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Artikel: Computational Investigation of Copper Phosphides as Conversion Anodes for Lithium-Ion Batteries.

Harper, Angela F / Evans, Matthew L / Morris, Andrew J

Chemistry of materials : a publication of the American Chemical Society

2020 Band 32, Heft 15, Seite(n) 6629–6639

Abstract: Using first-principles structure searching with density-functional theory (DFT), we identify a ... ...

Abstract	Using first-principles structure searching with density-functional theory (DFT), we identify a novel
Sprache	Englisch
Erscheinungsdatum	2020-06-25
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	1500399-1
ISSN	1520-5002 ; 0897-4756
ISSN (online)	1520-5002
ISSN	0897-4756
DOI	10.1021/acs.chemmater.0c02054
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet.

De Breuck, Pierre-Paul / Evans, Matthew L / Rignanese, Gian-Marco

Journal of physics. Condensed matter : an Institute of Physics journal

2021 Band 33, Heft 40

Abstract: As the number of novel data-driven approaches to material science continues to grow, it is crucial to perform consistent quality, reliability and applicability assessments of model performance. In this paper, we benchmark the Materials Optimal Descriptor ...

Abstract	As the number of novel data-driven approaches to material science continues to grow, it is crucial to perform consistent quality, reliability and applicability assessments of model performance. In this paper, we benchmark the Materials Optimal Descriptor Network (MODNet) method and architecture against the recently released MatBench v0.1, a curated test suite of materials datasets. MODNet is shown to outperform current leaders on 6 of the 13 tasks, while closely matching the current leaders on a further 2 tasks; MODNet performs particularly well when the number of samples is below 10 000. Attention is paid to two topics of concern when benchmarking models. First, we encourage the reporting of a more diverse set of metrics as it leads to a more comprehensive and holistic comparison of model performance. Second, an equally important task is the uncertainty assessment of a model towards a target domain. Significant variations in validation errors can be observed, depending on the imbalance and bias in the training set (i.e., similarity between training and application space). By using an ensemble MODNet model, confidence intervals can be built and the uncertainty on individual predictions can be quantified. Imbalance and bias issues are often overlooked, and yet are important for successful real-world applications of machine learning in materials science and condensed matter.
Sprache	Englisch
Erscheinungsdatum	2021-07-29
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	1472968-4
ISSN	1361-648X ; 0953-8984
ISSN (online)	1361-648X
ISSN	0953-8984
DOI	10.1088/1361-648X/ac1280
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Phase Transformations and Phase Segregation during Potassiation of Sn

Ells, Andrew W / Evans, Matthew L / Groh, Matthias F / Morris, Andrew J / Marbella, Lauren E

Chemistry of materials : a publication of the American Chemical Society

2022 Band 34, Heft 16, Seite(n) 7460–7467

Abstract: K-ion batteries (KIBs) have the potential to offer a cheaper alternative to Li-ion batteries (LIBs) using widely abundant materials. Conversion/alloying anodes have high theoretical capacities in KIBs, but it is believed that electrode damage from volume ...

Abstract	K-ion batteries (KIBs) have the potential to offer a cheaper alternative to Li-ion batteries (LIBs) using widely abundant materials. Conversion/alloying anodes have high theoretical capacities in KIBs, but it is believed that electrode damage from volume expansion and phase segregation by the accommodation of large K-ions leads to capacity loss during electrochemical cycling. To date, the exact phase transformations that occur during potassiation and depotassiation of conversion/alloying anodes are relatively unexplored. In this work, we synthesize two distinct compositions of tin phosphides, Sn
Sprache	Englisch
Erscheinungsdatum	2022-08-01
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	1500399-1
ISSN	1520-5002 ; 0897-4756
ISSN (online)	1520-5002
ISSN	0897-4756
DOI	10.1021/acs.chemmater.2c01570
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Machine Learning-Accelerated Discovery of

Wang, Zhiqi / Gong, Yutong / Evans, Matthew L / Yan, Yujing / Wang, Shiyao / Miao, Nanxi / Zheng, Ruiheng / Rignanese, Gian-Marco / Wang, Junjie

Journal of the American Chemical Society

2023 Band 145, Heft 48, Seite(n) 26412–26424

Abstract: This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in ... ...

Abstract	This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the
Sprache	Englisch
Erscheinungsdatum	2023-11-21
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	3155-0
ISSN	1520-5126 ; 0002-7863
ISSN (online)	1520-5126
ISSN	0002-7863
DOI	10.1021/jacs.3c10538
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Computational Investigation of Copper Phosphides as Conversion Anodes for Lithium-Ion Batteries

Harper, Angela F. / Evans, Matthew L. / Morris, Andrew J.

2020

Abstract: Using first principles structure searching with density-functional theory (DFT) we identify a novel $Fm\bar{3}m$ phase of Cu$_2$P and two low-lying metastable structures, an $I\bar{4}3d$--Cu$_3$P phase, and a $Cm$--Cu$_3$P$_{11}$ phase. The computed pair ...

Abstract	Using first principles structure searching with density-functional theory (DFT) we identify a novel $Fm\bar{3}m$ phase of Cu$_2$P and two low-lying metastable structures, an $I\bar{4}3d$--Cu$_3$P phase, and a $Cm$--Cu$_3$P$_{11}$ phase. The computed pair distribution function of the novel $Cm$--Cu$_3$P$_{11}$ phase shows its structural similarity to the experimentally identified $Cm$--Cu$_2$P$_7$ phase. The relative stability of all Cu--P phases at finite temperatures is determined by calculating the Gibbs free energy using vibrational effects from phonon modes at 0 K. From this, a finite-temperature convex hull is created, on which $Fm\bar{3}m$--Cu$_2$P is dynamically stable and the Cu$_{3-x}$P ($x < 1$) defect phase $Cmc2_1$--Cu$_8$P$_3$ remains metastable (within 20 meV/atom of the convex hull) across a temperature range from 0 K to 600 K. Both CuP$_2$ and Cu$_3$P exhibit theoretical gravimetric capacities higher than contemporary graphite anodes for Li-ion batteries; the predicted Cu$_2$P phase has a theoretical gravimetric capacity of 508 mAh/g as a Li-ion battery electrode, greater than both Cu$_3$P (363 mAh/g) and graphite (372 mAh/g). Cu$_2$P is also predicted to be both non-magnetic and metallic, which should promote efficient electron transfer in the anode. Cu$_2$P's favorable properties as a metallic, high-capacity material suggest its use as a future conversion anode for Li-ion batteries; with a volume expansion of 99% during complete cycling, Cu$_2$P anodes could be more durable than other conversion anodes in the Cu--P system with volume expansions greater than 150%. Comment: For associated data see https://github.com/harpaf13/data.copper-phosphides and https://doi.org/10.17863/CAM.52272
Schlagwörter	Condensed Matter - Materials Science
Erscheinungsdatum	2020-05-11
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Robust model benchmarking and bias-imbalance in data-driven materials science

De Breuck, Pierre-Paul / Evans, Matthew L. / Rignanese, Gian-Marco

a case study on MODNet

2021

Abstract	As the number of novel data-driven approaches to material science continues to grow, it is crucial to perform consistent quality, reliability and applicability assessments of model performance. In this paper, we benchmark the Materials Optimal Descriptor Network (MODNet) method and architecture against the recently released MatBench v0.1, a curated test suite of materials datasets. MODNet is shown to outperform current leaders on 6 of the 13 tasks, whilst closely matching the current leaders on a further 2 tasks; MODNet performs particularly well when the number of samples is below 10,000. Attention is paid to two topics of concern when benchmarking models. First, we encourage the reporting of a more diverse set of metrics as it leads to a more comprehensive and holistic comparison of model performance. Second, an equally important task is the uncertainty assessment of a model towards a target domain. Significant variations in validation errors can be observed, depending on the imbalance and bias in the training set (i.e., similarity between training and application space). By using an ensemble MODNet model, confidence intervals can be built and the uncertainty on individual predictions can be quantified. Imbalance and bias issues are often overlooked, and yet are important for successful real-world applications of machine learning in materials science and condensed matter.
Schlagwörter	Condensed Matter - Materials Science
Thema/Rubrik (Code)	670
Erscheinungsdatum	2021-02-03
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Sodiation and Desodiation via Helical Phosphorus Intermediates in High-Capacity Anodes for Sodium-Ion Batteries.

Marbella, Lauren E / Evans, Matthew L / Groh, Matthias F / Nelson, Joseph / Griffith, Kent J / Morris, Andrew J / Grey, Clare P

Journal of the American Chemical Society

2018 Band 140, Heft 25, Seite(n) 7994–8004

Abstract: Na-ion batteries are promising alternatives to Li-ion systems for electrochemical energy storage because of the higher natural abundance and widespread distribution of Na compared to Li. High capacity anode materials, such as phosphorus, have been ... ...

Abstract	Na-ion batteries are promising alternatives to Li-ion systems for electrochemical energy storage because of the higher natural abundance and widespread distribution of Na compared to Li. High capacity anode materials, such as phosphorus, have been explored to realize Na-ion battery technologies that offer comparable performances to their Li-ion counterparts. While P anodes provide unparalleled capacities, the mechanism of sodiation and desodiation is not well-understood, limiting further optimization. Here, we use a combined experimental and theoretical approach to provide molecular-level insight into the (de)sodiation pathways in black P anodes for sodium-ion batteries. A determination of the P binding in these materials was achieved by comparing to structure models created via species swapping, ab initio random structure searching, and a genetic algorithm. During sodiation, analysis of
Sprache	Englisch
Erscheinungsdatum	2018-06-19
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	3155-0
ISSN	1520-5126 ; 0002-7863
ISSN (online)	1520-5126
ISSN	0002-7863
DOI	10.1021/jacs.8b04183
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.

Jablonka, Kevin Maik / Ai, Qianxiang / Al-Feghali, Alexander / Badhwar, Shruti / Bocarsly, Joshua D / Bran, Andres M / Bringuier, Stefan / Brinson, L Catherine / Choudhary, Kamal / Circi, Defne / Cox, Sam / de Jong, Wibe A / Evans, Matthew L / Gastellu, Nicolas / Genzling, Jerome / Gil, María Victoria / Gupta, Ankur K / Hong, Zhi / Imran, Alishba /

Kruschwitz, Sabine / Labarre, Anne / Lála, Jakub / Liu, Tao / Ma, Steven / Majumdar, Sauradeep / Merz, Garrett W / Moitessier, Nicolas / Moubarak, Elias / Mouriño, Beatriz / Pelkie, Brenden / Pieler, Michael / Ramos, Mayk Caldas / Ranković, Bojana / Rodriques, Samuel G / Sanders, Jacob N / Schwaller, Philippe / Schwarting, Marcus / Shi, Jiale / Smit, Berend / Smith, Ben E / Van Herck, Joren / Völker, Christoph / Ward, Logan / Warren, Sean / Weiser, Benjamin / Zhang, Sylvester / Zhang, Xiaoqi / Zia, Ghezal Ahmad / Scourtas, Aristana / Schmidt, K J / Foster, Ian / White, Andrew D / Blaiszik, Ben

Digital discovery

2023 Band 2, Heft 5, Seite(n) 1233–1250

Abstract: Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article ... ...

Abstract	Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
Sprache	Englisch
Erscheinungsdatum	2023-08-08
Erscheinungsland	England
Dokumenttyp	Journal Article ; Review
ISSN	2635-098X
ISSN (online)	2635-098X
DOI	10.1039/d3dd00113j
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Evans, Matthew L. / Bergsma, Johan / Merkys, Andrius / Andersen, Casper W. / Andersson, Oskar B. / Beltrán, Daniel / Blokhin, Evgeny / Boland, Tara M. / Balderas, Rubén Castañeda / Choudhary, Kamal / Díaz, Alberto Díaz / García, Rodrigo Domínguez / Eckert, Hagen / Eimre, Kristjan / Montero, María Elena Fuentes / Krajewski, Adam M. / Mortensen, Jens Jørgen / Duarte, José Manuel Nápoles / Pietryga, Jacob /

Qi, Ji / Carrillo, Felipe de Jesús Trejo / Vaitkus, Antanas / Yu, Jusong / Zettel, Adam / de Castro, Pedro Baptista / Carlsson, Johan / Cerqueira, Tiago F. T. / Divilov, Simon / Hajiyani, Hamidreza / Hanke, Felix / Jose, Kevin / Oses, Corey / Riebesell, Janosh / Schmidt, Jonathan / Winston, Donald / Xie, Christen / Yang, Xiaoyu / Bonella, Sara / Botti, Silvana / Curtarolo, Stefano / Draxl, Claudia / Cobas, Luis Edmundo Fuentes / Hospital, Adam / Liu, Zi-Kui / Marques, Miguel A. L. / Marzari, Nicola / Morris, Andrew J. / Ong, Shyue Ping / Orozco, Modesto / Persson, Kristin A. / Thygesen, Kristian S. / Wolverton, Chris / Scheidgen, Markus / Toher, Cormac / Conduit, Gareth J. / Pizzi, Giovanni / Gražulis, Saulius / Rignanese, Gian-Marco / Armiento, Rickard

2024

Abstract: The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical ... ...

Abstract	The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.
Schlagwörter	Condensed Matter - Materials Science
Erscheinungsdatum	2024-02-01
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: 14 Examples of How LLMs Can Transform Materials Science and Chemistry

Jablonka, Kevin Maik / Ai, Qianxiang / Al-Feghali, Alexander / Badhwar, Shruti / Bocarsly, Joshua D. / Bran, Andres M / Bringuier, Stefan / Brinson, L. Catherine / Choudhary, Kamal / Circi, Defne / Cox, Sam / de Jong, Wibe A. / Evans, Matthew L. / Gastellu, Nicolas / Genzling, Jerome / Gil, María Victoria / Gupta, Ankur K. / Hong, Zhi / Imran, Alishba /

Kruschwitz, Sabine / Labarre, Anne / Lála, Jakub / Liu, Tao / Ma, Steven / Majumdar, Sauradeep / Merz, Garrett W. / Moitessier, Nicolas / Moubarak, Elias / Mouriño, Beatriz / Pelkie, Brenden / Pieler, Michael / Ramos, Mayk Caldas / Ranković, Bojana / Rodriques, Samuel G. / Sanders, Jacob N. / Schwaller, Philippe / Schwarting, Marcus / Shi, Jiale / Smit, Berend / Smith, Ben E. / Van Herck, Joren / Völker, Christoph / Ward, Logan / Warren, Sean / Weiser, Benjamin / Zhang, Sylvester / Zhang, Xiaoqi / Zia, Ghezal Ahmad / Scourtas, Aristana / Schmidt, KJ / Foster, Ian / White, Andrew D. / Blaiszik, Ben

A Reflection on a Large Language Model Hackathon

2023

Abstract	Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
Schlagwörter	Condensed Matter - Materials Science ; Computer Science - Machine Learning ; Physics - Chemical Physics
Thema/Rubrik (Code)	501
Erscheinungsdatum	2023-06-09
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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