arXiv subjects

Taku Watanabe

Publications and source records attributed to Taku Watanabe.

4 recordsLinked to original sources

arXiv2026

High-Throughput Computational Exploration of MOFs for Short-Chain PFAS Removal

Short-chain per- and polyfluoroalkyl substances (PFASs) are increasingly replacing regulated long-chain PFASs, yet they remain challenging to remove from water due to their high persistence, mobility, and weak affinity toward conventional adsorbents. In this work, we developed a hybrid high-throughput computational screening (HTCS) strategy to identify high-performance MOFs for the selective adsorption of perfluorobutanoic acid (PFBA), a representative short-chain PFAS, from water. The workflow begins with a curated MOF dataset and employs Monte Carlo (MC) simulations based on synergistic use of a classical universal force field (UFF) and a universal machine-learned interatomic potential (u-MLIP), enabling scalable and quantitatively accurate prediction of adsorption across large MOF databases. A set of promising MOFs initially identified using UFF-based HTCS, that combine strong PFBA affinity and high PFBA/H2O selectivity were re-evaluated with u-MLIP to refine adsorption predictions and to assess guest-induced framework flexibility, enabling the exclusion of materials with unfavourable high water-framework interactions. Ultimately, four high-performance MOFs were identified that optimally balance strong PFBA interactions, high PFBA selectivity over water, and practical considerations including sustainability, water stability, and synthetic feasibility. This study demonstrates that combining classical force fields with u-MLIPs enables scalable, quantitatively accurate MOF adsorption screening and establishes transferable principles for the rational design of adsorbents targeting short-chain PFAS.

cond-mat.mtrl-sci↗

arXiv2025

Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials

High-throughput computational screening (HTCS) of gas adsorption in metal-organic frameworks (MOFs) typically relies on classical generic force fields such as the Universal Force Field (UFF), which are efficient but often fail to capture complex host-guest interactions. Universal machine-learned interatomic potentials (u-MLIPs) offer near-quantum accuracy at far lower cost than density functional theory (DFT), yet their large-scale application in adsorption screening remains limited. Here, we present a hybrid screening strategy that merges Widom insertion Monte Carlo simulations performed with both UFF and the PreFerred Potential (PFP) u-MLIP to evaluate the adsorption performance of a large MOF database, using ethylene capture under humid conditions as a benchmark. From a curated set of MOFs, 88 promising candidates initially identified using UFF-based HTCS were re-evaluated with the PFP u-MLIP, benchmarked against DFT calculations to refine adsorption predictions and assess the role of framework flexibility. We show that PFP u-MLIP is essential to accurately assess the sorption performance of MOFs involving strong hydrogen bonding or confinement pockets within narrow pores, effects poorly captured using UFF. Notably, accounting for framework flexibility through full unit cell relaxation revealed deviations in ethylene affinity of up to 20 kJ mol-1, underscoring the impact of guest-induced structural changes. This HTCS workflow identified seven MOFs with optimal pore sizes, high ethylene affinity, and high C2H4/H2O selectivity, offering moisture-tolerant performance for applications from food packaging to trace ethylene removal. Our findings highlight the importance of accurately capturing host-guest energetics and framework flexibility, and demonstrate the practicality of incorporating u-MLIPs into scalable HTCS for identifying top MOF sorbents.

cond-mat.mtrl-sci↗

arXiv2022

Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements

Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. To overcome this issue, we have developed a universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO${}_4$F, molecular adsorption in metal-organic frameworks, an order-disorder transition of Cu-Au alloys, and material discovery for a Fischer-Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.

cond-mat.mtrl-sci↗

arXiv2016

Theoretical Study of Lithium Ionic Conductors by Electronic Stress Tensor Density and Electronic Kinetic Energy Density

We analyze the electronic structure of lithium ionic conductors, ${\rm Li_3PO_4}$ and ${\rm Li_3PS_4}$, using the electronic stress tensor density and kinetic energy density with special focus on the ionic bonds among them. We find that, as long as we examine the pattern of the eigenvalues of the electronic stress tensor density, we cannot distinguish between the ionic bonds and bonds among metalloid atoms. We then show that they can be distinguished by looking at the morphology of the electronic interface, the zero surface of the electronic kinetic energy density.

physics.chem-ph↗