A New Genetic Algorithm Approach Applied to Atomic and Molecular Cluster Studies

GAMaterial-A genetic-algorithm software for material design and discovery

Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti₆ O₁₂ cluster, doping Al in Si₁₁ (4Al@Si₁₁ ) and Na₁₀ supported on graphene (Na₁₀ @graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo₈ C₄ cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial.

Design, fabrication, and evaluation of a large-area hybrid solar simulator for remote sensing applications

Solar irradiance variations have a direct effect on the accuracy and repeatability of identifying spectral signatures in the remote sensing field experiments. Solar simulators have been deployed to allow for testing under controlled and reproducible laboratory conditions. However, it is difficult and expensive to make a large-area solar simulation with the appropriate spectral content and spatial uniformity of irradiance. In this study, a hybrid solar simulator has been designed and constructed to provide large-area illumination for remote sensing simulation applications. A design method based on the two-phase genetic algorithm is proposed to improve the performance of the spectral match and spatial uniformity, which no longer relies on the traditional trial-and-error technique. The first phase is used to determine the most appropriate configuration of different lamps in order to represent the solar spectrum. The second phase is to accommodate an optimal placement of the multiple sources to achieve irradiance uniformity. Both numerical simulations and experiments were performed to verify the performances. The results showed that the solar simulator provided a good spectral match and spatial irradiance for simulating the variations in direct normal irradiance at different solar zenith angles. In addition, the modular design makes it possible to adjust irradiance on the target area without altering the spectral distribution. This work demonstrates the development and measurement of a hybrid solar simulator with a realizable optimal configuration of multiple lamps, and offers the prospect of a scalable, large-area solar simulation.

Automatic structural elucidation of vacancies in materials by active learning

The artificial intelligence method based on active learning for the automatic structural elucidation of vacancies in materials. This is implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial).

How Many Isomers Do Metallic Clusters Have? Case of Magnesium Clusters of up to 55 Atoms

About 9000 structures of magnesium clusters Mg_n (n = 2-13) generated via different methods were optimized at the DFT levels in order to estimate the number of all possible stable structures that can exist for the given cluster size (∼820,000 PES points were explored in total). It was found that the number of possible cluster isomers N quickly grows with a number of atoms n; however, it is significantly lower than the number of possible nonisomorphic graph structures, which can be drawn for the given n. At the DFT potential energy surface, we found only 543 local minima corresponding to the isomers of Mg₂-Mg₁₃. The number of isomers obtained in the DFT optimizations grows with n approximately as n⁴, whereas the N values extrapolated to the infinite generation process grow as n⁸. The cluster geometries obtained from the global DFT optimization were then used to adjust two empirical potentials of Gupta type (GP) and modified Sutton-Chen type (SCG3) describing the interactions between the magnesium atoms. Using these potentials, the extensive sets of structures Mg₂-Mg₅₅ (up to 30,000 clusters for each n) were optimized to obtain the dependence of the cluster isomer count on n in the continuous range of n = 2-30 and for selected n up to n = 55. It was found that the SCG3 potential, which is closer to the DFT results, gives a number of possible isomers growing as approximately n^8.9, whereas GP potential results in the n^4.3 dependence.