing infinite AGNRs. Our results are used to rationalize termini effects on the optical response of...

Supplementary Data for “Non-Abelian band topology in noninteracting metals”

QuanSheng Wu, Alexey A. Soluyanov & Tomáš Bzdušek
Electron energy bands, which are studied to explain electronic and optical properties of crystalline solids, often exhibit degeneracies called band-structure nodes. Here, we introduce non-Abelian topological charges that characterize line nodes inside the momentum space of -symmetric crystalline metals with weak spin-orbit coupling. We show that these are quaternion charges, similar to those describing disclinations in biaxial nematics. Starting from two-band considerations, we develop the complete many-band description of nodes in the presence of and...

Exploring chemical space in the search for improved Azoheteroarene-based photoswitches

Sergi Vela, Clémence Corminboeuf & Constantin Krüger
In the quest for improved photo switches, azoheteroarenes have emerged as a potential alternative to azobenzene. However, to date the number and types of these species that have subjected to study is insufficient to provide an in-depth understanding of the photochemical effects brought about by different substituents. Here, we computationally screen the optical properties and thermal stabilities of 512 azoheteroarenes that consist of eight different N-containing heteroarenes combined with 64 substitution patterns. The most promising...

Tailoring Bond Topologies in Open-Shell Graphene Nanostructures

Shantanu Mishra, Carlo Antonio Pignedoli & Roman Fasel
The data contained in this record, raw data of images and input files to reproduce calculations, support our recent report for the on-surface synthesis and characterization of two ultralow-gap open-shell molecules, namely peri-tetracene, a benzenoid graphene fragment with zigzag edge topology, and dibenzo[a,m]dicyclohepta[bcde,nopq]rubicene, a nonbenzenoid nonalternant structural isomer of peri-tetracene with two embedded azulene units. Our results provide an understanding of the ramifications of altered bond topologies at the single-molecule scale, with the prospect of...

Data-driven studies of magnetic two-dimensional materials

Trevor David Rhone, Wei Chen, Shaan Desai, Amir Yacoby & Efthimios Kaxiras
We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

Mail-order metal-organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules

Richard L. Martin, Li-Chiang Lin, Kuldeep Jariwala, Berend Smit & Maciej Haranczyk
Metal–organic frameworks (MOFs), a class of porous materials, are of particular interest in gas storage and separation applications due largely to their high internal surface areas and tunable structures. MOF-5 is perhaps the archetypal MOF; in particular, many isoreticular analogues of MOF-5 have been synthesized, comprising alternative dicarboxylic acid ligands. In this contribution we introduce a new set of hypothesized MOF-5 analogues, constructed from commercially available organic molecules. We describe our automated procedure for hypothetical...

Hidden Beneath the Surface: Origin of the Observed Enantioselective Adsorption on PdGa(111)

Aliaksandr V. Yakutovich, Johannes Hoja, Daniele Passerone, Alexandre Tkatchenko & Carlo A. Pignedoli
We provide the input files to reproduce the data presented in the work: Hidden Beneath the Surface: Origin of the Observed Enantioselective Adsorption on PdGa(111) The files are subdivided in directories named after the figures/table of the manuscript A. V. Yakutovich, J. Hoja, D. Passerone, Alexandre Tkatchenko, C. A. Pignedoli J. Am. Chem. Soc., 140, 1401-1408 (2018) DOI: 10.1021/jacs.7b10980 In the work, we unravel the origin of the recently observed striking enantioselectivity of the PdGa(111)...

Capturing chemical intuition in synthesis of metal-organic frameworks

Seyed Mohamad Moosavi, Arunraj Chidambaram, Leopold Talirz, Maciej Haranczyk, Kyriakos C. Stylianou & Berend Smit
We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the...

Mining the C-C Cross-Coupling Genome using Machine Learning

Boodsarin Sawatlon, Alberto Fabrizio, Benjamin Meyer, Stefan N. Heinen, Matthew D. Wodrich, O. Anatole Von Lilienfeld & Clémence Corminboeuf
Applications of machine-learning (ML) techniques to the study of catalytic processes have begun to appear in the literature with increasing frequency. The computational speed up provided by ML allows the properties and energetics of thousands of prospective catalysts to be rapidly assessed. These results, once compiled into a database containing different properties, can be mined with the goal of establishing relationships between the intrinsic chemical properties of different catalysts and their overall catalytic performance. Previously,...

Applicability of tail-corrections in the molecular simulations of porous materials

Kevin Maik Jablonka, Daniele Ongari & Berend Smit
Molecular simulations with periodic boundary conditions require to define a certain cutoff distance beyond which pairwise dispersion interactions are neglected. For the simulation of homogeneous phases it is well-established to use tail-corrections, that can remedy this truncation of the potential. These corrections are built under the assumption that beyond the cutoff the radial distribution function is equal to one. In this work we shed some light on the discussion whether or not tail corrections should...

Picture of Wet Electron: A Localized Transient State in Liquid Water

Michele Pizzochero, Francesco Ambrosio & Alfredo Pasquarello
A transient state of the excess electron in liquid water preceding the development of the solvation shell, the so-called wet electron, has been invoked to explain spectroscopic observations, but its binding energy and atomic structure have remained highly elusive. Here, we carry out hybrid functional molecular dynamics to unveil the ultrafast solvation mechanism leading to the hydrated electron. In the pre-hydrated regime, the electron is found to repeatedly switch between a quasi-free electron state in...

Vanadium is an optimal element for strengthening in both fcc and bcc high-entropy alloys

Binglun Yin, Francesco Maresca & W. A. Curtin
The element Vanadium (V) appears unique among alloying elements for providing high strengthening in both the fcc Co-Cr-Fe-Mn-Ni-V and bcc Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr high-entropy alloy families. The origin of Vanadium’s special role is its atomic volume: large in the fcc alloys and small in the bcc alloys, and thus having a large misfit volume in both crystalline structures. A parameter-free theory applicable to both fcc and bcc HEAs rationalizes this finding, with predictions of strength across a...

Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide

April Cooper, Johannes Kästner, Alexander Urban & Nongnuch Artrith
This data set contains atomic structures of water clusters, bulk water and rock-salt Li8Mo2Ni7Ti7O32 in the XCrySDen [1] structure format (XSF), and total energies are included as additional meta information. The extended XSF format is compatible with the atomic energy network (aenet) package [2,3] for artificial neural network potential construction and application. The structures were generated using ab initio molecular dynamics (AIMD) simulations performed with the Vienna Ab Initio Simulation Package (VASP) [4,5] and projector-augmented...

A unified approach to enhanced sampling

Michele Invernizzi, Pablo Miguel Piaggi & Michele Parrinello
The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand tempering methods such as replica exchange that combine different thermodynamic ensembles in one...

Fast Bayesian force fields from active learning: study of inter-dimensional transformation of stanene

Yu Xie, Jonathan Vandermause, Lixin Sun, Andrea Cepellotti & Boris Kozinsky
Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor the quality of model predictions. A current limitation of existing GP force fields is that the prediction cost grows linearly with the size of the training data set, making accurate GP predictions slow. In this work, we exploit the special structure of the kernel function to construct a mapping of the...

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