arXiv subjects

Janghoon Ock

Publications and source records attributed to Janghoon Ock.

15 recordsLinked to original sources

arXiv2026

Graph-Based Kirchhoff Modeling of Non-Ohmic Electron Transport in Self-Assembled Nanonecklace Networks

Gold nanonecklace networks are promising platforms for single-electron switching, chemical sensing, and biogating devices because of their nonlinear current--voltage ($I$--$V$) characteristics arising from collective Coulomb-blockade transport. However, the mechanisms governing this macroscopic behavior remain poorly understood because experimental measurements are generally limited to the network topology and global $I$--$V$ response. To address this, we developed a graph-based Kirchhoff framework that represents a self-assembled nanonecklace network as a graph, with nodes corresponding to junctions between necklace segments and edges to the conducting segments themselves. The solver returns the active nodes, conducting subgraph, nodal potentials, and edge currents at each applied bias, while allowing the activation-voltage statistics, network density, and structural topology to be varied independently. The model reproduces the experimentally observed non-Ohmic response, $I \propto (V-V_T)^{\zeta}$, and shows that this behavior emerges from the collective, staggered activation of threshold junctions and voltage-driven percolation of the conducting subgraph. Independent parameter sweeps reveal that the mean activation voltage shifts the threshold $V_T$ while leaving $\zeta$ nearly unchanged, increasing network density raises $\zeta$ from approximately 1.9 to 3.1 and enhances current, and topology controls the response even at fixed density and node characteristics. These trends agree qualitatively with experimental observations and establish the model as a design tool for engineering collective transport in self-assembled nanonecklace devices.

cond-mat.mes-hall↗

arXiv2026

Catalyst-Agent: Autonomous heterogeneous catalyst screening with an LLM Agent

The discovery of catalysts for electrochemical applications such as the oxygen reduction reaction (ORR), nitrogen reduction reaction (NRR), and CO2 reduction reaction (CO2RR) remains a central challenge in chemistry and materials science. Machine-learning interatomic potentials (MLIPs) and graph neural network models now accelerate individual adsorption-energy calculations by orders of magnitude relative to density functional theory. However, true large-scale screening is still blocked by human decisions: selecting candidates, constructing slabs, enumerating adsorption sites, interpreting descriptor failures, and choosing follow-up modifications. Here, we introduce Catalyst-Agent, a Model Context Protocol (MCP) server-based, LLM-powered agent that autonomously coordinates closed-loop catalyst screening. Catalyst-Agent searches materials databases through OPTIMADE, constructs slabs, computes adsorption energies using Meta FAIRchem's UMA MLIP within AdsorbML, evaluates reaction-specific descriptors, and applies structural modifications to refine near-miss candidates. In ORR, NRR, and CO2RR campaigns, Catalyst-Agent demonstrates high performance and converges in 1.40-3.41 trials per successful material on average. It identified Sn3Sc, Sn3Y, Tl3La, Pb3Y and In3Y as CO2RR candidates for further validation that were not previously reported in the literature. DFT single-point checks confirmed screening outcomes for representative NRR and CO2RR candidates. Ablations show these gains arise from chemically informed candidate selection and feedback-directed modification rather than brute-force evaluation: fully randomized screening dropped to 13.3%, 16.7%, and 0% success for ORR, NRR, and CO2RR, respectively. These results show that tool-grounded LLM agents can shift catalyst screening from manual trial-and-error toward more autonomous, reproducible and adaptive workflows.

cs.CL↗

arXiv2025

Meta-Learning for Cross-Task Generalization in Protein Mutation Property Prediction

Protein mutations can have profound effects on biological function, making accurate prediction of property changes critical for drug discovery, protein engineering, and precision medicine. Current approaches rely on fine-tuning protein-specific transformers for individual datasets, but struggle with cross-dataset generalization due to heterogeneous experimental conditions and limited target domain data. We introduce two key innovations: (1) the first application of Model-Agnostic Meta-Learning (MAML) to protein mutation property prediction, and (2) a novel mutation encoding strategy using separator tokens to directly incorporate mutations into sequence context. We build upon transformer architectures integrating them with MAML to enable rapid adaptation to new tasks through minimal gradient steps rather than learning dataset-specific patterns. Our mutation encoding addresses the critical limitation where standard transformers treat mutation positions as unknown tokens, significantly degrading performance. Evaluation across three diverse protein mutation datasets (functional fitness, thermal stability, and solubility) demonstrates significant advantages over traditional fine-tuning. In cross-task evaluation, our meta-learning approach achieves 29% better accuracy for functional fitness with 65% less training time, and 94% better accuracy for solubility with 55% faster training. The framework maintains consistent training efficiency regardless of dataset size, making it particularly valuable for industrial applications and early-stage protein design where experimental data is limited. This work establishes a systematic application of meta-learning to protein mutation analysis and introduces an effective mutation encoding strategy, offering transformative methodology for cross-domain generalization in protein engineering.

cs.LG↗

arXiv2025

LLM-guided Chemical Process Optimization with a Multi-Agent Approach

Chemical process optimization maximizes production efficiency and economic performance, but optimization algorithms, including gradient-based solvers, numerical methods, and parameter grid searches, become impractical when operating constraints are ill-defined or unavailable. We present a multi-agent LLM framework that autonomously infers operating constraints from minimal process descriptions, then collaboratively guides optimization. Our AutoGen-based framework employs OpenAI's o3 model with specialized agents for constraint generation, parameter validation, simulation, and optimization guidance. Through autonomous constraint generation and iterative multi-agent optimization, the framework eliminates the need for predefined operational bounds. Validated on hydrodealkylation across cost, yield, and yield-to-cost ratio metrics, the framework achieved competitive performance with conventional methods while reducing wall-time 31-fold relative to grid search, converging in under 20 minutes. The reasoning-guided search demonstrates sophisticated process understanding, correctly identifying utility trade-offs and applying domain-informed heuristics. Unlike conventional methods requiring predefined constraints, our approach uniquely combines autonomous constraint generation with interpretable parameter exploration. Model comparison reveals reasoning-capable architectures (o3, o1) are essential for successful optimization, while standard models fail to converge. This approach is particularly valuable for emerging processes and retrofit applications where operational constraints are poorly characterized or unavailable.

cs.LG↗

arXiv2025

Large Language Model Agent for Modular Task Execution in Drug Discovery

We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline. By combining LLM reasoning with domain-specific tools, the framework performs biomedical data retrieval, literature-grounded question answering via retrieval-augmented generation, molecular generation, multi-property prediction, property-aware molecular refinement, and 3D protein-ligand structure generation. The agent autonomously retrieved relevant biomolecular information, including FASTA sequences, SMILES representations, and literature, and answered mechanistic questions with improved contextual accuracy compared to standard LLMs. It then generated chemically diverse seed molecules and predicted 75 properties, including ADMET-related and general physicochemical descriptors, which guided iterative molecular refinement. Across two refinement rounds, the number of molecules with QED > 0.6 increased from 34 to 55. The number of molecules satisfying empirical drug-likeness filters also rose; for example, compliance with the Ghose filter increased from 32 to 55 within a pool of 100 molecules. The framework also employed Boltz-2 to generate 3D protein-ligand complexes and provide rapid binding affinity estimates for candidate compounds. These results demonstrate that the approach effectively supports molecular screening, prioritization, and structure evaluation. Its modular design enables flexible integration of evolving tools and models, providing a scalable foundation for AI-assisted therapeutic discovery.

cs.LG↗

arXiv2025

Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations

Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are critical but often computationally expensive. In this work, we employ GemNet-T, a graph neural network model, as an MLFF and investigate two training strategies: (1) direct training on MD17 (10K samples) without pre-training, and (2) pre-training on the large-scale OC20 dataset followed by fine-tuning on MD17 (10K). While both approaches achieve low force mean absolute errors (MAEs), reaching 5 meV/A per atom, we find that lower force errors do not necessarily guarantee stable MD simulations. Notably, the pre-trained GemNet-T model yields significantly improved simulation stability, sustaining trajectories up to three times longer than the model trained from scratch. By analyzing local properties of the learned force fields, we find that pre-training produces more structured latent representations, smoother force responses to local geometric changes, and more consistent force differences between nearby configurations, all of which contribute to more stable and reliable MD simulations. These findings underscore the value of pre-training on large, diverse datasets to capture complex molecular interactions and highlight that force MAE alone is not always a sufficient metric of MD simulation stability.

physics.chem-ph↗

arXiv2025

NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research

This paper presents the development and application of a Large Language Model Retrieval-Augmented Generation (LLM-RAG) system tailored for nanotechnology research. The system leverages the capabilities of a sophisticated language model to serve as an intelligent research assistant, enhancing the efficiency and comprehensiveness of literature reviews in the nanotechnology domain. Central to this LLM-RAG system is its advanced query backend retrieval mechanism, which integrates data from multiple reputable sources. The system retrieves relevant literature by utilizing Google Scholar's advanced search, and scraping open-access papers from Elsevier, Springer Nature, and ACS Publications. This multifaceted approach ensures a broad and diverse collection of up-to-date scholarly articles and papers. The proposed system demonstrates significant potential in aiding researchers by providing a streamlined, accurate, and exhaustive literature retrieval process, thereby accelerating research advancements in nanotechnology. The effectiveness of the LLM-RAG system is validated through rigorous testing, illustrating its capability to significantly reduce the time and effort required for comprehensive literature reviews, while maintaining high accuracy, query relevance and outperforming standard, publicly available LLMS.

cs.CL↗

arXiv2025

Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction

We investigate transformer-based language models, including RoBERTa, T5, Llama-3, and MatSciBERT, for predicting the band gaps of semiconductor materials directly from textual descriptions. The inputs encode key material features, such as chemical composition, crystal system, space group, and other structural and electronic properties. Unlike shallow machine learning models, which require extensive feature engineering, or Graph Neural Networks, which rely on graph representations derived from atomic coordinates, pretrained language models can process textual inputs directly, eliminating the need for manual feature preprocessing or structure-based encoding. Material descriptions were constructed in two formats: structured strings with a consistent template and natural language narratives generated via the ChatGPT API. Each model was augmented with a custom regression head and finetuned for band gap prediction task. Language models of different architectures and parameter sizes were all able to predict band gaps from human-readable text with strong accuracy, achieving MAEs in the range of 0.25-0.33 eV, highlighting the success of this approach for scientific regression tasks. Finetuned Llama-3, with 1.2 billion parameters, achieved the highest accuracy (MAE 0.248 eV, R2 0.891). MatSciBERT, pretrained on materials science literature, reached comparable performance (MAE 0.288 eV, R2 0.871) with significantly fewer parameters (110 million), emphasizing the importance of domain-specific pretraining. Attention analysis shows that both models selectively focus on compositional and spin-related features while de-emphasizing geometric features, reflecting the difficulty of capturing spatial information from text. These results establish that pretrained language models can effectively extract complex feature-property relationships from textual material descriptions.

cs.CL↗

arXiv2024

UniMat: Unifying Materials Embeddings through Multi-modal Learning

Materials science datasets are inherently heterogeneous and are available in different modalities such as characterization spectra, atomic structures, microscopic images, and text-based synthesis conditions. The advancements in multi-modal learning, particularly in vision and language models, have opened new avenues for integrating data in different forms. In this work, we evaluate common techniques in multi-modal learning (alignment and fusion) in unifying some of the most important modalities in materials science: atomic structure, X-ray diffraction patterns (XRD), and composition. We show that structure graph modality can be enhanced by aligning with XRD patterns. Additionally, we show that aligning and fusing more experimentally accessible data formats, such as XRD patterns and compositions, can create more robust joint embeddings than individual modalities across various tasks. This lays the groundwork for future studies aiming to exploit the full potential of multi-modal data in materials science, facilitating more informed decision-making in materials design and discovery.

cs.LG↗

arXiv2024

Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent

Adsorption energy is a key reactivity descriptor in catalysis. Determining adsorption energy requires evaluating numerous adsorbate-catalyst configurations, making it computationally intensive. Current methods rely on exhaustive sampling, which does not guarantee the identification of the global minimum energy. To address this, we introduce Adsorb-Agent, a Large Language Model (LLM) agent designed to efficiently identify stable adsorption configurations corresponding to the global minimum energy. Adsorb-Agent leverages its built-in knowledge and reasoning to strategically explore configurations, significantly reducing the number of initial setups required while improving energy prediction accuracy. In this study, we also evaluated the performance of different LLMs, including GPT-4o, GPT-4o-mini, Claude-3.7-Sonnet, and DeepSeek-Chat, as the reasoning engine for Adsorb-Agent, with GPT-4o showing the strongest overall performance. Tested on twenty diverse systems, Adsorb-Agent identifies comparable adsorption energies for 84% of cases and achieves lower energies for 35%, particularly excelling in complex systems. It identifies lower energies in 47% of intermetallic systems and 67% of systems with large adsorbates. These findings demonstrate Adsorb-Agent's potential to accelerate catalyst discovery by reducing computational costs and enhancing prediction reliability compared to exhaustive search methods.

cs.CL↗

arXiv2024

Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis

The increasing popularity of machine learning (ML) in catalysis has spurred interest in leveraging these techniques to enhance catalyst design. Our study aims to bridge the gap between physics-based studies and data-driven methodologies by integrating ML techniques with eXplainable AI (XAI). Specifically, we employ two XAI techniques: Post-hoc XAI analysis and Symbolic Regression. These techniques help us unravel the correlation between adsorption energy and the properties of the adsorbate-catalyst system. Leveraging a large dataset such as the Open Catalyst Dataset (OC20), we employ a combination of shallow ML techniques and XAI methodologies. Our investigation involves utilizing multiple shallow machine learning techniques to predict adsorption energy, followed by post-hoc analysis for feature importance, inter-feature correlations, and the influence of various feature values on the prediction of adsorption energy. The post-hoc analysis reveals that adsorbate properties exert a greater influence than catalyst properties in our dataset. The top five features based on higher Shapley values are adsorbate electronegativity, the number of adsorbate atoms, catalyst electronegativity, effective coordination number, and the sum of atomic numbers of the adsorbate molecule. There is a positive correlation between catalyst and adsorbate electronegativity with the prediction of adsorption energy. Additionally, symbolic regression yields results consistent with SHAP analysis. It deduces a mathematical relationship indicating that the square of the catalyst electronegativity is directly proportional to the adsorption energy. These consistent correlations resemble those derived from physics-based equations in previous research. Our work establishes a robust framework that integrates ML techniques with XAI, leveraging large datasets like OC20 to enhance catalyst design through model explainability.

cs.LG↗

arXiv2024

GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation

The exploration of molecular systems' potential energy surface is important for comprehending their complex behaviors, particularly through identifying various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational efforts. Our study introduces the GradNav algorithm, which enhances the exploration of the energy surface, accelerating the reconstruction of the potential energy surface (PES). This algorithm employs a strategy of initiating short simulation runs from updated starting points, derived from prior observations, to effectively navigate across potential barriers and explore new regions. To evaluate GradNav's performance, we introduce two metrics: the deepest well escape frame (DWEF) and the search success initialization ratio (SSIR). Through applications on Langevin dynamics within Mueller-type potential energy surfaces and molecular dynamics simulations of the Fs-Peptide protein, these metrics demonstrate GradNav's enhanced ability to escape deep energy wells, as shown by reduced DWEF values, and its reduced reliance on initial conditions, highlighted by increased SSIR values. Consequently, this improved exploration capability enables more precise energy estimations from simulation trajectories.

physics.chem-ph↗

arXiv2024

Multimodal Language and Graph Learning of Adsorption Configuration in Catalysis

Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determining the lowest energy across various adsorption configurations on a catalytic surface, which can exhibit very similar energy values. While graph neural networks (GNNs) have shown great success in computing the energy of catalyst systems, they rely heavily on atomic spatial coordinates. In contrast, transformer-based language models can directly use human-readable text inputs, potentially bypassing the need for detailed atomic positions. However, these language models often struggle with accurately predicting the energy of adsorption configurations. Our study addresses this limitation by introducing a self-supervised multi-modal learning approach called graph-assisted pretraining, which connects well-established GNNs with emerging language model applications. This method reduces the MAE of energy prediction for adsorption configurations by about 10%. Furthermore, our findings demonstrate that graph-assisted pretraining enhances fine-tuning with different datasets, indicating the transferability of this approach. This method also redirects the model's attention toward adsorption configuration, rather than individual adsorbate and catalyst information, similar to common domain knowledge. Building on this, we propose using generative large language models to create text inputs for the predictive model, based solely on chemical composition and surface orientation, without relying on exact atomic positions. This demonstrates a potential use case of language models in energy prediction without geometric information.

cs.CE↗

arXiv2023

Catalyst Property Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models

Efficient catalyst screening necessitates predictive models for adsorption energy, a key property of reactivity. However, prevailing methods, notably graph neural networks (GNNs), demand precise atomic coordinates for constructing graph representations, while integrating observable attributes remains challenging. This research introduces CatBERTa, an energy prediction Transformer model using textual inputs. Built on a pretrained Transformer encoder, CatBERTa processes human-interpretable text, incorporating target features. Attention score analysis reveals CatBERTa's focus on tokens related to adsorbates, bulk composition, and their interacting atoms. Moreover, interacting atoms emerge as effective descriptors for adsorption configurations, while factors such as bond length and atomic properties of these atoms offer limited predictive contributions. By predicting adsorption energy from the textual representation of initial structures, CatBERTa achieves a mean absolute error (MAE) of 0.75 eV-comparable to vanilla Graph Neural Networks (GNNs). Furthermore, the subtraction of the CatBERTa-predicted energies effectively cancels out their systematic errors by as much as 19.3% for chemically similar systems, surpassing the error reduction observed in GNNs. This outcome highlights its potential to enhance the accuracy of energy difference predictions. This research establishes a fundamental framework for text-based catalyst property prediction, without relying on graph representations, while also unveiling intricate feature-property relationships.

cs.CE↗

arXiv2023

Beyond Independent Error Assumptions in Large GNN Atomistic Models

The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and determining the lowest adsorption energy on a catalytic surface. Although Density Functional Theory (DFT) can effectively calculate relative values through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To investigate this issue, we analyzed approximately 483 million pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020 - Dense dataset. Our analysis revealed that GNNs exhibit a correlated error that can be reduced through subtraction, thereby challenging the naive independent error assumption in GNN predictions and leading to more precise energy difference predictions. To assess the magnitude of error cancellation in chemically similar pairs, we introduced a new metric, the subgroup error cancellation ratio (SECR). Our findings suggest that state-of-the-art GNN models can achieve error reduction up to 77% in these subgroups, comparable to the level of error cancellation observed with DFT. This significant error cancellation allows GNNs to achieve higher accuracy than individual adsorption energy predictions, which can otherwise suffer from amplified error due to random error propagation.

physics.comp-ph↗