Compositional engineering of perovskites has enabled the precise control of material properties required for their envisioned applications in photovoltaics. However, challenges remain to address efficiency, stability, and toxicity simultaneously. Mixed lead‐free and inorganic perovskites have recently demonstrated potential for resolving such issues but their composition space is gigantic, making it difficult to discover promising candidates even using high‐throughput methods. A machine learning approach employing a generalized element‐agnostic fingerprint is shown to rapidly and accurately predict key properties using a new database of 344 perovskites generated with density functional theory. Bandgap, formation energy, and convex hull distance are predicted using validation subsets to within 146 meV, 15 meV per atom, and 11 meV per atom, respectively. The resulting model is used to predict trends in entirely different chemical spaces, and perform rapid composition and configuration space sampling without the need for expensive ab initio simulations.

ACL stump trephination and concomitant intra-ligamentary application of ACP revealed promising results at mid-term follow-up to treat partial ACL lesions.

Core−shell nanocatalyst activities are chiefly controlled by bimetallic material composition, shell thickness, and nanoparticle size. We present a machine learning framework predicting strain with site-specific precision to rationalize how strain on Pt core−shell nanocatalysts can enhance oxygen reduction activities. Large compressive strain on Pt@Cu and Pt@Ni induces optimal mass activities at 1.9 nm nanoparticle size. It is predicted that bimetallic Pt@Au and Pt@Ag have the best mass activities at 2.8 nm, where active sites are exposed to weak compressive strain. We demonstrate that optimal strain depends on the nanoparticle size; for instance, strengthening compressive strain on 1.92 nm sized Pt@Cu and Pt@Ni, or weakening compressive strain on 2.83 nm sized Pt@Ag and Pt@Au, can lead to further enhanced mass activities.

Purpose To determine the prevalence of a deep lateral femoral notch sign (LFNS) in magnetic resonance imaging (MRI) in patients with anterior cruciate ligament (ACL) and concomitant posterior root tears of the lateral meniscus (PLRT). Methods A retrospective chart review was conducted to identify all patients undergoing ACL reconstruction between 2016 and 2018. Based on the arthroscopic appearance of the lateral meniscus, patients were assorted to one of three groups: isolated ACL tear (ACL-Group), ACL tear with concomitant lateral meniscus tear not involving the posterolateral root (Meniscus-Group), and ACL tear with concomitant PLRT (PLRT-Group). Incidence and depth of a LFNS on preoperative MRI was compared between the three cohorts. Results 115 patients (mean age: 29.5 ± 11.3 years) were included in the study, with 58 patients (50.4%) assorted to the ACL-Group, 24 patients (20.9%) to the Meniscus-Group, and 33 patients (28.7%) to the PLRT-Group. The prevalence of a LFNS was signiicantly higher in the PLRT-Group (39.4%), when compared to the ACL-(5.2%) or Meniscus-Groups (25.0%; p < 0.001, respectively). Additionally, logistic regression analysis demonstrated that patients with PLRT were 5.3 times more likely to have a LFNS as compared to those without a lateral root tear (p < 0.001). ConclusionIn patients with ACL tears, the presence of a LFNS on preoperative MRI may be predictive for a PLRT. As the LFNS occurs in almost 40% of the patients with combined ACL tears and PLRT, the LFNS may be a useful secondary diagnostic inding in early MRI diagnostic. Identifying PLRT on MRI is clinically relevant, as it prevents misdiagnosis and facilitates surgical decision-making, thus avoiding subsequent delayed treatment. Level of evidence Level IV.

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