A critical contribution of artificial intelligence to materials physics is accurate and efficient predictions of crystal properties. Real-space graph neural networks are currently a mainstream choice for crystal property prediction, as reciprocal-space approaches have been commonly considered incapable of achieving comparable accuracy. Here, we develop an Angle-resolved-photoemission-spectroscopy-Inspired Reciprocal-space Crystal Property Predictor (AIRCPP), which combines spherical harmonics and Legendre polynomials as an orthogonal basis, sampled by a Lebedev grid for the reciprocal space. Using a large dataset of 95 700 materials, we demonstrate that AIRCPP achieves comparable or higher accuracy in predicting various crystal properties compared to the widely used crystal graph convolutional neural networks (CGCNN), but with much lower computational costs. Combining AIRCPP and CGCNN further enables a more powerful model termed AIRCPP-MIX that achieves comparable accuracy to the state-of-the-art advanced models. Our study demonstrates the giant potential of reciprocal-space approaches in highly accurate crystal property predictions, laying the foundation for computational materials design and discovery.
Computational prediction of crystal properties plays a pivotal role in materials science. With the accelerated progress in machine learning, crystal property prediction has seen remarkable advancements. Nevertheless, the utilization of machine learning in this context faces several challenges. First, existing methods that utilize the smallest repeatable unit cell of a crystal often have a limited receptive field. Second, as experiments measuring crystal properties are time-consuming, labeled data is often scarce. To address these challenges, we propose a
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ugmentation framework (SILVA) for crystal property prediction. To go beyond limited receptive fields, we introduce the notion of a crystal supercell, which enables more comprehensive explorations of crystal structure. To fully combine insights from multi-view structures, i.e., from unit cells and supercells, we propose a multi-view representation learning (MRL) module that features a representation space that enhances the learning of representative features specific to different views. To alleviate the limited availability of labeled data, we propose a selective representation augmentation (SRA) module. Given representations of labeled training data, we carefully select nearby representations in the representation space established by the MRL module so that labels can be reused. An experimental study offers evidence that SILVA is capable of outperforming state-of-the-art methods.
Haomin Yu, Jilin Hu, K. Tolborg et al.· ACM Transactions on AI for S...· 0 citations
This study developed an efficient framework integrating density functional theory with machine learning methods to predict surface energies in vdW crystals, by combining structural characteristics with elemental properties and found that the generative adversarial network achieved the best performance.
Shangbin Wu, Naihua Miao, Yu Shu et al.· Journal of Physical Chemistr...· 0 citations
This work pursues a multi-tier development strategy in which machine learning (ML) algorithms are combined with exact physical symmetries and constraints to significantly accelerate computations of electronic structure and atomistic dynamics.
Crystalline materials discovery is essential for energy, electronics, and catalysis, but the vast chemical and structural space makes exhaustive screening infeasible. Existing voxel-based methods are limited by the local receptive fields of three-dimensional convolutional neural networks and the posterior collapse of high-dimensional variational autoencoders. Here, we develop a Fourier Neural Operator (FNO)-based crystal-field solver that maps a prescribed chemical formula and lattice parameters to periodic number-density and electron-density fields. By operating on global Fourier modes, the solver captures long-range correlations in periodic crystal fields beyond conventional local convolutions. Building on this solver, we construct a coupled generation-solving framework in which a conditional variational autoencoder generates diverse candidate lattice parameters in a low-dimensional basis-coefficient space, followed by density-field prediction and atomic reconstruction through peak detection, position optimization, and weight optimization. The reconstructed structures are further screened using voxel-level filtering, machine-learning interatomic-potential relaxation, and first-principle calculations. The framework generates novel structures across 104 chemical formulas with competitive reconstruction accuracy, demonstrating high generative diversity and structural validity. By extending Fourier neural operators to periodic crystal fields and coupling them with composition-conditioned lattice generation, our approach provides a scalable route to crystal structure discovery from prescribed chemical compositions.
Zhi-Jie Yu, Jing-Yu Li, Yang Huang et al.· 0 citations
This work demonstrates how recent foundational machine learning interatomic potentials (MLIPs) trained at the r$^2$SCAN level can be leveraged to improve the agreement of formation energies with experiment, reducing the mean absolute error by more than 40% relative to GGA without requiring any additional DFT calculation.
Timo Reents, Marnik Bercx, Giovanni Pizzi· 0 citations
This work introduces a verified and interpretable pathway for high‐throughput screening of orthorhombic perovskites and provides fundamental insight into the descriptor‐property connections governing different perovskite polymorphs.
Q. Fatima, A. A. Haidry, Usaid Ahmad et al.· Advanced Theory and Simulati...· 0 citations
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