Search for: All records

Lattice thermal conductivity is important for many applications, but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even unaffordable. Machine learning approaches that can achieve similar accuracy have been a long-standing open question. Despite recent progress, machine learning models using structural information as descriptors fall short of experimental or first principles accuracy. This study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles accuracy. The success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal resistance. Transfer learning between different orders of phonon scattering can further improve the model performance. Our surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.

Membrane filtration of feed containing multiple species of particles is a common process in the industrial setting. In this work, we propose a model for filtration of a suspension containing multiple particle species (concrete examples of our model are shown in two and three species), each with different affinities for the material of the porous filter membrane. Using the pore shape within the membrane as a design objective, we formulate a number of optimization problems pertaining to effective separation of desired and undesired particles in the special case of two-particle species and we present results showing how properties such as feed composition affect the optimal filter design. In addition, we propose a novel multi-stage filtration strategy, which provides a significant mass yield improvement for the desired particles, and, surprisingly, higher purity of the product as well.

The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn$$_2$$${}_2$O$$_4$$${}_4$chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97$$R^2$$$R^2$score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor‐intensive and time‐consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data‐driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials’ uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p‐type TE materials and 8 promising n‐type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials.