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Electric vehicles (EVs) are key to transportation decarbonization, yet their battery performance and longevity are vulnerable to temperature extremes, which will be affected by climate change. Battery technology advancements moderate this vulnerability, a dynamic rarely captured in technology assessments under future climates. Here we show that technological advancements have largely mitigated battery lifetime reductions driven by climate change. We combine bottom-up EV simulation and battery degradation models with high-resolution downscaled climate data, capturing warming and variability, for 300 global cities. Under 2 °C warming, old (2010–2018) batteries would experience lifetime declines of 8% (average) and 30% (maximum), whereas new batteries (2019–2023) would experience lifetime declines of 3% (average) and 10% (maximum). New batteries also mitigate regional inequities in battery lifetime reductions driven by climate change. Increasing cell temperature primarily drives lifetime declines. Our findings emphasize climate adaptation co-benefits from technological advancement and increasing thermal resiliency of emerging battery technologies. 

Abstract Recent advances in topology‐based modeling have greatly improved molecular prediction tasks, particularly in protein–ligand binding affinity. However, when the focus shifts to predicting protein–protein interactions (PPIs) binding free energy, the question becomes significantly more challenging due to the ineffective use of topological features and the lack of reliable datasets. In this work, we propose a persistent‐Laplacian machine learning framework centered on the Persistent‐Laplacian Neural Network (PLNet), which encodes each protein chain at the binding interface using both persistent Laplacian–based features and protein language model embeddings. It can achieve a promising Pearson correlation of 0.80 under leave‐out‐protein‐out cross‐validation on our newly assembled benchmark dataset, P2P, which includes 6886 protein complexes drawn from existing sources. For comparison, we also implement a gradient‐boosting decision tree model under the same settings. This baseline method highlights the advantage of PLNet in capturing complex topology‐aware descriptors in PPI prediction. 

Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and the challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a “codeless” and free ML neural network building software, Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on the color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to students’ daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts. 

Multidimensional single-molecule localization microscopy (mSMLM) represents a paradigm shift in the realm of super-resolution microscopy techniques. It affords the simultaneous detection of single-molecule spatial locations at the nanoscale and functional information by interrogating the emission properties of switchable fluorophores. The latter is finely tuned to report its local environment through carefully manipulated laser illumination and single-molecule detection strategies. This Perspective highlights recent strides in mSMLM with a focus on fluorophore designs and their integration into mSMLM imaging systems. Particular interests are the accomplishments in simultaneous multiplexed super-resolution imaging, nanoscale polarity and hydrophobicity mapping, and single-molecule orientational imaging. Challenges and prospects in mSMLM are also discussed, which include the development of more vibrant and functional fluorescent probes, the optimization of optical implementation to judiciously utilize the photon budget, and the advancement of imaging analysis and machine learning techniques. 

Abstract We communicate a feasibility study for high‐resolution structural characterization of biomacromolecules in aqueous solution from X‐ray scattering experiments measured over a range of scattering vectors (q) that is approximately two orders of magnitude wider than used previously for such systems. Scattering data with such an extendedq‐range enables the recovery of the underlying real‐space atomic pair distribution function, which facilitates structure determination. We demonstrate the potential of this method for biomacromolecules using several types of cyclodextrins (CD) as model systems. We successfully identified deviations of the tilting angles for the glycosidic units in CDs in aqueous solutions relative to their values in the crystalline forms of these molecules. Such level of structural detail is inaccessible from standard small angle scattering measurements. Our results call for further exploration of ultra‐wide‐angle X‐ray scattering measurements for biomacromolecules.