Gamow-Teller (p,n) and (n,p) strength in a dressed extended random phase approximation

Geurts, W.J.W.; Allaart, K.; Dickhoff, W. H.

doi:10.1103/physrevc.50.514

Cited by 11 publications

(24 citation statements)

References 10 publications

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“…It identified that the field is scattered and largely lacking a supportive ecosystem with nonacademic stakeholders. [305,306] The socioeconomic study identified visionary scientists at academic institutions who have pursued their idiosyncratic research objectives using trending topics in public discussions and governmental funding, including Open Science, Big Data, and AI. At the same time, government and funding agencies have provided strategic research openings focused on propelling the field of data-driven science in general.…”

Section: Stakeholder Relationsmentioning

confidence: 99%

“…[311] To capitalize on their data, three business models can be applied or are considered for application. [305,306] First, firms can consult and collaborate directly with established data platforms (see Figure 9b), for example those discussed in Figures 2 and 3. Traditionally such collaborations take the form of strategic interfirm alliances that influence companies' potential for knowledge creation.…”

Section: Stakeholder Relationsmentioning

confidence: 99%

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Data‐Driven Materials Science: Status, Challenges, and Perspectives

et al. 2019

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Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high‐throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data‐driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data‐driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

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Section: Stakeholder Relationsmentioning

confidence: 99%

Section: Stakeholder Relationsmentioning

confidence: 99%

Data‐Driven Materials Science: Status, Challenges, and Perspectives

et al. 2019

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“…These were evaluated in the dressed random phase approximation (DRPA) [18]. The example of a diagram that contributes to R (2p1h) is given in Fig.…”

Section: Modelmentioning

confidence: 99%

Nucleon-nucleus optical potential in the particle-hole approach

2005

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Feshbach's projection formalism in the particle-hole model space leads to a microscopic description of scattering in terms of the many-body self-energy. To investigate the feasibility of this approach, an optical potential for 16 O is constructed starting from two previous calculations of the self-energy for this nucleus. The results reproduce the background phase shifts for positive parity waves and the resonances beyond the mean field. The latter can be computed microscopically for energies of astrophysical interest using Green's function theory.

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“…[31,220] The calculations of Sec. 5.5 employ a bare interaction V αβ,γδ for the vertices K (pp) and K (ph) so that the Bethe-Salpeter equations (154) reduce to the usual dressed RPA (DRPA) equations [32,221]. The solutions of these equations depend only on two times.…”

Section: Faddeev Approach To the Treatment Of Collective Excitationsmentioning

confidence: 99%

Self-consistent Green's function method for nuclei and nuclear matter

Dickhoff

Barbieri

2004

Progress in Particle and Nuclear Physics

500

599

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Recent results obtained by applying the method of self-consistent Green's functions to nuclei and nuclear matter are reviewed. Particular attention is given to the description of experimental data obtained from the (e,e ′ p) and (e,e ′ 2N) reactions that determine one and two-nucleon removal probabilities in nuclei since the corresponding amplitudes are directly related to the imaginary parts of the single-particle and two-particle propagators. For this reason and the fact that these amplitudes can now be calculated with the inclusion of all the relevant physical processes, it is useful to explore the efficacy of the method of self-consistent Green's functions in describing these experimental data. Results for both finite nuclei and nuclear matter are discussed with particular emphasis on clarifying the role of short-range correlations in determining various experimental quantities. The important role of long-range correlations in determining the structure of lowenergy correlations is also documented. For a complete understanding of nuclear phenomena it is therefore essential to include both types of physical correlations. We demonstrate that recent experimental results for these reactions combined with the reported theoretical calculations yield a very clear understanding of the properties of all protons in the nucleus. We propose that this knowledge of the properties of constituent fermions in a correlated many-body system is a unique feature of nuclear physics.

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Gamow-Teller (p,n) and (n,p) strength in a dressed extended random phase approximation

Cited by 11 publications

References 10 publications

Data‐Driven Materials Science: Status, Challenges, and Perspectives

Data‐Driven Materials Science: Status, Challenges, and Perspectives

Nucleon-nucleus optical potential in the particle-hole approach

Self-consistent Green's function method for nuclei and nuclear matter

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