English(EN) ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs

新的ADAPT模型使用Transformer处理长程原子相互作用

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了ADAPT，一种新的机器学习力场，它绕过了图神经网络来模拟原子相互作用。这种方法直接使用原子坐标和Transformer编码器来捕捉所有成对相互作用，旨在改进长程力的表示。在硅点缺陷上的测试中，与现有的基于图的模型相比，ADAPT在力和能量预测误差方面有了显著降低，同时计算成本也更低。 AI

影响提供了一种更有效、更准确的模拟材料性能的方法，有望加速材料发现。

排序理由详细介绍一种新颖的材料科学机器学习模型的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Evan Dramko, Yihuang Xiong, Yizhi Zhu, Geoffroy Hautier, Thomas Reps, Christopher Jermaine, Anastasios Kyrillidis · 2026-09-17 04:00

ADAPT：无需图的轻量级、长程机器学习力场

arXiv:2509.24115v2 Announce Type: replace Abstract: Point defects play a central role in driving the properties of materials. First-principles methods are widely used to compute defect energetics and structures, including at scale for high-throughput defect databases. However, th…

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ADAPT：无需图的轻量级、长程机器学习力场

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