2026-05-26T14:57:01Z https://archive-dev.materialscloud.cscs.ch/oai2d

oai:materialscloud.org:2530 2025-01-16T15:21:34Z community-mcarchive openaire_data

Severini, Leonardo Zaccarelli, Emanuela Severini, Leonardo Tavagnacco, Letizia Sennato, Simona Celi, Erika Chiessi, Ester Mazzuca, Claudia Zaccarelli, Emanuela 2025-01-16 Polyelectrolyte complexes (PECs), formed via the self-assembly of oppositely charged polysaccharides, are highly valued for their biocompatibility, biodegradability, and hydrophilicity, offering significant potential for biotechnological applications. However, the complex nature and lack of insight at a molecular level into polyelectrolytes conformation and aggregation often hinders the possibility of achieving an optimal control of PEC systems, limiting their practical applications. To address this problem, an in-depth investigation of PECs microscopic structural organization is required. In this work, for the first time, a hybrid approach that combines experimental techniques with atomistic molecular dynamics simulations is used to elucidate, at a molecular level, the mechanisms underlying the aggregation and structural organization of complexes formed by gellan and chitosan, i.e. PECs commonly used in food technology. This combined analysis reveals a two-step complexation process: gellan initially self-assembles into a double-helix structure, subsequently surrounded and stabilized by chitosan via electrostatic interactions. Furthermore, these results show that complexation preserves the individual conformation and intrinsic functionality of both polyelectrolytes, thereby ensuring the efficacy of the PECs in biotechnological applications. application/zip text/markdown https://doi.org/10.24435/materialscloud:9s-eb oai:materialscloud.org:2530 mcid:2025.11 eng Materials Cloud https://doi.org/10.1016/j.ijbiomac.2024.139098 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:ct-85 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Polyelectrolyte complexes Polysaccharides Gellan Chitosan Gels Unveiling the self-assembly process of gellan-chitosan complexes through a combination of atomistic simulations and experiments info:eu-repo/semantics/other

oai:materialscloud.org:2137 2024-04-10T12:28:25Z community-mcarchive openaire_data

Gigli, Lorenzo Goscinski, Alexander Ceriotti, Michele Tribello, Gareth A. Gigli, Lorenzo Goscinski, Alexander Ceriotti, Michele Tribello, Gareth A. 2024-04-10 The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the system's finite size. Therefore, one is often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT, to perform simulations that do not suffer from finite size effects. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of Barium Titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, due to the presence of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations and the large size and time scales afforded by empirical models. application/zip text/markdown https://doi.org/10.24435/materialscloud:xw-g5 oai:materialscloud.org:2137 mcid:2024.54 eng Materials Cloud https://doi.org/10.48550/arXiv.2310.12579 https://doi.org/10.1103/PhysRevB.110.024101 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:75-zg info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Ferroelectrics Finite-size effects Metadynamics Phase transitions Machine Learning potentials hybrid-DFT ML Dielectric correlations MARVEL SNSF Sinergia Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling info:eu-repo/semantics/other

oai:materialscloud.org:343 2020-03-23T00:00:00Z community-mcarchive openaire_data

Huber, Sebastiaan P. Huber, Sebastiaan P. Zoupanos, Spyros Uhrin, Martin Talirz, Leopold Kahle, Leonid Häuselmann, Rico Gresch, Dominik Müller, Tiziano Yakutovich, Aliaksandr V. Andersen, Casper W. Ramirez, Francisco F. Adorf, Carl S. Gargiulo, Fernando Kumbhar, Snehal Passaro, Elsa Johnston, Conrad Merkys, Andrius Cepellotti, Andrea Mounet, Nicolas Marzari, Nicola Kozinsky, Boris Pizzi, Giovanni 2020-03-23 The ever-growing availability of computing power and sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (http://www.aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with any simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible. This archive record contains the data to reproduce the figures on engine performance in the section "Event versus polling-based engine" of the paper entitled "AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance". It also includes instructions to reproduce the actual data from scratch using AiiDA v1.1.1 and AiiDA v0.12.5. text/markdown application/octet-stream application/gzip text/markdown https://doi.org/10.24435/materialscloud:2020.0027/v1 oai:materialscloud.org:343 mcid:2020.0027/v1 eng Materials Cloud https://doi.org/10.1038/s41597-020-00638-4 https://renkulab.io/projects/new?data=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 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:st-ht info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode reproducibility workflows high-throughput automation MARVEL MaX SNSF PASC PRACE ERC Swissuniversities MarketPlace Intersect NFFA EMMC AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance info:eu-repo/semantics/other

oai:materialscloud.org:2282 2025-06-23T10:42:52Z community-mcarchive openaire_data

Zhang, Yao Chen, Han Zhang, Yao Li, Chunyan Zhao, Haiyan Yu, Zhongxun Tang, Xiaoan Zhang, Jixiang Chen, Zhenhua Zeng, Jianrong Zhang, Peng Han, Liyuan Chen, Han 2024-08-05 Tin-lead halide perovskites with a bandgap near 1.2 electron-volt hold great promise for thin-film photovoltaics. However, the film quality of solution-processed Sn-Pb perovskites is compromised by the asynchronous crystallization behavior between Sn and Pb components, where the crystallization of Sn-based perovskites tends to occur faster than that of Pb. Here we show that the rapid crystallization of Sn is rooted in its stereochemically active lone pair, which impedes coordination between the metal ion and Lewis base ligands in the perovskite precursor. From this perspective, we introduce a noncovalent binding agent targeting the open metal site of coordinatively unsaturated Sn(II) solvates, thereby synchronizing crystallization kinetics and homogenizing Sn-Pb alloying. The resultant single-junction Sn-Pb perovskite solar cells achieve a certified power conversion efficiency of 24.13 per cent. The encapsulated device retains 90 per cent of the initial efficiency after 795 hours of maximum power point operation under simulated one-sun illumination. We conducted DFT and AIMD simulations to study the structure and interactions of metal iodide-solvent ligand complexes. This archive contains the optimized atomic coordinates of PbI2/SnI2-ligands complexes and the initial and final configurations of AIMD trajectories. text/markdown text/plain application/zip https://doi.org/10.24435/materialscloud:kv-g1 oai:materialscloud.org:2282 mcid:2024.115 eng Materials Cloud https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:z6-2j info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode halide perovskite lead iodide tin iodide Synchronized crystallization in tin-lead perovskite solar cells info:eu-repo/semantics/other

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

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:2107 2024-03-11T11:34:12Z community-mcarchive openaire_data

Xu, Xinhang Qi, Chongchong Aretxabaleta, Xabier M. Ma, Chundi Spagnoli, Dino Manzano, Hegoi Xu, Xinhang Qi, Chongchong Aretxabaleta, Xabier M. Ma, Chundi Spagnoli, Dino Manzano, Hegoi 2024-03-11 Cement hydration is crucial for the strength development of cement-based materials; however, the mechanism that underlies this complex reaction remains poorly understood at the molecular level. An in-depth understanding of cement hydration is required for the development of environmentally friendly cement and consequently the reduction of carbon emissions in the cement industry. Here, we use molecular dynamics simulations with a reactive force field to investigate the initial hydration processes of tricalcium silicate (C₃S) and dicalcium silicate (C₂S) up to 40 ns. Our simulations provide theoretical support for the rapid initial hydration of C₃S compared to C₂S at the molecular level. The dissolution pathways of calcium ions in C₃S and C₂S are revealed, showing that, two dissolution processes are required for the complete dissolution of calcium ions in C₃S. Our findings promote the understanding of the calcium dissolution stage and serve as a valuable reference for the investigation of the initial cement hydration. application/vnd.rar text/plain text/markdown https://doi.org/10.24435/materialscloud:sj-db oai:materialscloud.org:2107 mcid:2024.45 eng Materials Cloud https://doi.org/10.1038/s41467-024-46962-w https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:ff-c1 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Cement hydration molecular dynamics simulation ReaxFF reactive force field The initial stages of cement hydration at the molecular level info:eu-repo/semantics/other

oai:materialscloud.org:2050 2024-02-08T17:04:07Z community-mcarchive openaire_data

Kinikar, Amogh Xu, Xiushang Kinikar, Amogh Di Giovannantonio, Marco Pignedoli, Carlo Antonio Ruffieux, Pascal Müllen, Klaus Fasel, Roman Narita, Akimitsu 2024-02-08 On-surface synthesis has emerged as a powerful strategy to fabricate unprecedented forms of atomically precise graphene nanoribbons (GNRs). However, the on-surface synthesis of zigzag GNRs (ZGNR) has met with only limited success. In the paper where the data are discussed, we report the synthesis and on-surface reactions of 2,7-dibromo-9,9'-bianthryl as the precursor towards π-extended ZGNRs. Characterization by scanning tunneling microscopy and high-resolution noncontact atomic force microscopy clearly demonstrated the formation of anthracene-fused ZGNRs. Unique skeletal rearrangements were also observed, which could be explained by intramolecular Diels-Alder cycloaddition. Theoretical calculations of the electronic properties of the anthracene-fused ZGNRs revealed spin-polarized edge-states and a narrow bandgap of 0.20 eV. application/octet-stream application/gzip application/octet-stream text/markdown https://doi.org/10.24435/materialscloud:fq-nq oai:materialscloud.org:2050 mcid:2024.23 eng Materials Cloud https://doi.org/10.1021/prechem.3c00116 https://renkulab.io/projects/new?data=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 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:4v-mb info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode MARVEL on surface synthesis graphene nanoribbons On-surface synthesis of anthracene-fused zigzag graphene nanoribbons from 2,7-dibromo-9,9'-bianthryl reveals unexpected ring rearrangements info:eu-repo/semantics/other

oai:materialscloud.org:1906 2023-09-19T11:11:36Z community-mcarchive openaire_data

Lustemberg, Pablo G. Koller, Volkmar Lustemberg, Pablo G. Spriewald-Luciano, Alexander Gericke, Sabrina M. Larsson, Alfred Sack, Christian Preobrajenski, Alexei Lundgren, Edvin Ganduglia-Pirovano, M. Veronica Over, Herbert 2023-09-19 The catalytic oxidation of HCl by molecular oxygen (Deacon process) over ceria allows the recovery of molecular chlorine from omnipresent HCl waste produced in various industrial processes. In previous density functional theory (DFT) model calculations by Amrute et al. [J. Catal. 2012, 286, 287–297.], it was proposed that the most critical reaction step in this process is the displacement of tightly bound chlorine at a vacant oxygen position on the CeO2(111) surface (Clvac) toward a less strongly bound cerium on-top (Cltop) position. This step is highly endothermic by more than 2 eV. On the basis of a dedicated model study, namely the re-oxidation of a chlorinated single crystalline Clvac-CeO2−x(111)-(√3 × √3)R30° surface structure, we provide in-situ synchrotron-based spectroscopic data (high-resolution core level spectroscopy (HRCLS) and X-ray adsorption near edge structure (XANES)) for this oxygen-induced de-chlorination process. Combined with theoretical evidence from DFT calculations, the Clvac → Cltop displacement reaction is predicted to be induced by an adsorbed peroxo species (O22-), making the displacement step concerted and exothermic by only 0.6 eV with an activation barrier of only 1.04 eV. The peroxo species is shown to be important for the re-oxidation of Clvac-CeO2−x(111) and is considered essential for understanding the function of ceria in oxidation catalysis. application/zip application/zip application/zip application/zip application/zip application/zip application/zip application/zip application/zip text/markdown text/plain https://doi.org/10.24435/materialscloud:dq-1c oai:materialscloud.org:1906 mcid:2023.143 eng Materials Cloud https://doi.org/10.1021/acscatal.3c03222 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:br-33 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Deacon process reduced ceria peroxo surface species displacement of strongly adsorbed chlorine oxygen-induced de-chlorination process Critical step in the HCl oxidation reaction over single-crystalline CeO2−x(111): Peroxo-induced site change of strongly adsorbed surface chlorine info:eu-repo/semantics/other

oai:materialscloud.org:532 2020-09-25T18:04:55Z community-mcarchive openaire_data

Ruan, Xiulin Li, Xiangyu Peoples, Joseph Huang, Zhifeng Zhao, Zixuan Qiu, Jun Ruan, Xiulin 2020-09-25 Radiative cooling is a passive cooling technology by reflecting sunlight and emitting radiation in the sky window. Although highly desired, full daytime sub-ambient radiative cooling in commercial-like single-layer particle-matrix paints is yet to be achieved. Here we demonstrate full daytime sub-ambient radiative cooling in CaCO3-acrylic paint by utilizing the large bandgap CaCO3 fillers, a high particle concentration of 60% and a broad size distribution. Our paint shows high solar reflectance of 95.5% and high normal emissivity of 0.94 in the sky window. Field tests show cooling power exceeding 37W/m2 and surface temperature more than 1.7˚C below ambient at noon. A figure of merit RC is proposed to compare the cooling performance independent of weather conditions. The standard RC of our paint is 0.49, among the best radiative cooling performance while offering unprecedented benefits of the convenient paint form, low cost, and the compatibility with commercial paint fabrication process. application/zip text/plain text/markdown https://doi.org/10.24435/materialscloud:6c-my oai:materialscloud.org:532 mcid:2020.111 eng Materials Cloud https://doi.org/10.1016/j.xcrp.2020.100221 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:vj-4t info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Daytime radiative cooling Atmospheric sky window Particle-matrix paint Figure of merit Monte Carlo simulation Lorentz-Mie theory Full daytime sub-ambient radiative cooling in commercial-like paints with high figure of merit info:eu-repo/semantics/other

oai:materialscloud.org:f7sj2-fjr66 2025-10-09T08:46:18Z community-mcarchive openaire_data

Goswami, Rohit Goswami, Rohit Jónsson, Hannes 2025-10-09 <p>Gaussian process (GP) regression provides a strategy for accelerating saddle point searches on high-dimensional energy surfaces by reducing the number of times the energy and its derivatives with respect to atomic coordinates need to be evaluated. The computational overhead in the hyperparameter optimization can, however, be large and make the approach inefficient. Failures can also occur if the search ventures too far into regions that are not represented well enough by the GP model. Here, these challenges are resolved by using geometry-aware optimal transport measures and an active pruning strategy using a summation over Wasserstein-1 distances for each atom-type in farthest-point sampling, selecting a fixed-size subset of geometrically diverse configurations to avoid rapidly increasing cost of GP updates as more observations are made. Stability is enhanced by permutation-invariant metric that provides a reliable trust radius for early-stopping and a logarithmic barrier penalty for the growth of the signal variance. These physically motivated algorithmic changes prove their efficacy by reducing to less than a half the mean computational time on a set of 238 challenging configurations from a previously published data set of chemical reactions. With these improvements, the GP approach is established as a robust and scalable algorithm for accelerating saddle point searches when the evaluation of the energy and atomic forces requires significant computational effort.</p> <p> </p> <p>This record contains the complete traces of dimer saddle search runs with the OT-GP (optimal transport GP) framework. This includes STDOUT and HDF5 trajectories. The record is a companion to the code in the associated GitHub repository and can be used to regenerate the figures and validate the analysis in the accompanying manuscript.</p> application/x-xz text/markdown application/x-xz https://doi.org/10.24435/materialscloud:rh-tw oai:materialscloud.org:f7sj2-fjr66 mcid:2025.153 eng Materials Cloud https://github.com/theochemUI/otgpd_repro https://doi.org/10.48550/arXiv.2510.06030 https://archive-dev.materialscloud.cscs.ch/communities/mcarchive https://doi.org/10.24435/materialscloud:7t-f1 info:eu-repo/semantics/openAccess Materials Cloud non-exclusive license to distribute v1.0 https://www.materialscloud.org/licenses/nonexclusive-distrib/1.0 saddle-search transition-state gpr machine learning Adaptive pruning for increased robustness and reduced computational overhead in Gaussian process accelerated saddle point searches info:eu-repo/semantics/other .eJwtzMEKgjAYAOB3-c8S27TmvBUxI6JM2nReYrpl4kRRoyB69zr0PcD3hslaAxFa0JAG4SrEjFLk05B5MOjaQkQ8aJ96rCeI3tDZWRs962S0t-YFEfS6uZoKPh5M1dg7d21-GfDYPRR54SKWN0XYw8QyMJxhk7l2m3Nc5PvlOVu2aSeRIvWGOycvYi7MboOEZEnpH_sS3bcVmYJzXq3_TjYtkELDobykSen28SGbOyX4oLEiQhx9-HwBEGtA7Q.ahW0vg.coAZnPbHLOB7k3RmuBnMqGNFmT0