English(EN) Synthesizability Prediction of Crystalline Structures with Structure-Aware Feature Learning and Uncertainty Quantification

新型AI模型高精度预测晶体可合成性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了SyntheFormer，一个旨在预测假设无机晶体可合成性的新颖框架。该模型结合了傅里叶变换的晶体性质、感知结构特征提取和一个深度神经网络分类器。SyntheFormer在前瞻性评估中表现强劲，测试AUC达到0.735，并成功识别出两种先前未标记的材料Y6Fe(SiS7)2和BaYb2F8为可能可合成。该框架区分热力学稳定但未合成的化合物与实验证实的亚稳态化合物的能力，凸显了其加速材料发现的潜力。 AI

影响通过预测新型晶体结构的实验可行性，加速材料发现。

排序理由该集群包含一篇详细介绍材料科学新AI模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Danial Ebrahimzadeh, Sarah Sharif, Yaser Mike Banad · 2026-09-10 04:00

基于感知结构特征学习和不确定性量化的晶体结构可合成性预测

arXiv:2510.19251v2 Announce Type: replace-cross Abstract: Predicting which hypothetical inorganic crystals can be experimentally realized remains a central challenge in accelerating materials discovery. SyntheFormer is a positive-unlabeled framework that learns synthesizability d…

本文如何被排名

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基于感知结构特征学习和不确定性量化的晶体结构可合成性预测

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