Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces

VoiceBank-2023: A Multi-Speaker Mandarin Speech Corpus for Constructing Personalized TTS Systems for the Speech Impaired

Services of personalized TTS systems for the Mandarin-speaking speech impaired are rarely mentioned. Taiwan started the VoiceBanking project in 2020, aiming to build a complete set of services to deliver personalized Mandarin TTS systems to amyotrophic lateral sclerosis patients. This paper reports the corpus design, corpus recording, data purging and correction for the corpus, and evaluations of the developed personalized TTS systems, for the VoiceBanking project. The developed corpus is named after the VoiceBank-2023 speech corpus because of its release year. The corpus contains 29.78 hours of utterances with prompts of short paragraphs and common phrases spoken by 111 native Mandarin speakers. The corpus is labeled with information about gender, degree of speech impairment, types of users, transcription, SNRs, and speaking rates. The VoiceBank-2023 is available by request for non-commercial use and welcomes all parties to join the VoiceBanking project to improve the services for the speech impaired.Comment: submitted to 26th International Conference of the ORIENTAL-COCOSD

Munc18-1 binding to the neuronal SNARE complex controls synaptic vesicle priming

Munc18-1 and soluble NSF attachment protein receptors (SNAREs) are critical for synaptic vesicle fusion. Munc18-1 binds to the SNARE syntaxin-1 folded into a closed conformation and to SNARE complexes containing open syntaxin-1. Understanding which steps in fusion depend on the latter interaction and whether Munc18-1 competes with other factors such as complexins for SNARE complex binding is critical to elucidate the mechanisms involved. In this study, we show that lentiviral expression of Munc18-1 rescues abrogation of release in Munc18-1 knockout mice. We describe point mutations in Munc18-1 that preserve tight binding to closed syntaxin-1 but markedly disrupt Munc18-1 binding to SNARE complexes containing open syntaxin-1. Lentiviral rescue experiments reveal that such disruption selectively impairs synaptic vesicle priming but not Ca2+-triggered fusion of primed vesicles. We also find that Munc18-1 and complexin-1 bind simultaneously to SNARE complexes. These results suggest that Munc18-1 binding to SNARE complexes mediates synaptic vesicle priming and that the resulting primed state involves a Munc18-1–SNARE–complexin macromolecular assembly that is poised for Ca2+ triggering of fusion