Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388955%3A_____%2F21%3A00546397" target="_blank" >RIV/61388955:_____/21:00546397 - isvavai.cz</a>

Výsledek na webu

<a href="http://hdl.handle.net/11104/0322923" target="_blank" >http://hdl.handle.net/11104/0322923</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.jctc.1c00235" target="_blank" >10.1021/acs.jctc.1c00235</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems

Popis výsledku v původním jazyce

Active space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial but a nontrivial task. In this article, we present a neural network-based approach for automatic selection of active spaces, focused on transition metal systems. The training set has been formed from artificial systems composed of one transition metal and various ligands, on which we have performed the density matrix renormalization group and calculated the single-site entropy. On the selected set of systems, ranging from small benchmark molecules up to larger challenging systems involving two metallic centers, we demonstrate that our machine learning models could predict the active space orbitals with reasonable accuracy. We also tested the transferability on out-of-the-model systems, including bimetallic complexes and complexes with ligands, which were not involved in the training set. Also, we tested the correctness of the automatically selected active spaces on a Fe(II)–porphyrin model, where we studied the lowest states at the DMRG level and compared the energy difference between spin states or the energy difference between conformations of ferrocene with recent studies.

Název v anglickém jazyce

Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems

Popis výsledku anglicky

Active space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial but a nontrivial task. In this article, we present a neural network-based approach for automatic selection of active spaces, focused on transition metal systems. The training set has been formed from artificial systems composed of one transition metal and various ligands, on which we have performed the density matrix renormalization group and calculated the single-site entropy. On the selected set of systems, ranging from small benchmark molecules up to larger challenging systems involving two metallic centers, we demonstrate that our machine learning models could predict the active space orbitals with reasonable accuracy. We also tested the transferability on out-of-the-model systems, including bimetallic complexes and complexes with ligands, which were not involved in the training set. Also, we tested the correctness of the automatically selected active spaces on a Fe(II)–porphyrin model, where we studied the lowest states at the DMRG level and compared the energy difference between spin states or the energy difference between conformations of ferrocene with recent studies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

<a href="/cs/project/GJ19-13126Y" target="_blank" >GJ19-13126Y: Deep learning pro silně korelované systémy v kvantové chemii</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Journal of Chemical Theory and Computation

ISSN

1549-9618

e-ISSN

1549-9626

Svazek periodika

17

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

6053-6072

Kód UT WoS článku

000708673100006

EID výsledku v databázi Scopus

2-s2.0-85117236622