Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data

Large-area processing remains a key challenge for perovskite solar cells (PSCs). Advanced understanding and improved reproducibility of scalable fabrication processes are required to unlock the technology’s economic potential. In this regard, machine learning (ML) methods have emerged as a promising tool to accelerate research and unlock the control needed to produce large-area solution-processed perovskite thin-films. However, a suitable dataset allowing the analysis of a scalable fabrication process is currently missing. In this work, a unique labeled in situ photoluminescence (PL) dataset for blade-coated PSCs is introduced and explored with unsupervised k-means clustering, demonstrating the feasibility to derive meaningful insights from such data. Correlations between the obtained clusters and the measured performance of PSC reveal that the in situ PL signal encodes information about the perovskite thin-film quality. Detrimental mechanisms during thin-film formation are detected by identifying spatial differences in PL patterns and, consequently, of device performance. In addition, k-nearest neighbors is applied to predict the performance of PSCs, motivating further investigations into ML-based in-line process monitoring of scalable PSC fabrication to detect, understand, and ultimately minimize process variations across iterations

Photoassociation and coherent transient dynamics in the interaction of ultracold rubidium atoms with shaped femtosecond pulses - I. Experiment

We experimentally investigate various processes present in the photoassociative interaction of an ultracold atomic sample with shaped femtosecond laser pulses. We demonstrate the photoassociation of pairs of rubidium atoms into electronically excited, bound molecular states using spectrally cut femtosecond laser pulses tuned below the rubidium D1 or D2 asymptote. Time-resolved pump-probe spectra reveal coherent oscillations of the molecular formation rate, which are due to coherent transient dynamics in the electronic excitation. The oscillation frequency corresponds to the detun-ing of the spectral cut position to the asymptotic transition frequency of the rubidium D1 or D2 lines, respectively. Measurements of the molecular photoassociation signal as a function of the pulse energy reveal a non-linear dependence and indicate a non-perturbative excitation process. Chirping the association laser pulse allowed us to change the phase of the coherent transients. Furthermore, a signature for molecules in the electronic ground state is found, which is attributed to molecule formation by femtosecond photoassociation followed by spontaneous decay. In a subsequent article [A. Merli et al., submitted] quantum mechanical calculations are presented, which compare well with the experimental data and reveal further details about the observed coherent transient dynamics

Association Between Intravenous Thrombolysis and Clinical Outcomes Among Patients With Ischemic Stroke and Unsuccessful Mechanical Reperfusion.

IMPORTANCE Clinical evidence of the potential treatment benefit of intravenous thrombolysis preceding unsuccessful mechanical thrombectomy (MT) is scarce. OBJECTIVE To determine whether intravenous thrombolysis (IVT) prior to unsuccessful MT improves functional outcomes in patients with acute ischemic stroke. DESIGN, SETTING, AND PARTICIPANTS Patients were enrolled in this retrospective cohort study from the prospective, observational, multicenter German Stroke Registry-Endovascular Treatment between May 1, 2015, and December 31, 2021. This study compared IVT plus MT vs MT alone in patients with acute ischemic stroke due to anterior circulation large-vessel occlusion in whom mechanical reperfusion was unsuccessful. Unsuccessful mechanical reperfusion was defined as failed (final modified Thrombolysis in Cerebral Infarction grade of 0 or 1) or partial (grade 2a). Patients meeting the inclusion criteria were matched by treatment group using 1:1 propensity score matching. INTERVENTIONS Mechanical thrombectomy with or without IVT. MAIN OUTCOMES AND MEASURES Primary outcome was functional independence at 90 days, defined as a modified Rankin Scale score of 0 to 2. Safety outcomes were the occurrence of symptomatic intracranial hemorrhage and death. RESULTS After matching, 746 patients were compared by treatment arms (median age, 78 [IQR, 68-84] years; 438 women [58.7%]). The proportion of patients who were functionally independent at 90 days was 68 of 373 (18.2%) in the IVT plus MT and 42 of 373 (11.3%) in the MT alone group (adjusted odds ratio [AOR], 2.63 [95% CI, 1.41-5.11]; P = .003). There was a shift toward better functional outcomes on the modified Rankin Scale favoring IVT plus MT (adjusted common OR, 1.98 [95% CI, 1.35-2.92]; P < .001). The treatment benefit of IVT was greater in patients with partial reperfusion compared with failed reperfusion. There was no difference in symptomatic intracranial hemorrhages between treatment groups (AOR, 0.71 [95% CI, 0.29-1.81]; P = .45), while the death rate was lower after IVT plus MT (AOR, 0.54 [95% CI, 0.34-0.86]; P = .01). CONCLUSIONS AND RELEVANCE These findings suggest that prior IVT was safe and improved functional outcomes at 90 days. Partial reperfusion was associated with a greater treatment benefit of IVT, indicating a positive interaction between IVT and MT. These results support current guidelines that all eligible patients with stroke should receive IVT before MT and add a new perspective to the debate on noninferiority of combined stroke treatment

Extending ontologies by finding siblings using set expansion techniques

Motivation: Ontologies are an everyday tool in biomedicine to capture and represent knowledge. However, many ontologies lack a high degree of coverage in their domain and need to improve their overall quality and maturity. Automatically extending sets of existing terms will enable ontology engineers to systematically improve text-based ontologies level by level

The Helmholtz Analytics Toolkit (Heat) and its role in the landscape of massively-parallel scientific Python

When it comes to enhancing exploitation of massive data, machine learning methods are at the forefront of researchers’ awareness. Much less so is the need for, and the complexity of, applying these techniques efficiently across large-scale, memory-distributed data volumes. In fact, these aspects typical for the handling of massive data sets pose major challenges to the vast majority of research communities, in particular to those without a background in high-performance computing. Often, the standard approach involves breaking up and analyzing data in smaller chunks; this can be inefficient and prone to errors, and sometimes it might be inappropriate at all because the context of the overall data set can get lost. The Helmholtz Analytics Toolkit (Heat) library offers a solution to this problem by providing memory-distributed and hardware-accelerated array manipulation, data analytics, and machine learning algorithms in Python. The main objective is to make memory-intensive data analysis possible across various fields of research ---in particular for domain scientists being non-experts in traditional high-performance computing who nevertheless need to tackle data analytics problems going beyond the capabilities of a single workstation. The development of this interdisciplinary, general-purpose, and open-source scientific Python library started in 2018 and is based on collaboration of three institutions (German Aerospace Center DLR, Forschungszentrum Jülich FZJ, Karlsruhe Institute of Technology KIT) of the Helmholtz Association. The pillars of its development are... - ...to enable memory distribution of n-dimensional arrays, - to adopt PyTorch as process-local compute engine (hence supporting GPU-acceleration), - to provide memory-distributed (i.e., multi-node, multi-GPU) array operations and algorithms, optimizing asynchronous MPI-communication (based on mpi4py) under the hood, and - to wrap functionalities in NumPy- or scikit-learn-like API to achieve porting of existing applications with minimal changes and to enable the usage by non-experts in HPC. In this talk we will give an illustrative overview on the current features and capabilities of our library. Moreover, we will discuss its role in the existing ecosystem of distributed computing in Python, and we will address technical and operational challenges in further development