Fast and accurate excited states predictions: machine learning and diabatization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F24%3A43931003" target="_blank" >RIV/60461373:22340/24:43931003 - isvavai.cz</a>

Result on the web

<a href="https://pubs.rsc.org/en/content/articlelanding/2024/cp/d3cp05685f" target="_blank" >https://pubs.rsc.org/en/content/articlelanding/2024/cp/d3cp05685f</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1039/d3cp05685f" target="_blank" >10.1039/d3cp05685f</a>

Result language

angličtina

Original language name

Fast and accurate excited states predictions: machine learning and diabatization

Original language description

The efficiency of machine learning algorithms for electronically excited states is far behind ground-state applications. One of the underlying problems is the insufficient smoothness of the fitted potential energy surfaces and other properties in the vicinity of state crossings and conical intersections, which is a prerequisite for an efficient regression. Smooth surfaces can be obtained by switching to the diabatic basis. However, diabatization itself is still an outstanding problem. We overcome these limitations by solving both problems at once. We use a machine learning approach combining clustering and regression techniques to correct for the deficiencies of property-based diabatization which, in return, provides us with smooth surfaces that can be easily fitted. Our approach extends the applicability of property-based diabatization to multidimensional systems. We utilize the proposed diabatization scheme to achieve higher prediction accuracy for adiabatic states and we show its performance by reconstructing global potential energy surfaces of excited states of nitrosyl fluoride and formaldehyde. While the proposed methodology is independent of the specific property-based diabatization and regression algorithm, we show its performance for kernel ridge regression and a very simple diabatization based on transition multipoles. Compared to most other algorithms based on machine learning, our approach needs only a small amount of training data. Efficient machine learning predictions for excited states can be achieved via machine-learned diabatization.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2024

Confidentiality

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

Name of the periodical

PHYSICAL CHEMISTRY CHEMICAL PHYSICS

ISSN

1463-9076

e-ISSN

1463-9084

Volume of the periodical

26

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

4306-4319

UT code for WoS article

001144057300001

EID of the result in the Scopus database

2-s2.0-85182921885