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1. What influences students’ abilities to critically evaluate scientific investigations?

   https://doi.org/10.1371/journal.pone.0273337  

   Heim, Ashley B.; Walsh, Cole; Esparza, David; Smith, Michelle K.; Holmes, N. G. (August 2022, PLOS ONE)

   Pamucar, Dragan (Ed.)

   Critical thinking is the process by which people make decisions about what to trust and what to do. Many undergraduate courses, such as those in biology and physics, include critical thinking as an important learning goal. Assessing critical thinking, however, is non-trivial, with mixed recommendations for how to assess critical thinking as part of instruction. Here we evaluate the efficacy of assessment questions to probe students’ critical thinking skills in the context of biology and physics. We use two research-based standardized critical thinking instruments known as the Biology Lab Inventory of Critical Thinking in Ecology (Eco-BLIC) and Physics Lab Inventory of Critical Thinking (PLIC). These instruments provide experimental scenarios and pose questions asking students to evaluate what to trust and what to do regarding the quality of experimental designs and data. Using more than 3000 student responses from over 20 institutions, we sought to understand what features of the assessment questions elicit student critical thinking. Specifically, we investigated (a) how students critically evaluate aspects of research studies in biology and physics when they are individually evaluating one study at a time versus comparing and contrasting two and (b) whether individual evaluation questions are needed to encourage students to engage in critical thinking when comparing and contrasting. We found that students are more critical when making comparisons between two studies than when evaluating each study individually. Also, compare-and-contrast questions are sufficient for eliciting critical thinking, with students providing similar answers regardless of if the individual evaluation questions are included. This research offers new insight on the types of assessment questions that elicit critical thinking at the introductory undergraduate level; specifically, we recommend instructors incorporate more compare-and-contrast questions related to experimental design in their courses and assessments. 

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2. Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data

   https://doi.org/10.1021/acs.jpclett.4c02941  

   Shanks, Brennon L; Sullivan, Harry W; Hoepfner, Michael P (December 2024, The Journal of Physical Chemistry Letters)

   Learning interaction potentials from the structure factor is frequently seen as impractical due to accuracy constraints of neutron and X-ray scattering experiments. This study reexamines this historic inverse problem using Bayesian inference and probabilistic machine learning on a Mie fluid to elucidate how measurement noise impacts the accuracy of recovered potentials. To perform reliable potential reconstruction, we recommend that scattering data must have noise smaller than 0.005 up to ∼30 Å–1 at a standard bin width 0.05 Å–1. At uncertainties below this threshold, Mie potentials can be determined within approximately ±1.3 for the repulsive exponent, ±0.068 Å for atomic size, and ±0.024 kcal/mol in well-depth with 95% confidence. These findings highlight the potential of uniting scattering and machine learning to overcome a century-old physics problem, infer local atomic forces to serve as a vital benchmark … 

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3. Compression theory for inhomogeneous systems

   https://doi.org/10.1038/s41467-024-54341-8  

   Gökmen, Doruk Efe; Biswas, Sounak; Huber, Sebastian D; Ringel, Zohar; Flicker, Felix; Koch-Janusz, Maciej (December 2024, Nature Communications)

   Abstract The physics of complex systems stands to greatly benefit from the qualitative changes in data availability and advances in data-driven computational methods. Many of these systems can be represented by interacting degrees of freedom on inhomogeneous graphs. However, the lack of translational invariance presents a fundamental challenge to theoretical tools, such as the renormalization group, which were so successful in characterizing the universal physical behaviour in critical phenomena. Here we show that compression theory allows the extraction of relevant degrees of freedom in arbitrary geometries, and the development of efficient numerical tools to build an effective theory from data. We demonstrate our method by applying it to a strongly correlated system on an Ammann-Beenker quasicrystal, where it discovers an exotic critical point with broken conformal symmetry. We also apply it to an antiferromagnetic system on non-bipartite random graphs, where any periodicity is absent. 

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4. Visible light photonic integrated Brillouin laser

   https://doi.org/10.1038/s41467-021-24926-8  

   Chauhan, Nitesh; Isichenko, Andrei; Liu, Kaikai; Wang, Jiawei; Zhao, Qiancheng; Behunin, Ryan O.; Rakich, Peter T.; Jayich, Andrew M.; Fertig, C.; Hoyt, C. W.; et al (December 2021, Nature Communications)

   Abstract Narrow linewidth visible light lasers are critical for atomic, molecular and optical (AMO) physics including atomic clocks, quantum computing, atomic and molecular spectroscopy, and sensing. Stimulated Brillouin scattering (SBS) is a promising approach to realize highly coherent on-chip visible light laser emission. Here we report demonstration of a visible light photonic integrated Brillouin laser, with emission at 674 nm, a 14.7 mW optical threshold, corresponding to a threshold density of 4.92 mW μm −2 , and a 269 Hz linewidth. Significant advances in visible light silicon nitride/silica all-waveguide resonators are achieved to overcome barriers to SBS in the visible, including 1 dB/meter waveguide losses, 55.4 million quality factor (Q), and measurement of the 25.110 GHz Stokes frequency shift and 290 MHz gain bandwidth. This advancement in integrated ultra-narrow linewidth visible wavelength SBS lasers opens the door to compact quantum and atomic systems and implementation of increasingly complex AMO based physics and experiments. 

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5. Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization

   https://doi.org/10.1039/D5NR01562F  

   Golbabaei, Mohammad Hossein; Zohrevand, Mohammad; Zhang, Ning (September 2025, Nanoscale)

   High-entropy alloys (HEAs) have attracted considerable attention due to their exceptional properties and outstanding performance across various applications. However, the vast compositional space and complex high-dimensional atomic interactions pose significant challenges in uncovering fundamental physical principles and effectively guiding alloy design. Traditional experimental approaches, often reliant on trial-and-error methods, are time-consuming, cost-prohibitive, and inefficient. To accelerate progress in this field, advanced simulation techniques and data-driven methodologies, particularly machine learning (ML) with a particular interest in nanoscale phenomena, have emerged as transformative tools for composition design, property prediction, and performance optimization. By leveraging extensive materials databases and sophisticated learning algorithms, ML facilitates the discovery of intricate patterns that conventional methods may overlook, and enables the design of HEAs with targeted properties. This review paper provides a comprehensive overview of recent advancements in ML applications for HEAs. It begins with a brief introduction of the fundamental principles of HEAs and ML methodologies, including key algorithms, databases, and evaluation metrics. The critical role of materials representation and feature engineering in ML-driven HEA design is thoroughly discussed. Furthermore, state-of-the-art developments in the integration of ML with HEA research, particularly in composition optimization, property prediction, and phase identification, are systematically reviewed. Special emphasis is placed on cutting-edge deep learning techniques, such as generative models and computer vision, which are revolutionizing the f ield. This study explores the application of machine learning (ML) in developing highly accurate ML interatomic potentials (MLIPs) for molecular dynamics (MD) simulations. These MLIPs have the potential to enhance the accuracy and efficiency of simulations, enabling a more precise representation of the fundamental physics governing high-entropy alloys (HEAs) at the atomic level. A critical discussion is provided, addressing both the potential advantages and the inherent limitations of this approach. This review aims to provide insights into the future directions of ML-driven HEA research, offering a roadmap for advancing material design through data-driven innovation. 

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