相关论文: Reexamining Machine Learning Models on Predicting …
Machine Learning (ML) has the potential to accelerate discovery of new materials and shed light on useful properties of existing materials. A key difficulty when applying ML in Materials Science is that experimental datasets of material…
The advent of material databases provides an unprecedented opportunity to uncover predictive descriptors for emergent material properties from vast data space. However, common reliance on high-throughput ab initio data necessarily inherits…
It is well known that the efficiency of a good thermoelectric material should be optimized with respect to doping concentration. However, much less attention has been paid to the optimization of the dopant's energy level. Thermoelectric…
High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in…
A numerical study of thermoelectric nanocomposites is presented. Thermoelectric properties as a function of average grain size or nanoparticle density are examined by simulating the measurements as they would be done experimentally. In…
Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, $ZT$. To accelerate the discovery of high-$ZT$ materials, efforts have focused on identifying compounds with low thermal…
Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting data-driven method that could generally improve the…
Thermoelectric semiconducting materials are often evaluated by their figure-of-merit, zT. However, by using zT as the metric for showing improvements, it is not immediately clear whether the improvement is from an enhancement of the…
This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and…
In this Letter, we show thermoelectric conversion efficiency is \emph{exactly} determined by \emph{three} independent material parameters $Z_{\rm gen}$, $\tau$, and $\beta$. Each parameter is a figure of merit hence improving $\tau$ or…
Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding…
Thermoelectric devices can directly convert waste heat into electricity, which makes them an important clean energy technology. The underlying materials performance can be evaluated by the dimensionless figure of merit ZT. Metal halides are…
SnTe is a potential thermoelectric material in the mid temperature range. Detailed techniques to enhance the figure of merit by increasing the Power Factor, and reducing thermal conductivity, of SnTe-based TE materials are discussed. The…
The effect of interface scattering on the performance of disordered, nanocomposite thermoelectric materials is studied theoretically using effective medium theory and direct numerics. The interfacial electronic and phonon scattering…
Nanostructuring has spurred a revival in the field of direct thermoelectric energy conversion. Nanograined materials can now be synthesized with higher figures of merit (ZT) than the bulk counterparts. This leads to increased conversion…
We study the thermopower of a junction between a metal and a strongly correlated semiconductor. Both in the electronic ferroelectric regime and in the Kondo insulator regime the thermoelectric figures of merit, ZT, of these junctions are…
Enhancing the dimensionless figure of merit zT is central to developing better thermoelectric materials and advancing thermoelectric generation technology. However, the intrinsic interdependence between electrical conductivity, the Seebeck…
The limited extrapolative power of structure-based machine learning (ML) models is a critical bottleneck in chemical discovery, particularly for industrial R&D, where navigating uncharted chemical space to find next-generation materials or…
Owing to its high scalability and computational efficiency, machine learning methods have been increasingly integrated into various scientific research domains, including ab initio-based materials design. It has been demonstrated that, by…
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…