Designing stable bimetallic nanoclusters <i>via</i> an iterative two-step optimization approach

Yin, Xiangyu; Isenberg, Natalie M.; Hanselman, Christopher L.; Dean, James W.; Mpourmpakis, Giannis; Gounaris, Chrysanthos E.

doi:10.1039/d1me00027f

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…Yin et al. used simulated annealing (SA) to optimize the particle shapes, and this implementation allows for simulating arbitrary NP shapes by repositioning surface atoms. The scaling relation C E ∝ C N C B (present in the BC model) remains true for NP shapes that display extreme coordination environments.…”

Section: Discussionmentioning

confidence: 99%

“…To this end, there have been recent works that extend cluster expansion methods to capture unique configurational thermodynamics. , Then, by knowing the exact structure and metal composition on the NP surface, sophisticated catalytic models can be developed, predicting the catalytic behavior of NP ensembles that account for, in addition to the NP morphology distribution, the chemical ordering distribution at elevated temperatures. To discern the stability of different particle morphologies and chemical orderings, two-step approaches, such as the one proposed by Yin et al with AgCu, CuAu, and AuAg nanoclusters, can be implemented with the use of the BC model . One can first identify the most cohesive bimetallic ordering for a given composition and specific particle shape coupled with a metaheuristic strategy that searches over the space of particle shapes to obtain globally optimal structures (optimizing both chemical ordering and particle morphology).…”

Section: Discussionmentioning

confidence: 99%

“…52 One can first identify the most cohesive bimetallic ordering for a given composition and specific particle shape coupled with a metaheuristic strategy that searches over the space of particle shapes to obtain globally optimal structures (optimizing both chemical ordering and particle morphology). Yin et al used simulated annealing (SA) to optimize the particle shapes, 52 (present in the BC model) remains true for NP shapes that display extreme coordination environments. This relationship has been shown to accurately predict the energetics of nonequilibrium NP shapes such as the truncated cuboctahedron.…”

Section: ■ Conclusion and Outlookmentioning

confidence: 99%

“…To discern the stability of different particle morphologies and chemical orderings, twostep approaches, such as the one proposed by Yin et al with AgCu, CuAu, and AuAg nanoclusters, can be implemented with the use of the BC model. 52 One can first identify the most cohesive bimetallic ordering for a given composition and specific particle shape coupled with a metaheuristic strategy that searches over the space of particle shapes to obtain globally optimal structures (optimizing both chemical ordering and particle morphology). Yin et al used simulated annealing (SA) to optimize the particle shapes, 52 (present in the BC model) remains true for NP shapes that display extreme coordination environments.…”

Section: ■ Conclusion and Outlookmentioning

confidence: 99%

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Demystifying the Chemical Ordering of Multimetallic Nanoparticles

2023

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Conspectus Multimetallic nanoparticles (NPs) have highly tunable properties due to the synergy between the different metals and the wide variety of NP structural parameters such as size, shape, composition, and chemical ordering. The major problem with studying multimetallic NPs is that as the number of different metals increases, the number of possible chemical orderings (placements of different metals) for a NP of fixed size explodes. Thus, it becomes infeasible to explore NP energetic differences with highly accurate computational methods, such as density functional theory (DFT), which has a high computational cost and is typically applied to up to a couple of hundred metal atoms. Here, we demonstrate a methodology advancing NP simulations by effectively exploring the vast materials space of multimetallic NPs and accurately identifying the ones with the most thermodynamically preferred chemical orderings. With accuracies reaching that of DFT, our methodology is applicable to practically any NP size, shape, and metal composition. We achieve this by significantly advancing the bond-centric (BC) model, a physics-based model that has been previously shown to rapidly predict bimetallic NP cohesive energies (CEs). Specifically, the BC model is trained in a way to understand how the bimetallic bond strength changes under different coordination environments present on a NP and how the metal composition of every site affects the detailed coordination environment using fractional coordination numbers. This newly modified BC model leads to an improvement from 0.331 (original model) to 0.089 eV/atom in CE predictions when compared to DFT values on a robust data set of 90 different NPs consisting of PtPd, AuPt, and AuPd NPs with varying compositions and chemical orderings. By incorporating the modified BC model into an in-house-developed genetic algorithm (GA) we can effectively and accurately predict the most stable chemical orderings of large, realistic bimetallic NPs consisting of thousands of metal atoms. This is demonstrated on AuPd bimetallic NPs, a challenging system due to the similarity in the cohesion of the two metals. By training our BC model using a unique DFT calculation on a bimetallic NP (one calculation for two metals combining together), we expand to explore the chemical ordering of multimetallic NPs. We first demonstrate the application of our methodology on a AuPdPt NP and validate our stability predictions with literature data. Then, we effectively explore the vast materials space of multimetallic NPs consisting of combinations of Au, Pt, and Pd as a function of metal composition. Our thermodynamic stability trends are presented in a ternary diagram revealing detailed, and yet, unexpected chemical ordering trends. Our computational framework can aid both experimental and computational researchers toward effectively screening multimetallic NP stability. Moreover, we provide an outlook of how this framework can be applied to catalyst discovery, high-entropy alloys, and single-atom alloys.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: ■ Conclusion and Outlookmentioning

confidence: 99%

Section: ■ Conclusion and Outlookmentioning

confidence: 99%

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Demystifying the Chemical Ordering of Multimetallic Nanoparticles

2023

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“…In this paper, we seek to identify provably optimal designs by applying exact mathematical optimization algorithms to arrange nanostructured materials from their fundamental building blocks. Previously, we have developed mathematical optimization models for the design of nanostructured surfaces, , doped perovskites, , metallic nanoclusters, , and nanowires . In each case, we showed how mathematical optimization was well-suited to search the combinatorial design space of nanostructures by formulating appropriate models.…”

Section: Introductionmentioning

confidence: 99%

MatOpt: A Python Package for Nanomaterials Design Using Discrete Optimization

Hanselman

Yin

Miller

et al. 2022

J. Chem. Inf. Model.

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Novel materials are being enabled by advances in synthesis techniques that achieve ever better control over the atomic-scale structure of materials. The pace of materials development has been further increased by high-throughput computational experiments guided by informatics and machine learning. We have previously demonstrated complementary approaches using mathematical optimization models to search through highly combinatorial design spaces of atomic arrangements, guiding the design of nanostructured materials. In this paper, we highlight the common features of materials optimization problems that can be efficiently modeled via mixed-integer linear optimization models. To take advantage of these commonalities, we have created MatOpt, a Python package that formalizes the process of representing the design space and formulating optimization models for the on-demand design of nanostructured materials. This tool serves to bridge the gap between practitioners with expertise in materials science and those with expertise in formulating and solving mathematical optimization models, effectively lowering the barriers for applying rigorous numerical optimization capabilities during nanostructured materials development.

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An approach based on genetic algorithms and machine learning coupled for studying alloy and molecular clusters by optimizing quantum energy surfaces

Rezende,

De Souza,

Belchior

2023

J Comput Chem

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A new genetic algorithm has been proposed focusing on direct ab initio potential energy surface (PES) global minima search. Besides the commonly used operators, this new approach uses an operator to: improve the initial cluster generation, classify and compare all generated clusters, and use machine learning to model the quantum PES used in parallel optimization. Part of the validation process for this methodology was done with 19,38,55) and Au n Ag n (n ¼ 10,20,30, 40,50,60,70, and 75). The results are in fair agreement with the literature and led to a new global minimum for Cu 12 Au 7 . A search has been done for the lowest energies of Li n nanoclusters with 2-8 atoms using the DFT approach and for Li 3 , Li 4 ,Li 2 H, Li 3 H using DLPNO-CCSD(T) approach. NQGA successfully performed the MP2 optimizations for ðH 2 OÞ 11 cluster. In all cases, the proposed genetic algorithm located the previously reported global minima with very efficient performance. The new proposed methodology makes it possible to optimize cluster geometries directly using high-level ab initio methods relinquishing any bias introduced by a classical approach. Our results show that this proposed method has great potential applications due to its flexibility and efficiency in identifying global minima in the tested atomic systems.

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Designing stable bimetallic nanoclusters via an iterative two-step optimization approach

Abstract: Determining the energetically most favorable structure of nanoparticles is a fundamentally important task towards understanding their stability. In the case of bimetallic nanoclusters, their vast configurational space makes it especially...

Cited by 6 publications

References 29 publications

Demystifying the Chemical Ordering of Multimetallic Nanoparticles

Demystifying the Chemical Ordering of Multimetallic Nanoparticles

MatOpt: A Python Package for Nanomaterials Design Using Discrete Optimization

An approach based on genetic algorithms and machine learning coupled for studying alloy and molecular clusters by optimizing quantum energy surfaces

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