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5 results for “many-body models”
Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"
<p>This repository contains all raw data that were used to draw conclusions and generate figures for the paper:</p> <p><strong>"Many-body quantum sign structures as non-glassy Ising models"</strong><br> by Westerhout, T., Katsnelson, M. I., & Bagrov, A. A.</p> <p><em>Abstract:</em> The non-trivial phase structure of the eigenstates of many-body quantum systems severely limits the applicability of quantum Monte Carlo, variational, and machine learning methods. Here, we study real-valued signful ground-state wave functions of frustrated quantum spin systems and, assuming that the tasks of finding wave function amplitudes and signs can be separated, show that the signs can be easily bootstrapped from the amplitudes. We map the problem of finding the sign structure to an auxiliary classical Ising model defined on a subset of the Hilbert space basis. We show that the Ising model does not exhibit significant frustrations even for highly frustrated parental quantum systems, and is solvable with a fully deterministic O(K log K)-time combinatorial algorithm (where K is the Ising model size). Given the ground state amplitudes, we reconstruct the signs of the ground states of several frustrated quantum models, thereby revealing the hidden simplicity of many-body sign structures.</p>
Many-body machine learning models for water, acetonitrile, and methanol
<p><a href="https://keithgroup.github.io/mbGDML/">GDML</a>, <a href="https://libatoms.github.io/GAP/">GAP</a>, and <a href="https://schnetpack.readthedocs.io/en/stable/">SchNet</a> models trained on 1-, 2-, and 3-body energies and forces of water, acetonitrile, and methanol. Size-transferable <a href="https://github.com/mir-group/nequip">NequIPs</a> are trained on trimer data. Energies and forces were computed at the MP2/def2-TZVP level of theory in ORCA v4.2.0. Data sets, training scripts, and analyses of these potentials are available <a href="https://github.com/keithgroup/mbgdml-h2o-meoh-mecn">here</a>. Applications of these models on molecular dynamics simulations are found <a href="https://doi.org/10.5281/zenodo.7112198">here</a>.</p> <p><strong>Changelog</strong></p> <p>The format is based on <a href="https://keepachangelog.com/en/1.0.0/">Keep a Changelog</a>, and this project adheres to <a href="https://semver.org/spec/v2.0.0.html">Semantic Versioning.</a></p> <p>[0.0.2] - 2022-12-20</p> <p>Added</p> <ul> <li><a href="https://github.com/mir-group/nequip">NequIPs</a> trained for all solvents using 1000 trimers.</li> </ul> <p>[0.0.1] - 2022-09-25</p> <ul> <li>Initial release!</li> </ul> <p> </p>
Dataset for "Constructing many-body dissipative particle dynamics models of fluids from bottom-up coarse-graining"
<ul> <li>The trajectory files used for processing time correlation functions, as reported in the original paper, are provided here.</li> <li>The analysis tools can be found in the following GitHub repository: <a href="https://github.com/jaehyeokjin/ManyBodyDPD/tree/main/Time-Correlation" target="_new" rel="noopener">ManyBodyDPD/Time-Correlation</a>. These tools are designed to work with the two trajectory files included in this repository.</li> </ul>
Many-Body Models for Chirality-Induced Spin Selectivity in Electron Transfer. Open data set
<p>Data supporting the original figures 1, 2, 3 and 4 of the related publication.</p>
Accurate Modeling of Bromide and Iodide Hydration with Data-Driven Many-Body Potentials
<p>Ion–water interactions play a central role in determining the properties of aqueous systems in a wide range of environments. However, a quantitative understanding of how the hydration properties of ions evolve from small aqueous clusters to bulk solutions and interfaces remains elusive. Here, we introduce the second generation of data-driven many-body energy (MB-nrg) potential energy functions (PEFs) representing bromide–water and iodide–water interactions. The MB-nrg PEFs use permutationally invariant polynomials to reproduce two-body and three-body energies calculated at the coupled cluster level of theory, and implicitly represent all higher-body energies using classical many-body polarization. A systematic analysis of the hydration structure of small Br<sup>–</sup>(H<sub>2</sub>O)<sub><em>n</em></sub> and I<sup>–</sup>(H<sub>2</sub>O)<sub><em>n</em></sub> clusters demonstrates that the MB-nrg PEFs predict interaction energies in quantitative agreement with “gold standard” coupled cluster reference values. Importantly, when used in molecular dynamics simulations carried out in the isothermal–isobaric ensemble for single bromide and iodide ions in liquid water, the MB-nrg PEFs predict extended X-ray absorption fine structure (EXAFS) spectra that accurately reproduce the experimental spectra, which thus allows for characterizing the hydration structure of the two ions with a high level of confidence.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.