2026-09-23T14:17:13Z https://materialscloud-archive-failover.cineca.it/oai2d

oai:materialscloud.org:0958j-phx20 2025-07-31T20:14:24Z openaire_data community-mcarchive

de Souza Ferrari, Brenda Giro, Ronaldo B. Steiner, Mathias de Souza Ferrari, Brenda Giro, Ronaldo B. Steiner, Mathias 2025-07-31 Polymers are versatile materials with a wide range of applications. The improvement of polymer properties rises the importance on the way that the repeating units are connected (head-to-tail,head-to-head,tail-to-tail) to build the polymer structure since it directly influences the morphology, chain topology and consequently its properties. Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymer. One key enabling factor for the essential foundations for Polymer Informatics is the machine-readable polymer representation. Polymer have been represented in a string format with special characters used to tag the head and tail positions indicating where the linking bond happens between repeat units. Available tools to assign the head and tail position limits its applicability in a broad sense. In this work we show a new tool to assign the head and tail atoms for a given monomer. From a database of 206 polymer precursors curated from the literature, our algorithm correctly predicted the class of 201 data points, which represents 97.6% of accuracy and regarding the the head and tail assignment, correctly assigned the positions for 188 data points, which translates to 91.3% of accuracy. text/markdown text/csv text/csv text/csv https://doi.org/10.24435/materialscloud:2x-9j oai:materialscloud.org:0958j-phx20 mcid:2025.120 eng Materials Cloud https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:n3-c0 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode polymer repeat unit head and tail atom position HTA - An open-source software for assigning heads and tails to SMILES in polymerization reactions info:eu-repo/semantics/other

oai:materialscloud.org:1896 2025-01-10T10:23:14Z openaire_data community-mcarchive

de Souza Ferrari, Brenda Giro, Ronaldo B. Steiner, Mathias de Souza Ferrari, Brenda Giro, Ronaldo B. Steiner, Mathias 2025-01-10 Polymers are versatile materials with a wide range of applications. The improvement of polymer properties rises the importance on the way that the repeating units are connected (head-to-tail,head-to-head,tail-to-tail) to build the polymer structure since it directly influences the morphology, chain topology and consequently its properties. Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymer. One key enabling factor for the essential foundations for Polymer Informatics is the machine-readable polymer representation. Polymer have been represented in a string format with special characters used to tag the head and tail positions indicating where the linking bond happens between repeat units. Available tools to assign the head and tail position limits its applicability in a broad sense. In this work we show a new tool to assign the head and tail atoms for a given monomer. From a database of 206 polymer precursors curated from the literature, our algorithm correctly predicted the class of 201 data points, which represents 97.6% of accuracy and regarding the the head and tail assignment, correctly assigned the positions for 188 data points, which translates to 91.3% of accuracy. text/csv text/csv text/markdown https://doi.org/10.24435/materialscloud:tx-b9 oai:materialscloud.org:1896 mcid:2025.6 eng Materials Cloud https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:n3-c0 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode License addendum polymer repeat unit head and tail atom position HTA - An open-source software for assigning heads and tails to SMILES in polymerization reactions info:eu-repo/semantics/other

oai:materialscloud.org:ybgqt-ewh55 2026-01-12T15:42:06Z openaire_data community-mcarchive

Cajahuaringa, Samuel Cajahuaringa, Samuel Bidoggia, Davide Peressi, Maria Marrazzo, Antimo 2026-01-12 In rarefied atmospheric environments, where continuum fluid dynamics breaks down, aerodynamic drag is governed by gas–surface momentum exchange, making surface structure and chemistry key design knobs. Using molecular dynamics simulations, we show that coating the α-Al2O3(0001) surface with graphene markedly reduces the tangential momentum accommodation coefficient (TMAC) of N2, shifting scattering toward more specular reflection and thereby lowering drag; we further benchmark this response against graphite. The reduction is robust and strengthens up to 900 K. While structural defects can increase TMAC – and hence drag – via defect-induced corrugation and local atomic and electronic rearrangements, graphene retains near-ideal low-TMAC performance at experimentally relevant defect densities. These results identify graphene as a thermally resilient, low-drag coating for aerospace-relevant surfaces in rarefied flows. text/x-python application/gzip text/markdown application/gzip text/markdown application/gzip application/gzip text/plain https://doi.org/10.24435/materialscloud:cq-rs oai:materialscloud.org:ybgqt-ewh55 mcid:2026.18 eng Materials Cloud https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:f2-nn info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode rarefied gas air drag accommodation coefficients molecular dynamics alumina graphene graphite coating defects Defects, corrugation and temperature govern rarefied-air drag on graphene coatings info:eu-repo/semantics/other

oai:materialscloud.org:k1p8d-hse97 2026-04-24T13:44:14Z openaire_data community-mcarchive

Cajahuaringa, Samuel Cajahuaringa, Samuel Bidoggia, Davide Peressi, Maria Marrazzo, Antimo 2026-04-24 In rarefied atmospheric environments, where continuum fluid dynamics breaks down, aerodynamic drag is governed by gas–surface momentum exchange, making surface structure and chemistry key design knobs. Using molecular dynamics simulations, we show that coating the α-Al2O3(0001) surface with graphene markedly reduces the tangential momentum accommodation coefficient (TMAC) of N2, shifting scattering toward more specular reflection and thereby lowering drag; we further benchmark this response against graphite. The reduction is robust and strengthens up to 900 K. While structural defects can increase TMAC – and hence drag – via defect-induced corrugation and local atomic and electronic rearrangements, graphene retains near-ideal low-TMAC performance at experimentally relevant defect densities. These results identify graphene as a thermally resilient, low-drag coating for aerospace-relevant surfaces in rarefied flows. text/x-python text/plain text/markdown text/markdown application/gzip application/gzip https://doi.org/10.24435/materialscloud:2h-f5 oai:materialscloud.org:k1p8d-hse97 mcid:2026.88 eng Materials Cloud https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:f2-nn info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode rarefied gas air drag accommodation coefficients molecular dynamics alumina graphene graphite coating defects Defects, corrugation and temperature govern rarefied-air drag on graphene coatings info:eu-repo/semantics/other

oai:materialscloud.org:evx55-35x98 2026-01-05T19:11:05Z openaire_data community-mcarchive

Loche, Philip Ceriotti, Michele Schlaich, Alexander Stärk, Philipp Loche, Philip Langer, Marcel Stoß, Henrik Rumiantsev, Egor Ceriotti, Michele Schlaich, Alexander 2026-01-05 Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static charges, that participate in Coulomb interactions between atoms, and dynamic charges such as atomic polar tensors - aka Born effective charges - describing the response to an external electric field. We critically compare different approaches to learn both types of charges, taking bulk water and water clusters as paradigmatic examples: (1) Learning them independently; (2) Coupling static and dynamic charges based on their physical relationship with a single global coupling constant to account for dielectric screening; (3) Coupled learning with a local, environment-dependent screening factor. In the coupled case, correcting for dielectric screening is essential, yet the common assumption of homogeneous, isotropic screening breaks down in heterogeneous systems such as water clusters. A learned, environment-dependent screening restores high accuracy for the dynamical charges. However, the accuracy gain over independent dynamic predictions is negligible, while the computational cost increases compared to using separate models for static and dynamical charges. This suggests that, despite the formal connection between the two charge types, modeling them independently is the more practical choice for both condensed-phase and isolated cluster systems. chemical/x-xyz application/gzip chemical/x-xyz text/markdown chemical/x-xyz https://doi.org/10.24435/materialscloud:nc-fa oai:materialscloud.org:evx55-35x98 mcid:2026.5 eng Materials Cloud https://arxiv.org/abs/XXXX.XXXXX https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:fs-8h info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode machine-learning interatomic-potentials density-functional theory atomistic-simulations born-effective-charges long-range-models Simultaneous learning of static and dynamic charges info:eu-repo/semantics/other

oai:materialscloud.org:zd984-2ja12 2026-01-13T11:25:13Z openaire_data community-mcarchive

Schlaich, Alexander Ceriotti, Michele Loche, Philip Stärk, Philipp Stoß, Henrik Langer, Marcel F. Rumiantsev, Egor Schlaich, Alexander Ceriotti, Michele Loche, Philip 2026-01-08 Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static charges, that participate in Coulomb interactions between atoms, and dynamic charges such as atomic polar tensors - aka Born effective charges - describing the response to an external electric field. We critically compare different approaches to learn both types of charges, taking bulk water and water clusters as paradigmatic examples: (1) Learning them independently; (2) Coupling static and dynamic charges based on their physical relationship with a single global coupling constant to account for dielectric screening; (3) Coupled learning with a local, environment-dependent screening factor. In the coupled case, correcting for dielectric screening is essential, yet the common assumption of homogeneous, isotropic screening breaks down in heterogeneous systems such as water clusters. A learned, environment-dependent screening restores high accuracy for the dynamical charges. However, the accuracy gain over independent dynamic predictions is negligible, while the computational cost increases compared to using separate models for static and dynamical charges. This suggests that, despite the formal connection between the two charge types, modeling them independently is the more practical choice for both condensed-phase and isolated cluster systems. application/gzip chemical/x-xyz chemical/x-xyz chemical/x-xyz chemical/x-xyz text/markdown https://doi.org/10.24435/materialscloud:f8-1n oai:materialscloud.org:zd984-2ja12 mcid:2026.11 eng Materials Cloud https://doi.org/10.48550/arXiv.2601.03656 https://chemiscope.materialscloud.io/?load=https%3A%2F%2Farchive.materialscloud.org%2Fapi%2Frecords%2Fzd984-2ja12%2Ffiles%2Fclusters_val.chemiscope.json.gz%2Fcontent%3Ffilename%3Dclusters_val.chemiscope.json.gz%26materials_cloud_doi%3D10.24435%2Fmaterialscloud%3Af8-1n https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:fs-8h info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode machine-learning interatomic-potentials density-functional theory atomistic-simulations born-effective-charges long-range-models Simultaneous learning of static and dynamic charges info:eu-repo/semantics/other

oai:materialscloud.org:02rcd-nc341 2026-05-08T12:49:27Z openaire_data community-mcarchive

Bouquiaux, Julien Poncé, Samuel Jia, Yongchao Mikami, Masayoshi Gonze, Xavier 2026-05-08 The precise atomic-scale structure around Eu2+ activators in the β-Si6-zAlzOzN8-z:Eu2+ commercial green phosphor remains elusive. We use the first-principles ΔSCF excited-state method, embedding of the interatomic force constants for supercells up to 3501 atoms, and Huang-Rhys theory to clarify this issue. Monte Carlo exploration is used to identify representative low-energy structural models spanning different levels of Al/O concentration z.  For the lowest-energy structure at low z, our computed photoluminescence spectrum reproduces the experimental vibronic peaks at 6K with excellent agreement in peak positions and intensities, validating the Eu-N9 coordination model with Al, O, and Eu confined to the same crystallographic plane. Analysis of the low-energy structures reveals that the electron-phonon coupling is weak (S ≈ 2.15) with a robust characteristic phonon signature across different Al/O arrangements, explaining the surprising persistence of resolved phonon replicas with increasing z. We explain the experimentally observed red-shift of emission with increasing z through systematic trends in zero-phonon line energies, modest increases in Huang-Rhys factors, and larger configurational diversity at higher compositions. application/zip text/markdown https://doi.org/10.24435/materialscloud:4a-g6 oai:materialscloud.org:02rcd-nc341 mcid:2026.99 eng Materials Cloud https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:bq-5m info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode phosphors DFT vibronic electron-phonon Micro-environment of the Eu interstitial in the β-Si6-zAlzOzN8-z:Eu2+ green phosphor info:eu-repo/semantics/other

oai:materialscloud.org:36 2018-05-15T00:00:00Z openaire_data community-mcarchive

Smit, Berend Haranczyk, Maciej Martin, Richard L. Simon, Cory M. Medasani, Bharat Britt, David K. Smit, Berend Haranczyk, Maciej 2018-05-15 Covalent organic frameworks (COFs) are a class of advanced nanoporous polymeric materials which combine the crystallinity of metal–organic frameworks (MOFs) with the stability and potentially low-cost organic chemistry of porous polymer networks (PPNs). Like other advanced porous materials, COFs can potentially be designed to meet the needs of a variety of applications, from energy, to security, to human health. In this work, we construct in silico a database of hypothetical three-dimensional, crystalline COFs. In constructing this library we generate novel COFs using only established synthetic routes, previously utilized tetrahedral building units, and commercially available bridging "linker" molecules. This ensures that there are no known chemical barriers to synthesizing all materials in our database. We relaxed all materials in our database through semiempirical electronic structure calculations. In addition, for those structures that allow interpenetration, we designed interpenetrated versions of the basic structure. Then, we characterized the porosity of each of these structures. The final set of 4147 structures (based on 620 unique noninterpenetrated structures) and their computed properties are publicly available and can be screened to identify promising materials for a wide variety of applications. Here, we assess the suitability of our COFs for vehicular methane storage by performing molecular simulations to predict the equilibrium methane uptake. application/gzip text/markdown https://doi.org/10.24435/materialscloud:2018.0006/v1 oai:materialscloud.org:36 mcid:2018.0006/v1 eng Materials Cloud https://doi.org/10.1021/jp507152j https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:yt-bq info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode 3D three-dimensional database covalent organic frameworks COFs high-throughput nanoporous methane storage deliverable capacities DC grand canonical Monte Carlo GCMC In silico design of three-dimensional porous covalent organic frameworks via known synthesis routes and commercially available species info:eu-repo/semantics/other

oai:materialscloud.org:1376 2022-06-10T21:19:16Z openaire_data community-mcarchive

Lustemberg, Pablo G. Ganduglia-Pirovano, M. Verónica Lustemberg, Pablo G. Senanayake, Sanjaya D. Rodriguez, Jose A. Ganduglia-Pirovano, M. Verónica 2022-06-10 The efficient activation of methane and simultaneous water dissociation are crucial in many catalytic reactions on oxide-supported transition metal catalysts. On very low-loaded Ni/CeO₂ surfaces, methane easily fully decomposes, CH₄ -> C + 4H, and water dissociates, H₂O -> OH + H. However, in important reactions such as the direct oxidation of methane to methanol (MTM), where complex interplay exists between reactants (CH₄, O₂), it is desirable to avoid the complete dehydrogenation of methane to carbon. Remarkably, the barrier for the activation of C-H bonds in CHx (x= 1-3) species on Ni/CeO₂ surfaces can be manipulated by adding Cu, forming bimetallic NiCu clusters, whereas the ease for cleavage of O-H bonds in water, is not affected by ensemble effects, as obtained from density functional theory-based calculations. CH4 activation occurs only on Ni sites and H₂O activation on both Ni and Cu sites. The MTM reaction pathway for the example of the Ni₃Cu₁/CeO₂ model catalyst predict higher selectivity and a lower activation barrier for methanol production, compared with that for Ni₄-CeO₂. These findings point toward a possible strategy to design active and stable catalysts which can be employed for methane activation and conversions. application/zip text/markdown application/zip application/zip application/zip application/zip text/plain application/zip https://doi.org/10.24435/materialscloud:8y-7m oai:materialscloud.org:1376 mcid:2022.75 eng Materials Cloud https://doi.org/10.1021/acs.jpclett.2c00885 https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:wj-nb info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode bimetallic ceria methane activation water activation Tuning selectivity in the direct conversion of methane to methanol: bimetallic synergistic effects on the cleavage of C-H and O-H bonds over NiCu/CeO₂ catalysts info:eu-repo/semantics/other

oai:materialscloud.org:1880 2024-05-21T16:16:34Z openaire_data community-mcarchive

Zhao, Chenxiao Pignedoli, Carlo A. Zhao, Chenxiao Pignedoli, Carlo A. Bhagwandin, Dayanni D. Xu, Wangwei Rufieux, Pascal Fasel, Roman Rubin, Yves 2024-05-21 Hopf et al. first reported the high-temperature 6π-electrocyclization of cis-hexa-1,3-diene-5-yne to benzene in 1969. Subsequent studies using this cyclization have been limited by its very high reaction barrier. Here, we show that the reaction barrier for two model systems, (E)-1,3,4,6-tetraphenyl-3-hexen-1,5-diyne (1a) and (E)-3,4-bis(4-iodophenyl)-1,6-diphenyl-3-hexen-1,5-diyne 1b, is decreased by nearly half on a Au(111) surface. In recent work, we have used scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM) to monitor the Hopf cyclization of enediynes 1a,b on Au(111). Enediyne 1a undergoes two sequential, quantitative Hopf cyclizations, first to naphthalene derivative 2, and finally to chrysene 3. Density functional theory (DFT) calculations reveal that a gold atom from the Au(111) surface is involved in all steps of this reaction, and that it is crucial to lowering the reaction barrier. Our findings have important implications for the synthesis of novel graphene nanoribbons. Ullman coupling of enediyne 1b at 20 ˚C on Au(111), followed by a series of Hopf cyclizations and aromatization reactions at higher temperatures, produces nanoribbons 12, and eventually 13 upon further heating. These results show for the first time that graphene nanoribbons can be synthesized on-surface using the Hopf cyclization mechanism. This record contains all simulation data that support our scientific work. application/octet-stream application/gzip application/octet-stream text/markdown https://doi.org/10.24435/materialscloud:62-ew oai:materialscloud.org:1880 mcid:2024.75 eng Materials Cloud https://doi.org/10.1021/jacs.3c10144 https://renkulab.io/projects/new?data=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 https://materialscloud-archive-failover.cineca.it/communities/mcarchive https://doi.org/10.24435/materialscloud:1b-46 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode MARVEL/P4 SNSF CSCS DFT STM nc-AFM on surface synthesis Dramatic acceleration of the Hopf cyclization on gold(111): from enediynes to unusual graphene nanoribbons info:eu-repo/semantics/other .eJytzM9ugjAcAOB3-Z3JAuWfkHjYJm1iotEyivRiWluUUIRQjW7Gd98OOy878D3A9wCrtYLUffESP4ojNwqiMEZJHMwcGMRRQ4ocaG9iPFpIH9Dpi1DiIjajrps7pNCLZq8O8HTAHsbemH3zswEm5lqhu8cJqyuUXBVhgcKJp0rTLs7Y47tlyMjJyJYbRdb9G-VbjnCnMhV8MPoud0NOGa1WPg3zbvBff1XIlrJkdY7oF8uWhWy8hTxju2rczyLDm-Lfz22iZzbRE0z0uH8_2_kcnt-gRp5Q.arPfag.tphREEBt2KB1cGZrJHZkEz5GauY