Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10223-1

A composable neural network emulator is described for speeding up thermoelectric generator design, demonstrating the ability to predict generator performance with >99% accuracy while taking only 0.01% of the time compared with commercial finite-element solvers.

Background: High stress levels are common among health care workers (HCWs), threatening their health and workforce stability. Internet-based mobile stress management (MSM) is a promising intervention for reducing work-related stress; however, poor adherence limits effectiveness. Exploring factors influencing HCWs’ adherence may thus aid in developing optimal interventions. Objective: The research aimed to investigate (1) how HCWs’ well-being and cognitive factors influenced MSM treatment adherence and (2) what HCWs’ specific needs for MSM were. Methods: This study was a convergent mixed methods secondary analysis of a nonrandomized controlled trial. HCWs who were currently employed, had internet access, had no serious medical problems, and were willing to participate were recruited by convenience sampling through an MSM project in a large Chinese general hospital from August 11, 2021, to January 31, 2022. Those intending to leave the hospital or with insufficient medical condition for follow-up were excluded. Quantitative data were collected from 157 HCWs (n=135, 86% female participants; mean age of 33.7, SD 4.9 y) electronically via Research Electronic Data Capture (REDCap). Measures included sociodemographic characteristics, the Fatigue Assessment Scale, the 14-item Perceived Stress Scale, a user experience questionnaire, an attitudes scale (perceived usefulness, feasibility, and enjoyment), and self-reported practice frequency. Qualitative data were collected via an open-ended question answered by 96 participants. Quantitative data were analyzed using hierarchical regression and structural equation modeling. Qualitative data were analyzed using reflexive-thematic analysis in NVivo (QSR International). Results: In the quantitative study (n=157), hierarchical regression analyses showed that fatigue was a significant negative predictor of adherence (b=−0.050, 95% CI −0.086 to −0.015; =−2.859; 2-tailed =.005), while user experience (b=0.074, 95% CI 0.042-0.106; =4.569; 2-tailed

A versatile, atom-level generative AI model enables unified pocket-interacting tasks, from docking to de novo design, and demonstrates robust experimental validation for both small-molecule and peptide therapeutics.