Building on Frenkel’s century-old theoretical foundation for understanding defects, modern computational methods for studying defects in semiconductors and insulators have evolved from the initial interpretive tools to predictive approaches capable of guiding technological applications. In this article, we examine the current state and future directions of computational approaches for studying point defects in semiconductors. Density functional theory (DFT) has become the primary tool for defect calculations, with hybrid functionals proving essential for accurately describing electronic structure and charge localization effects that standard DFT cannot capture. We discuss recent advances in treating excited states and calculating experimentally observable properties from first principles. Current methods can predict thermodynamic properties within 0.1 eV accuracy and luminescence spectra with meV precision through sophisticated electron–phonon coupling treatments. Emerging techniques include quantum-embedding methods and machine learning interatomic potentials that promise to extend current capabilities while reducing computational costs. Future developments in exchange–correlation functionals and beyond-DFT methods offer exciting possibilities for further advancing computational defect physics.
Abstract Agriculture depletes soil organic carbon (SOC), partly due to the exclusion of deep‐rooted perennials. Reintroducing deep‐rooted perennials to cultivated land may help to mitigate SOC loss. We quantified the effect of deep roots on SOC by comparing 8 to 30 year‐old stands of switchgrass (Panicum virgatum L.) with paired annual row crop fields at 12 sites across the central and eastern USA. We hypothesized that switchgrass would store more root C and SOC than neighboring shallow‐rooted annual crops, and that these effects would extend deeper than 30 cm. We also evaluated whether switchgrass stimulates decomposition of SOC at depth using radiocarbon (14C). Finally, we explored whether the effect of switchgrass on SOC is moderated by soil chemical and physical properties. While the effect of switchgrass on SOC in the surface 100 cm was positive at most sites, the average effect was not statistically significant (difference in SOC = 0.6 kg C m−2 [95% CI −0.8 to +1.9 kg C m−2]). By contrast, we found that root C was consistently more abundant under switchgrass, yielding an estimated additional 0.6 kg C m−2 in the surface 100 cm of soil [95% CI +0.5 to +0.7 kg C m−2]. 14C measurements suggested that root C inputs were adding to existing SOC without stimulating decomposition. The effect of switchgrass on belowground C was not strongly related to any of the soil properties that we evaluated. Our observations show that root C can contribute substantially to belowground C stocks when deep‐rooted perennials replace shallow‐rooted crops.
We compute nuclear response functions by solving the time-dependent A-body Schr & ouml;dinger equation, recording the time-dependent transition moment and extracting spectral information via Fourier transforms. The solution of the time-dependent many-body problem accounts for correlations on top of the mean field by taking advantage of a time-dependent formulation of coupled-cluster theory. As a validation, we focus on electric dipole transitions in 4He and 16O and compare moments of the response function distribution to the results of an equivalent static framework, finding negligible discrepancies. We investigate how proton and neutron densities evolve in time, and we see the traditional picture of soft and giant dipole resonances as collective oscillations of protons and neutrons emerging from our calculations in 16O and 24O. This method also allows us to investigate the behavior of the nucleus in the presence of a strong electric field. In that regime, the behavior of the system becomes chaotic. Qualitatively, the spectral information obtained in this limit is in line with previous time-dependent mean-field results.
In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79
Overlapping genes-wherein two different proteins are translated from alternative reading frames of the same DNA sequence-provide a means to stabilize an engineered gene by directly linking its evolutionary fate with that of an overlapping gene. However, creating overlapping gene pairs is challenging, as it requires redesigning both protein products to accommodate overlap constraints. Here, we present a new "overlapping, alternate-frame insertion" (OAFI) method for creating synthetic overlapping genes by inserting an "inner" gene, encoded in an alternate frame, into a flexible region of an "outer" gene. Using OAFI, we create new overlapping gene pairs of genetic reporters and bacterial toxins within an antibiotic resistance gene. We show that both the inner and outer genes retain function despite redesign, with translation of the inner gene influenced by its overlap position in the outer gene. Importantly, we show that, despite these inner gene sequences not contributing to outer gene function, selection for the outer gene alters the permitted inactivating mutations in the inner gene, and that overlapping toxins can restrict horizontal gene transfer of the antibiotic resistance gene. Overall, OAFI offers a versatile tool for synthetic biology, expanding the applications of overlapping genes in gene stabilization and biocontainment.IMPORTANCEGenetically engineered microbes promise to improve human health and help solve global climate crises. However, the widespread adoption of these microbes is often hindered by genetic instability caused by mutations and by the unpredictable spread of synthetic genes in the environment. We present a simple but effective method for creating synthetic overlapping genes to stabilize genes against mutations and prevent their spread in the environment. This method is broadly useful for constructing stable genetically engineered microbes and studying how they evolve in the environment.
