arXiv subjects

Yongchul G. Chung

Publications and source records attributed to Yongchul G. Chung.

7 recordsLinked to original sources

arXiv2026

Hunting Structural Demons in Digital Reticular Chemistry: Lessons from Metal-Organic Frameworks

Digital reticular chemistry relies on accurate crystal structures to power computational screening, data-driven discovery, and structure-property analysis, yet recent studies reveal that more than half of the top-performing candidates in major computational screening campaigns are chemically invalid. In experimental MOF databases, structural errors arise when disordered or incomplete structural models are incorrectly converted into fully specified simulation inputs. In hypothetical MOF database, structures are complete by construction but may encode chemically implausible oxidation states, coordination environments, or charge distributions. We term these erroneous structural models "structural demons." This mini-review asks three questions: where these errors enter, how we find them, and how we prevent them. On the prevention side, the key steps are keeping diffraction data and synthesis details together from the start, using consistent curation when structures enter a database, and filtering topology choices before structure generation. Connecting these steps can keep many bad structures out of downstream databases and reduce the need to fix them later.

cond-mat.mtrl-sci↗

arXiv2026

Data-driven Prediction of Ionic Conductivity in Solid-State Electrolytes with Machine Learning and Large Language Models

Solid-state electrolytes (SSEs) are attractive for next-generation lithium-ion batteries due to improved safety and stability but their low room-temperature ionic conductivity hinders practical application. Experimental synthesis and testing of new SSEs remain time-consuming and resource intensive. Machine learning (ML) offers an accelerated route for SSE discovery; however, composition-only models neglect structural factors important for ion transport while graph neural networks (GNNs) are challenged by the scarcity of structure-labeled conductivity data and the prevalence of crystallographic disorder in CIFs. Here, we train two complementary predictors on the same room-temperature, structure-labeled dataset (n = 499). A gradient-boosted tree regressor (GBR) combining stoichiometric and geometric descriptors achieves best performance (MAE = 0.543 in log(S cm-1)), and Shapley Additive exPlanations (SHAP) identifies probe-occupiable volume (POAV) and lattice parameters as key correlations for conductivity. In parallel, we fine-tune large language models (LLMs) using compact text prompts derived from CIF metadata (formula with optional symmetry and disorder tags), avoiding direct use of raw atomic coordinates. Notably, Llama-3.1-8B-Instruct achieves high accuracy (MAE = 0.657 in log(S cm-1)) using formula and symmetry information, eliminating the need for numerical feature extraction from CIF files. Together, these results show that global geometric descriptors improve tree-based predictions and enable interpretable structure-property analysis, while LLMs provide a competitive low-preprocessing alternative for rapid SSE screening.

cond-mat.mtrl-sci↗

arXiv2025

Integrating Macrostate Probability Distributions with Swing Adsorption Modeling for Binary/Ternary Gas Separation

Accurate and efficient prediction of multicomponent adsorption equilibria across pressures, temperatures, and compositions remain a central challenge for designing energy-efficient adsorption-based separation processes. Traditional approaches, including model fitting and ideal adsorbed solution theory (IAST), often fail to balance accuracy, computational efficiency, and transferability under process-relevant conditions. Here, we introduce a material-to-process modeling framework that integrates macrostate probability distributions (MPDs) from flat-histogram Monte Carlo simulations with rigorous cyclic process optimization. MPDs directly capture the joint occupancy distributions of adsorbates, producing reweightable landscape that enables high-fidelity mixture adsorption equilibria without repeated simulations or model assumptions. We show that coupling this statistical mechanical foundation with process modeling delivers accurate and computationally efficient evaluations for binary and ternary gas mixture separations. This integration establishes MPD-based modeling as a generalized method for predictive multicomponent adsorption equilibria, accelerating the discovery and design of adsorbent materials for carbon capture and other separation challenges.

cond-mat.stat-mech↗

arXiv2025

AIM: A User-friendly GUI Workflow program for Isotherm Fitting, Mixture Prediction, Isosteric Heat of Adsorption Estimation, and Breakthrough Simulation

Adsorption breakthrough modeling often requires complex software environments and scripting, limiting accessibility for many practitioners. We present AIM, a MATLAB-based graphical user interface (GUI) application that streamlines fixed-bed adsorption modeling and analysis through an integrated workflow, which includes isotherm fitting, estimation of the enthalpy of adsorption, prediction of mixture behavior, and multicomponent breakthrough simulations. AIM supports 13 isotherm models for isotherm fitting and includes the implementation of Ideal Adsorbed Solution Theory (IAST) (FastIAS) and extended Langmuir models for predicting mixture isotherms. Moreover, the isotherm models can be used to run non-isothermal breakthrough simulations along with isosteric enthalpies of adsorption from the Clausius-Clapeyron and Virial equations. Users can export detailed column and outlet profiles (e.g., composition, temperature) in multiple formats, enhancing reproducibility and data sharing among practitioners. We compared the breakthrough simulation results from the AIM workflow and compared that with the experimental data in the literature for a ternary gas mixture (CO2/H2/N2) and found excellent agreement for outlet compositions and temperature profiles.

cond-mat.mtrl-sci↗

arXiv2025

Multiscale, Techno-economic Evaluation of Isoreticular Series of CALF-20 for Biogas Upgrading using a Pressure/Vacuum Swing Adsorption (PVSA) Process

Cyclic swing adsorption processes, such as pressure/vacuum swing adsorption (PVSA), are a promising technology for upgrading biogas by separating carbon dioxide (CO2) from methane (CH4). The rational design of adsorbent materials with tailored properties is important for the deployment of high-performance PVSA technology. Metal-organic frameworks (MOFs), particularly the CALF-20 isoreticular series, have attracted interest due to their high CO2 selectivity, thermal, and water stability. In this study, we report a multiscale assessment of CALF-20 and its isoreticular five derivatives by integrating molecular simulations with PVSA process optimization and techno-economic analysis. Structural and adsorption characteristics were calculated and employed to assess how each material performs in terms of energy efficiency and cost. The analysis reveals distinct differences in cost performance among the CALF-20 series, with CALF-20 showing the most favorable economics with \gt97\% purity CH4 production cost at \$4.31 per kg of CH4 and energy consumption of 9.35 kWh per kg of CH4. This study demonstrates that the integrated molecular-process optimization framework can effectively guide the search for adsorbent materials for biogas upgrading.

physics.chem-ph↗

arXiv2025

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks

The computational discovery and design of new crystalline materials, particularly metal-organic frameworks (MOFs), heavily relies on high-quality, computation-ready structural data. However, recent studies have revealed significant error rates within existing MOF databases, posing a critical data problem that hinders efficient high-throughput computational screening. While rule-based algorithms like MOSAEC, MOFChecker, and the Chen and Manz method (Chen-Manz) have been developed to address this, they often suffer from inherent limitations and misclassification of structures. To overcome this challenge, we developed MOFClassifier, a novel machine learning approach built upon a positive-unlabeled crystal graph convolutional neural network (PU-CGCNN) model. MOFClassifier learns intricate patterns from perfect crystal structures to predict a crystal-likeness score (CLscore), effectively classifying MOFs as computation-ready. Our model achieves a ROC value of 0.979 (previous best 0.912) and, importantly, can identify subtle structural and chemical errors that are undetectable by current rule-based methods. By accurately recovering previously misclassified false-negative structures, MOFClassifier reduces the risk of overlooking promising material candidates in large-scale computational screening campaigns. This user-friendly tool is freely available and has been integrated into the prepara-tion workflow for the updated CoRE MOF DB 2025 v1.0, contributing to accelerated computational discovery of MOF materials.

physics.chem-ph↗

arXiv2022

A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models

High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures that samples orders of magnitude more connectivity nets and inorganic building blocks than prior databases. This database shows an order of magnitude enrichment of ultrastable MOF structures that are stable upon activation and more than one standard deviation more thermally stable than the average experimentally characterized MOF. For the nearly 10,000 ultrastable MOFs, we compute bulk elastic moduli to confirm these materials have good mechanical stability, and we report methane deliverable capacities. Our work identifies privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.

cond-mat.mtrl-sci↗