基于知识蒸馏的分子性质预测：可扩展性分析（Sci. 22/2025）

IF 14.3 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Advanced Science Pub Date : 2025-06-12 DOI:10.1002/advs.202570174

Rahul Sheshanarayana, Fengqi You

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引用次数: 0

摘要

这张图展示了人工智能如何将丰富的分子见解从复杂的教师模型提炼到紧凑的学生模型中，从而实现对实验和量子力学特性的准确、可扩展的预测，从而推进跨领域的分子设计和发现。更多细节可以在Rahul Sheshanarayana和Fengqi You的文章编号2503271中找到。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Knowledge Distillation for Molecular Property Prediction: A Scalability Analysis (Adv. Sci. 22/2025)

Knowledge Distillation for Molecular Property Prediction

This illustration shows how AI distills rich molecular insights from complex teacher models into compact student models, enabling accurate, scalable prediction of both experimental and quantum mechanical properties—advancing molecular design and discovery across domains. More details can be found in article number 2503271 by Rahul Sheshanarayana and Fengqi You.

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来源期刊

Advanced Science CHEMISTRY, MULTIDISCIPLINARYNANOSCIENCE &-NANOSCIENCE & NANOTECHNOLOGY

CiteScore

18.90

自引率

2.60%

发文量

1602

审稿时长

1.9 months

期刊介绍： Advanced Science is a prestigious open access journal that focuses on interdisciplinary research in materials science, physics, chemistry, medical and life sciences, and engineering. The journal aims to promote cutting-edge research by employing a rigorous and impartial review process. It is committed to presenting research articles with the highest quality production standards, ensuring maximum accessibility of top scientific findings. With its vibrant and innovative publication platform, Advanced Science seeks to revolutionize the dissemination and organization of scientific knowledge.