Chang Woo Myung

Equivariant networks with variational quantum circuit readouts yield an accurate hybrid potential for liquid Si, transferable from 3000 to 2000 K.

Chemiresistive sensors based on graphene derivatives and metal oxides continue to attract attention, yet achieving rapid and selective room‐temperature detection remains challenging due to sluggish surface kinetics and limited understanding of interfacial interactions. Here, we report a Bi ₂ MoO ₆ /reduced graphene oxide (rGO) heterostructure synthesized via hydrothermal synthesis and integrated onto interdigitated electrodes via blade coating. The device delivers outstanding ethanol sensing at 27°C, exhibiting a ∼16‐fold enhancement over pristine Bi ₂ MoO ₆ at 100 ppm, rapid 13/18 s response–recovery dynamics, a detection limit of 1.6 ppm, and excellent reproducibility and long‐term stability. A portable prototype module incorporating this sensor further demonstrates reliable real‐time ethanol monitoring, highlighting its practical applicability for environmental and breath analysis. To elucidate the origin of the unusual sensing behavior, we combine density functional theory (DFT) calculations with COMSOL‐based kinetic modeling. Despite its lowest adsorption energy among the tested analytes, ethanol induces the largest conduction‐band perturbation at the interface, whereas kinetic simulations reveal that weak adsorption accelerates adsorption–desorption turnover, thereby enabling frequent charge‐transfer events within the sensing timescale. This cooperative interplay between weak binding and dynamic interfacial modulation explains the exceptional selectivity and ultrafast response. These results establish Bi ₂ MoO ₆ /rGO as a mechanism‐guided platform for high‐performance room‐temperature gas sensing device development.

Machine learning interatomic potentials (MLIPs) provide a practical route to bridge first‐principles accuracy with the spatiotemporal scales required for modeling complex energy materials. By learning potential‐energy surfaces from density functional theory (DFT) data, MLIPs enable atomistic simulations up to 10 ⁴ –10 ⁶ atoms over nanosecond timescales, thereby extending access to phenomena inaccessible to ab initio molecular dynamics. This review presents a phenomenon‐oriented synthesis of MLIP development, spanning descriptor‐based models, Gaussian process frameworks with uncertainty quantification, equivariant graph neural networks, and foundation models trained on large‐scale datasets. We assess how architectural choices, training strategies (including active learning and multi‐fidelity approaches), and dataset design influence accuracy, transferability, and computational efficiency across representative energy systems. Applications in solid‐state electrolytes, battery electrodes, electrocatalysts, perovskite photovoltaics, and high‐entropy materials are discussed with both performance metrics and methodological rationale. While MLIPs capture qualitative trends and relative property variations, absolute predictions remain sensitive to DFT functional choices, dataset representativeness, and treatment of long‐range interactions. We address a structured validation framework linking model fidelity to experimentally relevant observables and system complexity. Also, we outline emerging directions including generative inverse design, autonomous discovery workflows, and foundation‐model fine‐tuning, highlighting opportunities and limitations for predictive deployment in energy materials research.

Two-dimensional (2D) indium selenide (In₂Se₃) has great potential for next-generation processing-in-memory applications owing to high intrinsic carrier mobility and strong ferroelectricity. However, the lack of wafer-scale, back-end-of-line (BEOL) compatible growth and inherent polarization-induced hysteresis limit their viability in logic circuits. Here, we report thermal evaporation of a non-ferroelectric κ-phase In₂Se₃ film that forms uniformly over 4-inch wafer-scale at κ-phase formation with an indirect bandgap of 1.45 eV and n-type conduction. The unique atomic arrangement suppresses spontaneous polarization, eliminating hysteresis in field-effect transistors (FETs). The κ-In₂Se₃ FETs exhibit enhancement-mode operation, high field-effect mobility of 39.3 cm² V⁻¹ s⁻¹, and stable switching over 1000 cycles, enabled by reduced trap states and contact resistance. By integrating κ-In₂Se₃ with a p-type selenium-alloyed tellurium oxide FET, we demonstrate a complementary inverter with full-swing operation and high voltage gain. These results establish κ-In₂Se₃ as a scalable 2D semiconductor platform for BEOL-compatible logic integration.

Proton conductors are essential for sustainable energy technologies, enabling efficient ion transport in electrochemical devices. Zr-based metal–organic polyhedra (MOPs) offer a distinctive platform as charge-neutral assemblies of cationic cages and Cl− counterions, creating intrinsic ionic microenvironment without acid doping. Here, we establish surface decoration as a powerful strategy to enhance proton transport in structurally distinct Zr-MOP solids, with the magnitude of enhancement dictated by packing structure. Li@UMOP-1 and Li@UMOP-11 exhibit superprotonic conductivities of 2.43 and 4.19 × 10−2 S cm−1, respectively, at 90% relative humidity (RH) and 95 °C, with Li@UMOP-11 representing the highest value reported to date for MOP-based proton conductors. Structural analyses and computations reveal that the topology-dependent positioning of Li+ ions governs water accessibility to the ionic sites. These findings establish packing-guided surface decoration as a design strategy for high-performance proton-conducting MOP solids.

ConspectusQuantum mechanical calculations predict atomic behavior with high accuracy but scale cubically, limiting routine applications to hundreds of atoms. This computational bottleneck prevents materials discovery at realistic scales where complex chemistry occurs. Designing next-generation batteries, solar cells, and catalysts requires predicting atomic behavior under diverse conditions, yet direct quantum simulations remain prohibitively expensive for such systems.Machine learning potentials offer a transformative approach by learning from limited quantum calculations to enable simulations that are orders of magnitude faster. This Account presents sparse Gaussian process regression (SGPR) as a statistically rigorous framework that addresses two critical challenges: achieving quantum-level accuracy from minimal training data and providing calibrated uncertainty estimates essential for exploring unknown chemical spaces. Unlike state-of-the-art graph neural network approaches that typically require 10,000-100,000+ training structures, while enabling simulations of up to ∼10⁴-10⁵ atoms, SGPR achieves practical accuracy with merely 100-1000 quantum calculations and a 10-100× reduction in data requirements, though more suited for focused chemical spaces (for systems < ∼3000 atoms) rather than broad multielement coverage.The SGPR framework exploits redundancy in chemical environments through intelligent rank reduction, selecting only the most informative local chemical environments as inducing points. On-the-fly adaptive sampling triggers new quantum calculations only when model uncertainty exceeds defined thresholds, ensuring efficient data generation. The robust Bayesian committee machine (RBCM) architecture partitions large models into specialized expert models for specific chemical compositions, then combines them through confidence-weighted schemes. This modular approach achieves O(nm²/p²) scaling (for n data points, m inducing sets, and p experts) and enables systematic expansion to complex multicomponent systems without retraining entire models.Applications demonstrate the framework's versatility across diverse materials challenges. For solid electrolytes, SGPR potentials trained on ∼100 selected structures achieved force prediction accuracy sufficient for reliable molecular dynamics, enabling simulations of Li₇P₃S₁₁ supercells with 10⁴× speedup over direct DFT. The calculations revealed high intrinsic conductivities in the tens of mS/cm range with activation energies consistent with NMR measurements. These simulated single-crystal values are around the upper limit of typical experimental measurements on polycrystalline samples, likely reflecting the absence of grain boundary resistance in idealized simulations. For perovskite solar cells achieving ∼26% efficiency, SGPR-MD simulations revealed that SnO₂/perovskite interfaces spontaneously form stabilizing FASnCl₃ interlayers─mechanistic insights requiring extensive configurational sampling beyond static DFT capabilities. In electrocatalysis, screening identified Pt-C₂N₂ single-atom catalysts with overpotentials of ∼30 mV for chlorine evolution, significantly outperforming industrial catalysts (∼100 mV). For organic systems, unified potentials for hydrocarbons achieved subkcal/mol accuracy for π-π stacking interactions and correctly predicted polymer folding dynamics where empirical force fields systematically fail.The SGPR-RBCM framework offers distinct advantages when training data is limited, uncertainty quantification is critical, and focused chemical accuracy is prioritized. By enabling quantum-accurate simulations at near-classical computational costs, these methods accelerate high-throughput screening across diverse materials spaces. The modular expert architecture creates pathways toward comprehensive potentials that could transform materials discovery for clean energy, electronics, and sustainable chemistry applications.

Large-scale simulations of atomic systems play a vital role in fields, including chemistry, materials science, and more. However, they often struggle with efficiency due to high computational costs. To overcome this, machine learning force fields (MLFFs) have emerged as a promising alternative, providing accuracy comparable to density functional theory (DFT) while lowering costs. Nonetheless, variations in potential energy based on atomic environments make it challenging to apply kernel-based models across different compounds. To address this issue, we present an MLFF using the Robust Bayesian Committee Machine (RBCM) for various compounds containing carbon, nitrogen, and hydrogen (C-N-H). The MLFF is trained with first-principles calculations and molecular dynamics simulations of various C-N-H molecules. Testing these models with longer amine structures and two Diels-Alder reactions shows excellent agreement with DFT results, demonstrating that machine learning models accurately predict potential energy surfaces of organic molecules and offer opportunity for studying a wide range of C-N-H compounds.

Incorporating a p-block B atom into B-2TM@NC disrupts bridging-CO adsorption, facilitating C–C coupling beyond conventional bi-metallic designs.

Quantum algorithms for simulating large and complex molecular systems are still in their infancy, and surpassing state-of-the-art classical techniques remains an ever-receding goal post. A promising avenue of inquiry in the meanwhile is to seek practical advantages through hybrid quantum-classical algorithms, which combine conventional neural networks with variational quantum circuits (VQCs) running on today's noisy intermediate-scale quantum (NISQ) hardware. Such hybrids are well suited to NISQ hardware. The classical processor performs the bulk of the computation, while the quantum processor executes targeted sub-tasks that supply additional non-linearity and expressivity. Here, we benchmark a purely classical E(3)-equivariant message-passing machine learning potential (MLP) against a hybrid quantum-classical MLP for predicting density functional theory (DFT) properties of liquid silicon. In our hybrid architecture, every readout in the message-passing layers is replaced by a VQC. Molecular dynamics simulations driven by the HQC-MLP reveal that an accurate reproduction of high-temperature structural and thermodynamic properties is achieved with VQCs. These findings demonstrate a concrete scenario in which NISQ-compatible HQC algorithm could deliver a measurable benefit over the best available classical alternative, suggesting a viable pathway toward near-term quantum advantage in materials modeling.