A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles

Inclan, Eric; Geohegan, David B.; Yoon, Mina

doi:10.1016/j.commatsci.2017.08.046

Cited by 4 publications

(4 citation statements)

References 48 publications

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“…Furthermore, there is no guarantee of finding it. In literature, for example, there are TiO 2 n cluster studies, generally for small systems (n < 20), using density functional theory (DFT) [19][20][21][22], hybrid DFT [23][24][25][26][27][28][29][30], GW approach [19,[31][32][33], time-dependent density functional theory (TDDFT) [19,24,25,[34][35][36][37], coupled-cluster theory [20,29,31], hybrid algorithms [38], and genetic algorithms [39,40].…”

Section: Introductionmentioning

confidence: 99%

Strongly Bound Frenkel Excitons on TiO2 Nanoparticles: An Evolutionary and DFT Approach

Olvera-Neria,

García-Cruz,

Gonzalez-Torres

et al. 2024

International Journal of Photoenergy

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An evolutionary algorithm was employed to locate the global minimum of TiO2n nanoparticles with n=2–20. More than 61,000 structures were calculated with a semiempirical method and reoptimized using density functional theory. The exciton binding energy of TiO2 nanoparticles was determined through the fundamental and optical band gap. Frenkel exciton energy scales as EB eV=8.07/n0.85, resulting in strongly bound excitons of 0.132–1.2 eV for about 1.4 nm nanoparticles. Although the exciton energy decreases with the system size, these tightly bound Frenkel excitons inhibit the separation of photogenerated charge carriers, making their application in photocatalysis and photovoltaic devices difficult, and imposing a minimum particle size. In contrast, the exciton binding energy of rutile is 4 meV, where the Wannier exciton energy scales as EB eV=13.61 μ/ε2. Moreover, the Wannier excitons in bulk TiO2 are delocalized according to the Bohr radii: 3.9 nm for anatase and 7.7 nm for rutile.

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Section: Introductionmentioning

confidence: 99%

Strongly Bound Frenkel Excitons on TiO2 Nanoparticles: An Evolutionary and DFT Approach

Olvera-Neria,

García-Cruz,

Gonzalez-Torres

et al. 2024

International Journal of Photoenergy

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“…10 For basinhopping, Monte Carlo approaches to structure optimizations have been performed, 12 and Broyden−Fletcher−Goldfarb− Shanno (BFGS) search has been hybridized with evolutionary algorithms for stable nanoparticles. 16 Specifically for gold nanoparticles, which we also explore in this work, basinhopping has been combined with DFT calculations to search for global minima configurations for Au n particles. 11 Machine learning (ML) has expedited many problems in computational science, and has also previously been applied to nanoparticle structure search.…”

Section: Introductionmentioning

confidence: 99%

“…Genetic algorithms have been used to search for global minima over multiple potential energy surfaces, and has furthermore been coupled to DFT calculations . For basin-hopping, Monte Carlo approaches to structure optimizations have been performed, and Broyden–Fletcher–Goldfarb–Shanno (BFGS) search has been hybridized with evolutionary algorithms for stable nanoparticles . Specifically for gold nanoparticles, which we also explore in this work, basin-hopping has been combined with DFT calculations to search for global minima configurations for Au n particles …”

Section: Introductionmentioning

confidence: 99%

Equivariant Graph-Representation-Based Actor–Critic Reinforcement Learning for Nanoparticle Design

Elsborg

Bhowmik

2023

J. Chem. Inf. Model.

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We have developed an actor–critic-type policy-based reinforcement learning (RL) method to find low-energy nanoparticle structures and compared its effectiveness to classical basin-hopping. We took a molecule building approach where nanoalloy particles can be regarded as metallic molecules, albeit with much higher flexibility in structure. We explore the strengths of our approach by tasking an agent with the construction of stable mono- and bimetallic clusters. Following physics, an appropriate reward function and an equivariant molecular graph representation framework is used to learn the policy. The agent succeeds in finding well-known stable configuration for small clusters in both single and multicluster experiments. However, for certain use cases the agent lacks generalization to avoid overfitting. We relate this to the pitfalls of actor–critic methods for molecular design and discuss what learning properties an agent will require to achieve universality.

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“…A hybrid method is proposed for improving the performance of the stochastic and deterministic optimal power flow problem [33]. A hybrid method combined DE with the Broyden-Fletcher-Goldfarb-Shanno quasi-Newton is presented [34]. A semi-autonomous particle swarm optimizer as another mixed method is developed for optimizing multimodal functions.…”

Section: Introductionmentioning

confidence: 99%

Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Pençe

Çeşmeli

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Deterministic methods are used for optimum solution of many engineering and scientific problems. Filled function method, which is a deterministic method, is not trapped to local minimums with its ability to bypass energy barriers. In order to achieve this, the basin regions of the filled function should be located, and the filled function should be constructed in that region. However, classical search strategies used for finding the basin regions don't yield effective results. In this study, a new stochastic search approach is presented as a faster and more efficient alternative to classic filled function search strategy. An unconstrained global optimization method based on clustering and parabolic approximation (GOBC-PA) has been used as a stochastic method for accelerating the L type filled function as a deterministic method. The developed method has been tested against classical filled function using 11 benchmark functions. When the obtained results are examined, it is seen that the stochastic search approach has superiority over the mean error, standard deviation and elapsed time values according to the classical approach. These results show that the combination of deterministic and stochastic methods can be more successful in finding the global minimum against the classic deterministic method.

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A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles

Cited by 4 publications

References 48 publications

Strongly Bound Frenkel Excitons on TiO2 Nanoparticles: An Evolutionary and DFT Approach

Strongly Bound Frenkel Excitons on TiO2 Nanoparticles: An Evolutionary and DFT Approach

Equivariant Graph-Representation-Based Actor–Critic Reinforcement Learning for Nanoparticle Design

Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

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