Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 19 references
Abstract
Machine learning (ML) has seen promising developments in materials science, yet its efficacy largely depends on detailed crystal structural data, which are often complex and hard to obtain, limiting their applicability in real-world material synthesis processes. An alternative, using compositional descriptors, offers a simpler approach by indicating the elemental ratios of compounds without detailed structural insights. However, accurately representing materials solely with compositional descriptors presents challenges due to polymorphism, where a single composition can correspond to various structural arrangements, creating ambiguities in its representation. To this end, we introduce PCRL, a novel approach that employs probabilistic modeling of composition to capture the diverse polymorphs from available structural information. Extensive evaluations on sixteen datasets demonstrate the effectiveness of PCRL in learning compositional representation, and analysis on model uncertainty highlights its potential applicability of PCRL in material discovery.
Machine learning models such as Crystal graph convolutional neural networks (CGCNN) have been widely adopted for the rapid and accurate prediction of crystalline material properties. However, these models suffer from a critical limitation: prediction errors increase sharply during extrapolation to out-of-distribution s...
Machine learning force fields (MLFFs) combine the high accuracy of first-principles methods with the high efficiency of classical force fields, offering new opportunities for atomic-level studies of inorganic crystalline materials. We systematically summarize the research progress on MLFFs, elucidate their fundamental...
Jing Yi, Yuxin Zhan, Yuanmao Hu et al.· Physical Chemistry, Chemical...· 0 citations
This work proposes a novel approach that learns hierarchical discrete representations of protein structures using vector quantization, and outperforms state-of-the-art models such as ESMDiff across challenging benchmark datasets, including BPTI MD trajectories and conformational-changing pairs.
Seokjun On, Yujin Jeong, Kanghyeon Kim et al.· Bioinformatics· 0 citations
Graph neural networks have become the dominant machine-learning architecture for predicting materials properties from crystal structures. Yet the initialization of atomic node features has received comparatively little attention, and conventional approaches rely on static elemental descriptors that carry no information...
Paulo R. Pires, Pierre-Paul De Breuck, Mauro Fava et al.· 0 citations
This paper introduces a distance measure that assesses the output of material generative models by capturing both quality and novelty in a single distribution-based evaluation framework, and introduces the Coarse-Fine Transport Distance (CFTD), which is used as guidance for a material generative model.
P. Hagemann, Katharina Ueltzen, Simon Müller et al.· arXiv.org· 0 citations
The ElemeNet software package enables the training of advanced ML models for diverse properties and datasets with an enlarged range of elemental compositions, and introduces moiety predictions, a unified, general-purpose software package for molecular machine learning.
Jacob W. Toney, S. Darouich, Yiran Wang et al.· arXiv.org· 0 citations
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