2026-11-05T02:24:38Z https://archive-dev.materialscloud.cscs.ch/oai2d

oai:materialscloud.org:49 2019-01-04T00:00:00Z openaire_data community-mcarchive

Smit, Berend Moosavi, Seyed Mohamad Chidambaram, Arunraj Talirz, Leopold Haranczyk, Maciej Stylianou, Kyriakos C. Smit, Berend 2019-01-04 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 highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials. video/mp4 text/csv text/markdown https://doi.org/10.24435/materialscloud:2018.0011/v3 oai:materialscloud.org:49 mcid:2018.0011/v3 eng Materials Cloud https://doi.org/10.5281/zenodo.2531564 https://www.materialscloud.org/work/tools/sycofinder https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:76-52 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Machine learning Synthesis Optimisation Genetic algorithms Metal-Organic frameworks Robotic synthesi MARVEL Capturing chemical intuition in synthesis of metal-organic frameworks info:eu-repo/semantics/other

oai:materialscloud.org:113 2019-04-17T00:00:00Z openaire_data community-mcarchive

Spiwok, Vojtech Šućur, Zoran Kříž, Pavel Spiwok, Vojtech Šućur, Zoran Kříž, Pavel 2019-04-17 Biomolecular simulations have a great potential in protein engineering, drug discovery and many other fields. Unfortunately, this method is computationally expensive, so many interesting processes cannot be routinely studied. In order to address this problem we developed Flying Gaussian method [Journal of Chemical Theory and Computation 12, 4644-4650 (2016)]. This method simultaneously simulates multiple replicas of the studied system and disfavor replicas with similar structures by artificial bias potential. The question arises how to calculate an unbiased free energy surface from a biased simulation. This dataset demonstrates together with mathematical arguments supports application of Umbrella Sampling reweighing method, despite the fact that this method is designed for methods with a time-independent bias potential. application/zip text/plain text/markdown https://doi.org/10.24435/materialscloud:2019.0013/v1 oai:materialscloud.org:113 mcid:2019.0013/v1 eng Materials Cloud https://doi.org/10.1021/acs.jpcb.7b09337 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:vq-vf info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode metadynamics flying Gaussian method molecular dynamics simulation sampling Free-Energy Surface Prediction by Flying Gaussian Method: Numerical Proof info:eu-repo/semantics/other

oai:materialscloud.org:2 2017-03-14T00:00:00Z openaire_data community-mcarchive

Smit, Berend Lee, Yongjin Barthel, Senja D. Dłotko, Paweł Moosavi, S. Mohamad Hess, Kathryn Smit, Berend 2017-03-14 In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. Recently, we developed a pore recognition approach to quantify similarity of pore structures using topological data analysis. Barcodes generated with using this approach allow us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. This database has barcodes for zeolites, metal organic frameworks, and zeolitic imidazolate frameworks. application/x-bzip2 application/x-bzip2 text/markdown https://doi.org/10.24435/materialscloud:2017.0001/v1 oai:materialscloud.org:2 mcid:2017.0001/v1 eng Materials Cloud https://arxiv.org/abs/1701.06953 https://doi.org/10.1038/ncomms15396 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:en-ze info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode zeolites metal organic frameworks nanoporous materials topological data analysis persistence homology MARVEL Barcodes for nanoporous materials info:eu-repo/semantics/other

oai:materialscloud.org:2252 2024-07-05T10:21:26Z openaire_data community-mcarchive

Chen, Shuonan Bai*, Yongtao Zhou*, Xuhong Chen, Shuonan Bai*, Yongtao Zhou*, Xuhong Yang, Ao 2024-07-05 In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability. application/zip text/markdown https://doi.org/10.24435/materialscloud:wt-98 oai:materialscloud.org:2252 mcid:2024.105 eng Materials Cloud https://doi.org/10.1111/j.1460-2695.2004.00740.x https://doi.org/10.1016/j.ijfatigue.2005.05.014 https://doi.org/10.1016/j.ijfatigue.2019.105202 https://doi.org/10.1016/j.ijfatigue.2019.01.003 https://ntrs.nasa.gov/citations/19880015632 https://doi.org/10.1016/j.ijpvp.2021.104393 https://doi.org/10.1016/j.ijfatigue.2021.106174 https://doi.org/10.1016/j.ijfatigue.2007.07.005 https://doi.org/10.1016/j.ijfatigue.2017.03.042 https://doi.org/10.1515/mt-2022-0172 https://doi.org/10.1111/j.1460-2695.1993.tb00115.x https://doi.org/10.1016/S1566-1369(03)80006-7 https://doi.org/10.1016/j.ijfatigue.2005.07.007 https://doi.org/10.1016/j.ijfatigue.2020.105818 https://doi.org/10.1016/j.ijfatigue.2013.08.028 https://doi.org/10.1016/j.ijfatigue.2010.09.020 https://doi.org/10.1016/j.msea.2012.03.039 https://doi.org/10.1111/ffe.13048 https://doi.org/10.1016/j.ijfatigue.2003.11.005 https://doi.org/10.1115/1.2812377 https://doi.org/10.7666/d.d167264 https://doi.org/10.7666/d.d167264 https://doi.org/10.7666/d.Y2407972 https://doi.org/10.7666/d.Y2407972 https://doi.org/10.7666/d.Y2887183 https://doi.org/10.7666/d.Y2887183 https://doi.org/10.1016/j.ijfatigue.2015.12.002 https://book.sciencereading.cn/shop/book/Booksimple/show.do?id=BF47DB6BB813C41F4B9C8626A594DAAF2000 https://doi.org/10.1016/j.ijfatigue.2006.10.028 https://doi.org/10.1115/1.3008041 https://doi.org/10.1111/j.1460-2695.1988.tb01169.x https://doi.org/10.1520/STP36242S https://doi.org/10.1016/S0142-1123(02)00013-0 https://doi.org/10.1016/j.ijfatigue.2017.04.011 https://doi.org/10.1016/j.msea.2006.06.014 https://doi.org/10.1115/1.2804529 https://doi.org/10.1115/1.3225707 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:ar-nw info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Metal fatigue Multiaxial fatigue Deep learning Fatigue life prediction A deep learning dataset for metal multiaxial fatigue life prediction info:eu-repo/semantics/other

oai:materialscloud.org:25 2018-02-11T00:00:00Z openaire_data community-mcarchive

Nguyen, Ngoc Linh Marzari, Nicola Nguyen, Ngoc Linh Baletto, Francesca Marzari, Nicola 2018-02-11 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 absence of the adatom, the surface instead remains crystalline until reaching the bulk melting temperature. application/zip application/zip text/markdown https://doi.org/10.24435/materialscloud:2018.0002/v1 oai:materialscloud.org:25 mcid:2018.0002/v1 eng Materials Cloud https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:3t-va info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode First-principles molecular dynamics Aluminum Surface melting Defects MARVEL Adatom-Induced Local Melting info:eu-repo/semantics/other

oai:materialscloud.org:50 2019-03-03T00:00:00Z openaire_data community-mcarchive

Smit, Berend Moosavi, Seyed Mohamad Chidambaram, Arunraj Talirz, Leopold Haranczyk, Maciej Stylianou, Kyriakos C. Smit, Berend 2019-03-03 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 highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials. video/mp4 text/csv text/csv text/markdown https://doi.org/10.24435/materialscloud:2018.0011/v4 oai:materialscloud.org:50 mcid:2018.0011/v4 eng Materials Cloud https://doi.org/10.5281/zenodo.2531564 https://www.materialscloud.org/work/tools/sycofinder https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:76-52 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Machine learning Synthesis Optimisation Genetic algorithms Metal-Organic frameworks Robotic synthesi MARVEL Capturing chemical intuition in synthesis of metal-organic frameworks info:eu-repo/semantics/other

oai:materialscloud.org:41 2018-05-15T00:00:00Z openaire_data community-mcarchive

Smit, Berend Haranczyk, Maciej Martin, Richard L. Simon, Cory M. Smit, Berend Haranczyk, Maciej 2018-05-15 Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal–organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure–property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane–methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage. application/gzip text/markdown https://doi.org/10.24435/materialscloud:2018.0008/v1 oai:materialscloud.org:41 mcid:2018.0008/v1 eng Materials Cloud https://doi.org/10.1021/ja4123939 https://pubs.acs.org/doi/abs/10.1021/ja4123939 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:pj-6m info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode 3D three-dimensional database high-throughput porous polymer networks PPN nanoporous methane storage deliverable capacities DC grand canonical Monte Carlo GCMC In Silico Design of Porous Polymer Networks: High Throughput Screening for Methane Storage Materials info:eu-repo/semantics/other

oai:materialscloud.org:2395 2024-10-10T11:29:34Z openaire_data community-mcarchive

Chen, Shuonan Bai*, Yongtao Zhou*, Xuhong Chen, Shuonan Bai*, Yongtao Zhou*, Xuhong Yang, Ao 2024-10-10 In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability. application/zip text/markdown https://doi.org/10.24435/materialscloud:2d-bz oai:materialscloud.org:2395 mcid:2024.155 eng Materials Cloud https://doi.org/10.1111/j.1460-2695.2004.00740.x https://doi.org/10.1016/j.ijfatigue.2005.05.014 https://doi.org/10.1016/j.ijfatigue.2019.105202 https://doi.org/10.1016/j.ijfatigue.2019.01.003 https://ntrs.nasa.gov/citations/19880015632 https://doi.org/10.1016/j.ijpvp.2021.104393 https://doi.org/10.1016/j.ijfatigue.2021.106174 https://doi.org/10.1016/j.ijfatigue.2007.07.005 https://doi.org/10.1016/j.ijfatigue.2017.03.042 https://doi.org/10.1515/mt-2022-0172 https://doi.org/10.1111/j.1460-2695.1993.tb00115.x https://doi.org/10.1016/S1566-1369(03)80006-7 https://doi.org/10.1016/j.ijfatigue.2005.07.007 https://doi.org/10.1016/j.ijfatigue.2020.105818 https://doi.org/10.1016/j.ijfatigue.2013.08.028 https://doi.org/10.1016/j.ijfatigue.2010.09.020 https://doi.org/10.1016/j.msea.2012.03.039 https://doi.org/10.1111/ffe.13048 https://doi.org/10.1016/j.ijfatigue.2003.11.005 https://doi.org/10.1115/1.2812377 https://doi.org/10.7666/d.d167264 https://doi.org/10.7666/d.d167264 https://doi.org/10.7666/d.Y2407972 https://doi.org/10.7666/d.Y2407972 https://doi.org/10.7666/d.Y2887183 https://doi.org/10.7666/d.Y2887183 https://doi.org/10.1016/j.ijfatigue.2015.12.002 https://doi.org/10.1016/j.ijfatigue.2006.10.028 https://doi.org/10.1115/1.3008041 https://doi.org/10.1111/j.1460-2695.1988.tb01169.x https://doi.org/10.1520/STP36242S https://doi.org/10.1016/S0142-1123(02)00013-0 https://doi.org/10.1016/j.ijfatigue.2017.04.011 https://doi.org/10.1016/j.msea.2006.06.014 https://doi.org/10.1115/1.2804529 https://doi.org/10.1115/1.3225707 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:ar-nw info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Metal fatigue Multiaxial fatigue Deep learning Fatigue life prediction A deep learning dataset for metal multiaxial fatigue life prediction info:eu-repo/semantics/other

oai:materialscloud.org:132 2019-05-17T00:00:00Z openaire_data community-mcarchive

Hozzova, Jana Krenek, Ales Spiwok, Vojtech Hozzova, Jana Krenek, Ales Simkova, Maria Spiwok, Vojtech 2019-05-17 Biomolecular simulations are computationally expensive. This limits their application in protein folding simulations, protein engineering, drug design and related fields. Enhanced sampling techniques such as metadynamics accelerates slow events in molecular simulation. This and other method apply artificial forces in directions of collective degrees of freedom (aka collective variables). Path collective variables and Property Map collective variables are defined using a series of reference structures of the studied molecular system. They require a huge number of mean square deviation calculations along the simulation. Close Structure algorithm reduces the number of these operations. application/zip text/plain text/markdown https://doi.org/10.24435/materialscloud:2019.0021/v1 oai:materialscloud.org:132 mcid:2019.0021/v1 eng Materials Cloud https://doi.org/10.1063/1.4978296 https://arxiv.org/abs/1801.02362v1 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:yg-cm info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode metadynamics collective variable mean square deviation Reducing the Number of Mean-Square Deviation Calculations with Floating Close Structure in Metadynamics info:eu-repo/semantics/other

oai:materialscloud.org:169 2019-07-02T00:00:00Z openaire_data community-mcarchive

Fang, Yue-Wen Chen, Hanghui Fang, Yue-Wen Chen, Hanghui 2019-07-02 Project abstract: Frustrated magnets are one class of fascinating materials that host many intriguing phases such as spin ice, spin liquid and complex long-range magnetic orderings at low temperatures. In this work we use first-principles calculations to find that in a wide range of magnetically frustrated oxides, at zero temperature a number of non-collinear magnetic orderings are more stable than the type-I collinear ordering that is observed at finite temperatures. The emergence of non-collinear orderings in those complex oxides is due to higher-order exchange interactions that originate from second-row and third-row transition metal elements. This implies a collinear-to-noncollinear spin transition at sufficiently low temperatures in those frustrated complex oxides. Furthermore, we find that in a particular oxide Ba2YOsO6, experimentally feasible uniaxial strain can tune the material between two different non-collinear magnetic orderings. Our work predicts new non- collinear magnetic orderings in frustrated complex oxides at very low temperatures and provides a mechanical route to tuning complex non-collinear magnetic orderings in those materials. About this entry: We provide the input files of our DFT calculations for the studied complex oxides. The structures in POSCAR format and the INCAR files for all stabilized magnetic orderings in our study are all included. These files can be directly used into DFT calculations with VASP. application/octet-stream text/markdown https://doi.org/10.24435/materialscloud:2019.0036/v1 oai:materialscloud.org:169 mcid:2019.0036/v1 eng Materials Cloud https://doi.org/10.1088/1361-648X/ab31e0 https://iopscience.iop.org/article/10.1088/1361-648X/ab31e0 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive/ https://doi.org/10.24435/materialscloud:v9-6s info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode double perovskite non-collinear magnetic ordering spin-orbital coupling first-principles calculations frustrated complex oxides The complex non-collinear magnetic orderings in Ba2YOsO6: A new approach to tuning spin-lattice interactions and controlling magnetic orderings in frustrated complex oxides info:eu-repo/semantics/other .eJwljM2OgjAYAN_lOxvTllZXbv5Q1I3rQtwP6cVUWhEo0QCuZo3vLsnOZeY0T2itNeCT4eiDe1zwMfcI9ThnA7jq3ILfR3XXTd6C_4TadtroTn839lQ8wIeLLg4mg9cA2qy5OHco-hnI0N1S9qAqxFPKJjcTIjdyQk3iqsVeUrVfiygRVVwjSVk-k27V_dTnLVK1xWUskODo-IcBLtblVxVM_5l_3s_qN6ojtiMmMjLe9Z5ngZCbRJW6dDN4vQH3l0D-.auvo5w.gDUDtZ7x-R3wv74MQZza0_xLgHE