Paper
Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors
Authors
Jian-Feng Zhang, Ze-Feng Gao, Xiao-Qi Han, Bo Zhan, Dingshun Lv, Miao Gao, Kai Liu, Xinguo Ren, Zhong-Yi Lu, Tao Xiang
Abstract
Although chemical bonding is the fundamental mechanistic bridge connecting atomic structure to macroscopic material properties, current data-driven materials science largely treats it as an implicit "black box". Existing machine learning (ML) models rely predominantly on geometric coordinates, forcing them to implicitly relearn complex quantum mechanics from scratch. This lack of intermediate physical features limits model interpretability and generalizability, particularly when training data is scarce. To solve this problem, we introduce MattKeyBond, a bond-centric materials database that explicitly maps the local electronic landscape and bonding interactions of materials. Building on this, we propose Bonding Attractivity (BA), a novel element-specific descriptor that quantifies the intrinsic capability of atoms to form covalent networks. By providing pre-calculated, energy-dimensional bonding descriptors, MattKeyBond transforms the implicit "black box" into physically interpretable features. This strategy relieves ML models from the burden of deducing physical laws from pure geometry, enabling accurate predictions even with limited data and seamlessly integrating electronic structure theory into modern AI workflows.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.18876v1</id>\n <title>Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors</title>\n <updated>2026-03-19T13:21:25Z</updated>\n <link href='https://arxiv.org/abs/2603.18876v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.18876v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Although chemical bonding is the fundamental mechanistic bridge connecting atomic structure to macroscopic material properties, current data-driven materials science largely treats it as an implicit \"black box\". Existing machine learning (ML) models rely predominantly on geometric coordinates, forcing them to implicitly relearn complex quantum mechanics from scratch. This lack of intermediate physical features limits model interpretability and generalizability, particularly when training data is scarce. To solve this problem, we introduce MattKeyBond, a bond-centric materials database that explicitly maps the local electronic landscape and bonding interactions of materials. Building on this, we propose Bonding Attractivity (BA), a novel element-specific descriptor that quantifies the intrinsic capability of atoms to form covalent networks. By providing pre-calculated, energy-dimensional bonding descriptors, MattKeyBond transforms the implicit \"black box\" into physically interpretable features. This strategy relieves ML models from the burden of deducing physical laws from pure geometry, enabling accurate predictions even with limited data and seamlessly integrating electronic structure theory into modern AI workflows.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cond-mat.mtrl-sci'/>\n <published>2026-03-19T13:21:25Z</published>\n <arxiv:comment>17 pages, 10 figures</arxiv:comment>\n <arxiv:primary_category term='cond-mat.mtrl-sci'/>\n <author>\n <name>Jian-Feng Zhang</name>\n </author>\n <author>\n <name>Ze-Feng Gao</name>\n </author>\n <author>\n <name>Xiao-Qi Han</name>\n </author>\n <author>\n <name>Bo Zhan</name>\n </author>\n <author>\n <name>Dingshun Lv</name>\n </author>\n <author>\n <name>Miao Gao</name>\n </author>\n <author>\n <name>Kai Liu</name>\n </author>\n <author>\n <name>Xinguo Ren</name>\n </author>\n <author>\n <name>Zhong-Yi Lu</name>\n </author>\n <author>\n <name>Tao Xiang</name>\n </author>\n </entry>"
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