Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389005%3A_____%2F22%3A00556226" target="_blank" >RIV/61389005:_____/22:00556226 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1103/PhysRevC.105.034306" target="_blank" >https://doi.org/10.1103/PhysRevC.105.034306</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevC.105.034306" target="_blank" >10.1103/PhysRevC.105.034306</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model

Popis výsledku v původním jazyce

A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amid a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results from the symmetry-adapted no-core shell model (SA-NCSM) for light nuclei. We show that the SA-NCSM, which expands ab initio applications up to medium-mass nuclei by using dominant symmetries of nuclear dynamics, can reach heavier nuclei when coupled with the machine learning approach. In particular, we find that a neural network trained on probability amplitudes for s- and p-shell nuclear wave functions not only predicts dominant configurations for heavier nuclei but in addition, when tested for the Ne-20 ground state, accurately reproduces the probability distribution. The non-negligible configurations predicted by the network provide an important input to the SA-NCSM for reducing ultralarge model spaces to manageable sizes that can be, in turn, utilized in SA-NCSM calculations to obtain accurate observables. The neural network is capable of describing nuclear deformation and is used to track the shape evolution along the Mg20-42 isotopic chain, suggesting a shape coexistence that is more pronounced toward the very neutron-rich isotopes. We provide first descriptions of the structure and deformation of Si-24 and Mg-40 of interest to x-ray burst nucleosynthesis, and even of the extremely heavy nuclei such as Er-166,Er-168 and U-236, that build on first-principles considerations.

Název v anglickém jazyce

Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model

Popis výsledku anglicky

A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amid a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results from the symmetry-adapted no-core shell model (SA-NCSM) for light nuclei. We show that the SA-NCSM, which expands ab initio applications up to medium-mass nuclei by using dominant symmetries of nuclear dynamics, can reach heavier nuclei when coupled with the machine learning approach. In particular, we find that a neural network trained on probability amplitudes for s- and p-shell nuclear wave functions not only predicts dominant configurations for heavier nuclei but in addition, when tested for the Ne-20 ground state, accurately reproduces the probability distribution. The non-negligible configurations predicted by the network provide an important input to the SA-NCSM for reducing ultralarge model spaces to manageable sizes that can be, in turn, utilized in SA-NCSM calculations to obtain accurate observables. The neural network is capable of describing nuclear deformation and is used to track the shape evolution along the Mg20-42 isotopic chain, suggesting a shape coexistence that is more pronounced toward the very neutron-rich isotopes. We provide first descriptions of the structure and deformation of Si-24 and Mg-40 of interest to x-ray burst nucleosynthesis, and even of the extremely heavy nuclei such as Er-166,Er-168 and U-236, that build on first-principles considerations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10304 - Nuclear physics

Projekt

<a href="/cs/project/GA22-14497S" target="_blank" >GA22-14497S: Posouvání hranic ab initio výpočtů jaderné struktury</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Physical Review C

ISSN

2469-9985

e-ISSN

2469-9993

Svazek periodika

105

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

034306

Kód UT WoS článku

000767144000001

EID výsledku v databázi Scopus

2-s2.0-85126700632