Jihan Kim

Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. Here, we have implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 121 crystalline porous materials. Our neural network ta

In the past decade, there has been an increasing number of computational screening works to facilitate finding optimal materials for a variety of different applications. Unfortunately, most of these screening studies are limited to their initial set of materials and result in a brute-force type of screening approach. In this work, we present a systematic strategy that can find metal-organic frameworks (MOFs) with the desired properties from an extremely diverse and large set of over 100 trillion

Large-scale simulations of aluminosilicate zeolites were conducted to identify structures that possess large CO(2) uptake for postcombustion carbon dioxide capture. In this study, we discovered that the aluminosilicate zeolite structures with the highest CO(2) uptake values have an idealized silica lattice with a large free volume and a framework topology that maximizes the regions with nearest-neighbor framework atom distances from 3 to 4.5 Å. These predictors extend well to different Si:Al rat

We have conducted large-scale screening of zeolite materials for CO2/CH4 and CO2/N2 membrane separation applications using the free energy landscape of the guest molecules inside these porous materials. We show how advanced molecular simulations can be integrated with the design of a simple separation process to arrive at a metric to rank performance of over 87,000 different zeolite structures, including the known IZA zeolite structures. Our novel, efficient algorithm using graphics processing u

The concept of integrating diverse functional 2D materials into a heterostructure provides platforms for exploring physics that cannot be accessed in a single 2D material. Here, physically mixing two 2D materials, MXene and MoS₂, followed by freeze-drying is utilized to successfully fabricate a 3D MoS₂/MXene van der Waals heterostructure aerogel. The low-temperature synthetic approach effectively suppresses significant oxidation of the Ti₃C₂T<sub><i>x<

Large-scale computational screening of thirty thousand zeolite structures was conducted to find optimal structures for separation of ethane/ethene mixtures. Efficient grand canonical Monte Carlo (GCMC) simulations were performed with graphics processing units (GPUs) to obtain pure component adsorption isotherms for both ethane and ethene. We have utilized the ideal adsorbed solution theory (IAST) to obtain the mixture isotherms, which were used to evaluate the performance of each zeolite structu

Though highly promising as powerful gas sensors, oxide semiconductor chemiresistors have low surface reactivity, which limits their selectivity, sensitivity, and reaction kinetics, particularly at room temperature (RT) operation. It is proposed that a hybrid design involving the nanostructuring of oxides and passivation with selective gas filtration layers can potentially overcome the issues with surface activity. Herein, unique bi-stacked heterogeneous layers are introduced; that is, nanostruct

We have developed a high-throughput graphics processing unit (GPU) code that can characterize a large database of crystalline porous materials. In our algorithm, the GPU is utilized to accelerate energy grid calculations, where the grid values represent interactions (i.e., Lennard-Jones + Coulomb potentials) between gas molecules (i.e., CH4 and CO2) and materials' framework atoms. Using a parallel flood fill central processing unit (CPU) algorithm, inaccessible regions inside the framework struc

This research focused on the efficient collection of experimental metal-organic framework (MOF) data from scientific literature to address the challenges of accessing hard-to-find data and improving the quality of information available for machine learning studies in materials science. Utilizing a chain of advanced large language models (LLMs), we developed a systematic approach to extract and organize MOF data into a structured format. Our methodology successfully compiled information from more