arXiv subjects

Sjoerd Hoogland

Publications and source records attributed to Sjoerd Hoogland.

3 recordsLinked to original sources

arXiv2026

High-magnitude, spatially and directionally programmable, and sustained strain engineering of 2D semiconductors

Crystalline two-dimensional (2D) semiconductors often combine high elasticity and in-plane strength, making them ideal for strain-induced tuning of electronic characteristics, akin to strategies used in silicon electronics. However, existing techniques have not achieved strain in 2D materials that is simultaneously high in magnitude (>1%), stable over long periods, and spatially and directionally programmable. In this context, programmable strain means that the strain level and direction can be deterministically engineered across a single layer. Here, we apply spatially programmable biaxial strain (e_b) up to ~2.2% with a spatial gradient of ~0.1 %e_b um-1 in monolayer MoS2 via conformal transfer onto patterned substrates fabricated using two-photon lithography. The induced e_b is stable for months and enables local band gap tuning of ~0.4 eV in monolayer MoS2, ~25% of its intrinsic band gap. Further, by tailoring substrate topography, we introduce uniaxial (anisotropic) strain, demonstrating control over both strain magnitude and direction. We also extend this strain engineering framework to MoS2-WS2 bilayer heterostructures. Overall, we introduce a distinct regime of strain-enabled control in 2D semiconductors to support the development of optoelectronics and nanoelectronics with engineered optical and electronic landscapes.

cond-mat.mtrl-sci↗

arXiv2026

Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs

Machine learning interatomic potentials (MLIPs) require generating computationally expensive, large-scale training datasets to accurately simulate materials and molecules. Incorporating electronic structure information using multitask learning improves sample efficiency, however, training on full Hamiltonian matrices, which scale quadratically with the number of atoms, is intractable for large datasets. In this work, we show that multitask learning utilizing orbitally resolved semiempirical charges significantly improves sample efficiency and accuracy in MLIPs. To efficiently predict orbital charges, we implement a specialized equivariant model, reducing charge prediction error compared to an invariant baseline. By augmenting training with computationally inexpensive GFN1-xTB orbital charges, which scale linearly with the number of atoms, our model achieves a 46\% reduction in energy mean absolute error and requires five times less data to match the performance of energy-only models. Furthermore, our approach outperforms models trained on expensive density functional theory (DFT) atomic charges, capturing orbitally resolved electronic complexity and forcing the network to learn a physically accurate latent space that spontaneously clusters metals by shared chemical properties. Because orbital charges are only required during training, this approach preserves inference efficiency, providing a scalable recipe for developing accurate, data-efficient foundation models for complex chemical systems.

physics.chem-ph↗

arXiv2024

Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models

The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new catalysts is currently limited by the gap between what AI-accelerated computational models predict and what experimental studies produce. To make progress, large and diverse experimental datasets are needed that are reproducible and tested at industrially-relevant conditions. We address these needs by utilizing a comprehensive high-throughput characterization and experimental pipeline to create the Open Catalyst Experiments 2024 (OCX24) dataset. The dataset contains 572 samples synthesized using both wet and dry methods with X-ray fluorescence and X-ray diffraction characterization. We prepared 441 gas diffusion electrodes, including replicates, and evaluated them using zero-gap electrolysis for carbon dioxide reduction (CO$_2$RR) and hydrogen evolution reactions (HER) at current densities up to $300$ mA/cm$^2$. To find correlations with experimental outcomes and to perform computational screens, DFT-verified adsorption energies for six adsorbates were calculated on $\sim$20,000 inorganic materials requiring 685 million AI-accelerated relaxations. Remarkably from this large set of materials, a data driven Sabatier volcano independently identified Pt as being a top candidate for HER without having any experimental measurements on Pt or Pt-alloy samples. We anticipate the availability of experimental data generated specifically for AI training, such as OCX24, will significantly improve the utility of computational models in selecting materials for experimental screening.

cond-mat.mtrl-sci↗