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Abstract Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N²AMD (Neural-Network Non-Adiabatic Molecular Dynamics), which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. Distinct from conventional machine learning methods that predict key quantities in NAMD, N²AMD computes these quantities directly with a deep neural Hamiltonian, ensuring excellent accuracy, efficiency, and consistency. N²AMD not only achieves impressive efficiency in performing NAMD simulations at the hybrid functional level within the framework of the classical path approximation (CPA), but also demonstrates great potential in predicting non-adiabatic coupling vectors and suggests a method to go beyond CPA. Furthermore, N²AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework offers a reliable and efficient approach for conducting accurate NAMD simulations across various condensed materials. 

Bubbles play a ubiquitous role in electrochemical gas evolution reactions. However, a mechanistic understanding of how bubbles affect the energy efficiency of electrochemical processes remains limited to date, impeding effective approaches to further boost the performance of gas evolution systems. From a perspective of the analogy between heat and mass transfer, bubbles in electrochemical gas evolution reactions exhibit highly similar dynamic behaviors to them in the liquid–vapor phase change. Recent developments of liquid–vapor phase change systems have substantially advanced the fundamental knowledge of bubbles, leading to unprecedented enhancement of heat transfer performance. In this Review, we aim to elucidate a promising opportunity of understanding bubble dynamics in electrochemical gas evolution reactions through a lens of phase change heat transfer. We first provide a background about key parallels between electrochemical gas evolution reactions and phase change heat transfer. Then, we discuss bubble dynamics in gas evolution systems across multiple length scales, with an emphasis on exciting research problems inspired by new insights gained from liquid–vapor phase change systems. Lastly, we review advances in engineered surfaces for manipulating bubbles to enhance heat and mass transfer, providing an outlook on the design of high-performance gas evolving electrodes. 

This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students’ reflective writing. The goal of ReflectSumm is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, with potential implications in the opinion summarization domain in general and the educational domain in particular. The dataset encompasses a diverse range of summarization tasks and includes comprehensive metadata, enabling the exploration of various research questions and supporting different applications. To showcase its utility, we conducted extensive evaluations using multiple state-of-the-art baselines. The results provide benchmarks for facilitating further research in this area. 

This paper presents a data-driven study focusing on analyzing and predicting sentence deletion — a prevalent but understudied phenomenon in document simplification — on a large English text simplification corpus. We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected. We reveal that professional editors utilize different strategies to meet readability standards of elementary and middle schools. To predict whether a sentence will be deleted during simplification to a certain level, we harness automatically aligned data to train a classification model. Evaluated on our manually annotated data, our best models reached F1 scores of 65.2 and 59.7 for this task at the levels of elementary and middle school, respectively. We find that discourse level factors contribute to the challenging task of predicting sentence deletion for simplification.