Strukturuntersuchung von Mehrstoffsystemen durch kınematische Elektronenbeugung

Boettcher, A.; Haase, Günter; Thun, R.

doi:10.1515/ijmr-1955-460511

Cited by 11 publications

(16 citation statements)

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“…24 The transformation from achantite into the b allotropic form, argentite, of bcc structure, occurs through the formation of an intermediate tetragonal phase. 23,25 Above 600 ³C the cubic c phase with fcc structure, of Ag 2 S is formed, 23 which melts at ca. 825 ³C.…”

Section: Introductionmentioning

confidence: 99%

Sol–gel synthesis and characterization of Ag2S nanocrystallites in silica thin film glasses

Armelao¹,

Colombo²,

Fabrizio³

et al. 1999

J. Mater. Chem.

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Section: Introductionmentioning

confidence: 99%

Sol–gel synthesis and characterization of Ag2S nanocrystallites in silica thin film glasses

Armelao¹,

Colombo²,

Fabrizio³

et al. 1999

J. Mater. Chem.

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“…Since the first demonstration of combinatorial synthesis several decades ago, [18] there has been tremendous progress in the field of materials discovery through incorporation of combinatorial synthesis [19,20] and high-throughput (HT) characterization. [13] Utilization of the wealth of metadata accompanying measurement results generated by HT methods became possible through rapid advancement in data management and integration of machine learning (ML), deep learning (DL), and artificial intelligence (AI).…”

Section: Accelerating the Search For Functional Materials Is An Ongoi...mentioning

confidence: 99%

High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research

Benayad

Diddens

Heuer

et al. 2021

Advanced Energy Materials

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The timely arrival of novel materials plays a key role in bringing advances to society, as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at which novel materials are identified within chemical space. High‐throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes toward identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision‐making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces.

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“…Historically, one of the most significant driving forces for accelerated research in materials science has been high‐throughput experimentation and simulations. Originating in the 1950s, for example, in the fields of biology with the use of multi‐well microtiter plates [ 1 ] and metallurgy where scientists sought to discover new dental alloys, [ 2 ] comprehensive platforms are now being developed for the advancement of autonomous research. [ 3 ] The recent flourish in machine learning (ML) for materials and energy science, and methods for AI‐orchestrated materials discovery, may likely provide a similar or even larger boost.…”

Section: Introductionmentioning

confidence: 99%

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Bhowmik¹,

Berecibar

Casas‐Cabanas

et al. 2021

Advanced Energy Materials

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BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries.Here, a description is given of how the AI-assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low-technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI-assisted toolkit developed within BIG-MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time-and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling and explainable AI, as well as AI-assisted materials characterization, self-driving labs, closed-loop optimization, and AI for advanced sensing and self-healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low-TRL battery chemistries and concepts covering multivalent anodes, metalsulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries.

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Strukturuntersuchung von Mehrstoffsystemen durch kınematische Elektronenbeugung

Cited by 11 publications

References 0 publications

Sol–gel synthesis and characterization of Ag2S nanocrystallites in silica thin film glasses

Sol–gel synthesis and characterization of Ag2S nanocrystallites in silica thin film glasses

High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

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