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10 results for “ab initio calculations”
TREXIO files used for the validation tests in the paper entitled 'TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods'.
<p>The TREXIO files used for the validation tests in the paper entitled TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods. The detail about the TREXIO library is described in the JCP article [J. Chem. Phys. 158, 174801 (2023)] and the GitHub repository [https://github.com/TREX-CoE/trexio]. The TREXIO files were generated using TREXIO version 2.3.2 (and the corresponding Python API version 1.3.2).</p>
amorphous carbon ab-initio calculation dataset
<p><strong>Description</strong></p> <p>This dataset was used in our manuscript titled “Persistent homology-based descriptor for machine-learning potential of amorphous structures” (arXiv:2206.13727 [cs.LG] <a href="https://arxiv.org/abs/2206.13727">https://arxiv.org/abs/2206.13727</a>).</p> <p><strong>Methods to generate the dataset</strong></p> <p>The amorphous carbon dataset was generated using ab initio calculations with VASP software. We utilized the LDA exchange-correlation functional and the PAW potential for carbon. Melt-quench simulations were performed to create amorphous and liquid-state structures. A simple cubic lattice of 216 carbon atoms was chosen as the initial state. Simulations were conducted at densities of 1.5, 1.7, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm<sup>3</sup> to produce a variety of structures. The NVT ensemble was employed for all melt-quench simulations, and the density was adjusted by modifying the size of the simulation cell. A time step of 1 fs was used for the simulations. For all densities, only the Γ points were sampled in the k-space. To increase structural diversity, six independent simulations were performed.</p> <p>In the melt-quench simulations, the temperature was raised from 300 K to 9000 K over 2 ps to melt carbon. Equilibrium molecular dynamics (MD) was conducted at 9000 K for 3 ps to create a liquid state, followed by a decrease in temperature to 5000 K over 2 ps, with the system equilibrating at that temperature for 2 ps. Finally, the temperature was lowered from 5000 K to 300 K over 2 ps to generate an amorphous structure.</p> <p>During the melt-quench simulation, 30 snapshots were taken from the equilibrium MD trajectory at 9000 K, 100 from the cooling process between 9000 and 5000 K, 25 from the equilibrium MD trajectory at 5000 K, and 100 from the cooling process between 5000 and 300 K. This yielded a total of 16,830 data points.</p> <p>Data for diamond structures containing 216 atoms at densities of 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3 were also prepared. Further data on the diamond structure were obtained from 80 snapshots taken from the 2 ps equilibrium MD trajectory at 300 K, resulting in 560 data points.</p> <p>To validate predictions for larger structures, we generated data for 512-atom systems using the same procedure as for the 216-atom systems. A single simulation was conducted for each density. The number of data points was 2,805 for amorphous and liquid states.</p> <p><strong>Contents of each folder </strong></p> <p>・216atom_amorphous: Contains six xyz files generated from the trajectory of the melt-quench simulation.</p> <p>・216atom_crystal: Contains a single xyz file with data of diamond structures containing 216 atoms at densities of 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, and 3.5 g/cm3.</p> <p>・512atom_amorphous: Contains a single xyz file with data of 512-atom systems.</p> <p>・dataset_train_test_split: The training and test data used in the manuscript, constructed from splitting the entire 216atom_amorphous dataset.</p>
Linear machine learning based force matching for amorphous silica: How close are the classical two-body potentials to ab initio calculations?
<p>Please later see our manuscript (in submission) for details.</p>
Data from: Ab initio grand canonical Monte Carlo calculation of grain boundary composition and structure
Open the record for dataset details and reuse information.
Dataset for deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
<p>Dataset files of atomic structures and Hamiltonian matrices of graphene, MoS<sub>2</sub>, bilayer graphene and bilayer bismuthene.</p> <p>Please note that the DFT results in this dataset were calculated using OpenMX. This means that if you want to use a DeepH model trained on this dataset to calculate properties, you need to use the <a href="https://github.com/mzjb/overlap-only-OpenMX">overlap calculated using OpenMX</a>. The orbital information required for overlap calculations can be found in the <a href="https://www.nature.com/articles/s43588-022-00265-6">paper</a>.</p>
Stability and electronic properties of K-Sb and Na-Sb binary crystals from high-throughput ab initio calculations
<p>This is the dataset to the identically named publication. All data is stored in an AiiDA archive.</p> <p>In a high-throughput analysis based on density-functional theory (DFT) with the advanced r2SCAN functional, we focus on the two families of binary crystals with K-Sb and Na-Sb compositions that are expected to form during evaporation growth of multi-alkali antimonide photocathodes. Starting from an initial pool of structures mined from existing computational databases, we employed automatized routines included in the in-house developed library aim2dat to determine the stability and the electronic properties of the aforementioned systems. We analyze the formation energy, the band structure and the projected density of states of selected stable compounds.</p>
Data and code for "Efficient calculation of the lattice thermal conductivity by atomistic simulations with ab-initio accuracy"
<p>This data set contains data and code related to the publication "Efficient calculation of the lattice thermal conductivity by atomistic simulations with ab-initio accuracy".</p>
Results of ab initio (DFT) calculations of fullerenes
<p>This is the collection of input and output files of ground state DFT calculations of fullerenes. They were carried out using Quantum Espresso. These calculations correspond to the article <i>Electron-vibrational renormalization in fullerenes through ab initio and machine learning methods, by Pablo Garcıa-Risueno, Eva Armengol, Angel Garcıa-Cerdana, David Carrasco-Busturia and Juan Marıa Garcıa-Lastra. </i>The results make it possible to calculate renormalisations of electronic eigenvalues due to electron-vibrational (electron-phonon) interaction using the frozen-phonon method.</p>
Electronic structure fingerprints of nickel-cobalt-manganese oxide from x-ray spectroscopy and high-throughput ab initio calculations
<p>AiiDA archives of the calculations presented in the paper "Electronic structure fingerprints of nickel-cobalt-manganese oxide from x-ray spectroscopy and high-throughput <em>ab initio</em> calculations".</p> <p> </p> <ul> <li>structures_and_general_info.aiida: The "EnumlibCalcJob" to generate the structural candidates, the resulting initial structures and AiiDA "Dict" nodes containing the mapping of the different steps (via their corresponding <em>uuid</em>) to each structure.</li> <li>pre_optimization.aiida: The relevant calculations to perform the pre-relaxation.</li> <li>optimization.aiida: The relevant calculations to perform the final structural optimization.</li> <li>electronic_structure.aiida: The bandstructure and DOS/PDOS calculations. All preliminary calculations such as SCF and NSCF calculations to obtain the eigenvalues are included as well.</li> </ul> <p>Finally, the Pandas DataFrame containing the PDOS for each site of all the structures (resolved into orbital and spin contributions) which builds the foundation for the presented analysis is stored in the `pickle` format in `pdos_all.pckl`.</p>
Free and defect-bound (bi)polarons in LiNbO3: Atomic structure and spectroscopic signatures from ab initio calculations
<p>Dataset of the publication “Free and defect-bound (bi)polarons in LiNbO<sub>3</sub>: Atomic structure and spectroscopic signatures from ab initio calculations“, F. Schmidt, A. L. Kozub, T. Biktagirov, C. Eigner, C. Silberhorn, A. Schindlmayr, W. G. Schmidt, and U. Gerstmann, Physical Review Research 2, 043002 (2020) ( <a href="https://doi.org/10.1103/PhysRevResearch.2.043002">https://doi.org/10.1103/PhysRevResearch.2.043002</a> ). The tar file includes the data on which the plots shown in figures 2, 3, 4, 6, 7, 8, and 9 are based.</p>
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OpenNeuro
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