Oleksandr Voznyy

Indium antimonide (InSb) colloidal quantum dots (CQDs) offer a heavy‐metal‐free route to short‐wave infrared (SWIR) photodetection, but their performance still lags behind lead‐ and mercury‐based CQD systems, and the surface chemistry that limits their performance is not well understood. Here, we identify two independent oxide‐derived trap pathways on the polar terminations of zincblende InSb: oxidation of the In‐terminated {111}A and Sb‐terminated {111}B facets creates chemically distinct mid‐gap states, each of which degrades carrier transport. Density functional theory calculations show that metal halides bind strongly to Sb‐terminated facets, but weakly to In‐terminated facets, while thiolates show the opposite preference; this asymmetry leaves single‐ligand passivation incomplete. Exploiting this orthogonality, we develop a facet‐specific exchange that passivates both terminations and removes their surface oxides in a single step. The resulting CQD films show a fivefold reduction in hole‐trap density and a sixfold improvement in hole mobility relative to thiol‐only controls. The photodetectors achieve 50% external quantum efficiency under 1 V reverse bias at 1390 nm, to the best of our knowledge, the highest reported for InSb CQD photodetectors in the 1300–1500 nm range, along with a 580 ns fall time and 95% photocurrent retention after 100 h of continuous operation.

Electrochemical carbon capture (ECC) with quinones is a promising, energy-efficient alternative to thermal systems, but oxygen sensitivity remains a significant challenge. The persistent trade-off between CO2 affinity (KCO2) and reduction potentials (Ered) requires identifying “borderline” species amenable to system-level optimization. Using density functional theory (DFT), we screened 31,000 quinones and identified 681 candidates with parameters suitable for oxygen-tolerant ECC. Our screening revealed unexplored structural motifs, including heterocyclic and extended aromatic systems, alongside intramolecular H-bonding effects. We also identified functional groups prone to structural fragmentation upon CO2 binding, which are unsuitable for durable operation despite favorable potentials. Finally, synthetic feasibility was assessed via a dual-metric approach, narrowing the selection to 20 high-priority quinones for experimental validation. This study provides a rational design roadmap for high-performance organic ECC sorbents.

Raising the upper cutoff voltage and deploying Ni-rich layered oxides (e.g., NMC811) are promising routes to higher lithium-ion cell energy density. However, the origin of degradation remains unclear at high-voltage operation. We propose using a cycle-relevant scan rate (1.0 mV s−1) cyclic voltammetry in a three-electrode cell, combined with post-cycled 1H NMR, ex-situ XRD, SEM, and XRF to decouple intrinsic cathode and electrolyte degradation contributions. Comparing to controls (glassy-carbon working electrode; 0.5M LiTFSI in PC electrolyte), we show that conventional electrolyte LiPF6/EC/DMC undergoes oxidative decomposition at ≥4.2 V vs Li/Li+ on an inert electrode, producing characteristic decomposition products that are reproduced when NMC811 is cycled in the same electrolyte. By contrast, NMC811 in LiTFSI/PC shows only minor solvent oxidation to 4.3 V but rapid surface reconstruction and rock-salt formation by 4.5 V. Critically, NMC811 coupled with LiPF6/EC/DMC at 4.3V exhibits rapid electrochemical decay (oxidation-peak current loss 68 ± 13% after 20 cycles) and accelerated appearance of intermediate superlattice phases by XRD. Together, these results identify electrolyte oxidative instability as a primary trigger for degradation near 4.3 V and implicate synergistic cathode-electrolyte mechanisms at higher voltages, motivating the design of higher-voltage stability electrolytes and cathode surface protection for durable Ni-rich cathode batteries.

Identifying materials with strong performance in a specific application, particularly when the origin of that performance is unclear or difficult to compute, remains a central challenge in materials science. Trial-and-error exploration is prohibitively expensive due to the vastness of the materials space. A more practical strategy is to search for new materials within the proximity of known compounds exhibiting the desired property. This requires defining a meaningful representation to assess materials similarity. Structural fingerprints are commonly used, yet structural similarity often fails to translate into similarity in properties. Electronic fingerprints, such as density of states or band structure, were proposed as a better alternative, but computing them for >100 000 materials is still too costly for rapid screening. Here, we present ProDosNet, a graph convolutional network trained on atom- and orbital-resolved projected density of states (PDOS) data, capable of predicting electronic structure at extremely low computational cost. Using this model, we generated PDOS fingerprints for all compounds in the Materials Project database and clustered them by orbital-resolved PDOS similarity. We demonstrate that these electronic fingerprints allow finding materials with similar electronic properties but drastically different structures for applications in photovoltaics, catalysis, and batteries.

As a core technology for industrial chemical synthesis and electrochemical energy storage, electrocatalysis is central to decarbonization strategies aimed at combating climate change. In this context, computational and machine learning driven catalyst discovery has emerged as a major research focus. These approaches frequently use the thermodynamic overpotential, calculated from adsorption free energies of reaction intermediates, as a key parameter in their analysis. In this paper, we explore the large-scale applicability of such overpotential estimates for identifying good catalyst candidates by using datasets from the Open Catalyst Project (OC20 and OC22). We start by quantifying the uncertainty in predicting adsorption energies using ab initio methods and find that, depending on the type of surface, the uncertainty is approximately 0.3–0.5 eV for a single adsorption energy prediction. We then compute the overpotential of all materials in the OC20 and OC22 datasets for the hydrogen and oxygen evolution reactions. We find that while the overpotential allows the identification of known good catalysts such as platinum and iridium oxides, the uncertainty is large enough to misclassify a broad fraction of the datasets as “good”, which limits its value as a screening criterion. These results question the reliance on overpotential estimation as a primary evaluation metric to sort through catalyst candidates and emphasize the importance of using other metrics such as synthesizability, stability, lifetime, or affordability.

The central dogma of materials informatics is the "Scaling Hypothesis": the assumption that chemical space is a continuous manifold, and that, with sufficient data, deep learning models can approximate any structure-property relationship. While this strategy has achieved near-DFT accuracy for thermodynamic properties like formation energy, prediction accuracy for bandgaps suffers from a fundamental, non-converging error floor (~0.4 eV). By analyzing a comprehensive dataset of binary semiconductors and state-of-the-art Graph Neural Networks (including coGN and foundational embeddings), this work isolates the root cause: unlike the smooth, transferable landscape of total energy, the electronic bandgap manifold is topologically disjoint. Consequently, increasing dataset size fails to improve predictions, instead resulting in negative learning, where increasing the structural diversity of the training set (e.g., adding rocksalt data to zincblende models) degrades prediction accuracy for unseen chemistries. This can be traced back to the discrete quantization of atomic orbitals and the resulting band crossings, which create non-differentiable cusps in the property surface. This work establishes a boundary condition for the "Big Data" paradigm: simple regression on atomic coordinates cannot resolve the intrinsic discontinuities of eigenvalue phenomena. Instead, a more detailed integration of quantum mechanics is required, such as direct entrainment of Hamiltonians as descriptors.

Lithium dendrite growth at the lithium–solid-state electrolyte (SSE) interface, driven by void formation, remains a major barrier to the deployment of Li metal solid-state batteries. Here, we reveal a mechanism of lateral lithium flux that explains how interfacial layers (ILs) along the Li/SSE interface enable suppression of void formation, thereby preventing dendrite growth. Using Li₃Sb as a model IL and Li₇La₃Zr₂O₁₂ as a benchmark SSE, we directly visualize and quantify lateral lithium transport, demonstrating that ILs mitigate void formation by defocusing the local current density. This mechanism accounts for the cycling stability of IL-functionalized Li/SSE interfaces and provides design principles for safe, high-energy-density Li metal solid-state batteries.

Thermal activation of reactants is a key initial step in catalysis, which can occur either by temperature-driven activation of surface adsorbates or by radiation-driven excitation above the surface. Here, two configurations of five-bilayer Ni/SiO₂ thermal metasurfaces are engineered to provide superblackbody near-field emission within the CO₂ asymmetric stretching band of 4.3 μm wavelength, with broadband and narrowband absorption, respectively. Thermal dipole simulations show that the broadly absorbing cylindrical array drives edge-guided resonances with a near-uniform rim field, whereas the narrowly emitting cuboid array with higher absorptivity shows corner field localization due to higher field interior confinement. Both metasurfaces have high near-field intensities in excess of blackbody radiation by up to ×2. Experimentally, the wideband metasurface achieves a higher CO production rate and >50% higher CO/CH₄ selectivity. In situ-diffused reflectance Fourier transform spectroscopy indicates reduced buildup of carbonate intermediates and adsorbed CO on the wideband metasurface relative to the narrowband metasurface, indicating faster CO desorption and suppressed methanation. Density functional theory simulations support the observation by showing that gas CO₂ bond stretching can eliminate the kinetic energy barrier to CO₂ chemisorption and promote CO₂ dissociation toward CO formation. Minimizing CO adsorbate stretching suppresses hydrogenation pathways that proceed through formate intermediate and subsequent methanation. Overall, the engineering of the metasurface thermal dipole-induced field coupling with confinement or radiative channels, modulated by emission spatial distribution, can independently activate reactant molecules above the surface while favoring the surface for desorption of products. This shows a physically driven approach to catalysis by decoupling the surface activation and desorption counteractions.

In atomistic simulation, ab initio methods are accurate but too computationally expensive for large systems, long trajectories, or high-throughput screening. Recently, machine-learned interatomic potentials (MLIPs) are approaching the accuracy of ab initio methods at speeds closer to traditional force fields by training on large datasets of ab initio results. Some datasets include atomic partial charges, which are convenient for training models with explicit charge assignments. We propose adding diversity to the training data by perturbing atomic charge, and annotating atoms with corresponding energy derivatives. These additional atom-level labels are identical to electronegativity as defined in conceptual density functional theory. To demonstrate this proposal, we construct a training dataset of crystals with two-atom unit cells, controlling the charge transfer between the atoms by imposing an external potential. By matching the form of the potential to the charge partitioning scheme, we compute the electronegativity difference from the strength of the imposed potential, and observe a near-linear relationship with charge transfer. Fitting these electronegativity differences with linear regression yields the parameters of a charge equilibration model. Our results demonstrate a new way of training charge prediction models, and show how to diversify MLIP training datasets while simultaneously adding atom-level response properties that can be used as training targets.