High-Throughput Screening of Aperiodic Superlattices Based on Atomistic Simulation-Informed Effective Medium Theory and Genetic Algorithm

A physics rule to design aperiodic width-modulated waveguides for minimum phonon transmission with Bayesian optimization

“…Optimization of aperiodic hetero-SLs showed minimum coherent phonon conduction for a moderate disorder rather than for the maximum. This conclusion is supported by different studies based on atomistic calculations, atomistic Green's function (AGF), non-equilibrium Green's function (NEGF), molecular dynamics, and different machine learning optimization techniques such as Bayesian optimization, [43][44][45] genetic algorithms, 46,47,49 Monte Carlo tree search 48 and pattern analysis. 48 Table 2 compares optimal width-modulated nWVGs and hetero-SLs of the same number of unit multilayers and makes evident that nWVGs are optimized by maximum disorder as expected by the underlying physics whereas hetero-SLs are optimized by moderate disorder against physics intuition.…”

“…47 It was proposed to guess the optimal aperiodic configuration with pattern analysis based on a statistical analysis of configurations. 49 Optimal aperiodic hetero-SL configurations were though not unambiguously determined as different studies found different optimal configurations for the same type of hetero-SLs (Table 2). Importantly, our work reveals geometry-modulated nWVGs as a class of aperiodic metamaterials where optimal disorder can be unambiguously determined as the maximum disorder, the disorder being quantified by the number of non-identical modulation units.…”

“…Optimization of aperiodic hetero-SLs showed that coherent phonon conduction is minimized for moderate rather than maximum disorder against physics intuition. [43][44][45][46][47][48][49] Systematic investigation concluded that optimization could not reveal the underlying physics rule in hetero-SLs. 47 Notably, a physics rule was indicated in width-modulated nWVGs by the monotonic decrease of thermal conductance with increasing disorder in aperiodic modulation profiles.…”

“…[51][52][53][54] It has been applied to optimize coherent phonon transport in aperiodic hetero-SLs of two constituent materials. [43][44][45][46][47][48][49] ML has not been so far applied to optimize aperiodic width-modulated nWVGs, although they have been adequately investigated with the same computational physics techniques as hetero-SLs in the last decade. 13 Such optimization is very challenging because of the enormous number of possible configurations.…”

A brief review on the recent development of phonon engineering and manipulation at nanoscales

“…Nevertheless, it has always been a challenge to quickly obtain the optimal structure and chemical composition with target properties due to the fact that the design of nanostructures or atomic structures has the characteristics of large selectivity for parameters, high degree of freedom, and excessive time and economic costs associated with structure search and subsequent experimental verification. The emergence of materials informatics [188] can assist in material discovery and structure optimization gradually transforming from traditional and slow trialand-error strategies to efficient high-throughput screening from the pool of candidate materials [189], which have been applied in structural design in fields of thermal metamaterials [163,190,191], thermoelectric conversion [188,192,193], thermal manipulation [194,195], etc. Hu et al [190] used machine learning to search for globally optimal aperiodic superlattice structures with minimized coherent phonon heat conduction.…”