The paper introduces a data descriptor and a graph neural network designed to learn inter‑graph interactions between heterogeneous substructures in complex chemical systems, specifically between organic and inorganic compounds. It addresses the limitation of prior methods that only considered homogeneous graph interactions. The proposed approach was applied to predict band gaps for 1,682 hybrid solar cell materials, achieving state‑of‑the‑art accuracy.
Complex chemical systems containing heterogeneous substructures are common in real-world applications. Various physical phenomena of the complex chemical systems are derived from the interactions betw...
arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.
arXiv:2607. 29510v1 Announce Type: cross Abstract: High-entropy perovskite oxides (HEPOs) represent a chemically complex class of materials with promising functional properties, yet their vast compositional space and, chemical/structural disorder pose significant challenge for accurate property prediction.
The thesis presents AI frameworks that accelerate crystalline materials discovery by tackling both crystal property prediction and crystal structure generation. It introduces CrysXPP, CrysGNN, and CrysMMNet for efficient, data‑sparse property prediction using graph autoencoding, self‑supervised pretraining, and multimodal learning. For generation, TGDMat is a text‑guided diffusion model that jointly learns lattice parameters, atomic types, and coordinates, enabling valid, stable, and conditionally generated periodic materials.
arXiv:2608. 14640v1 Announce Type: cross Abstract: Stacked bilayer materials exhibit rich stacking-dependent properties driven by the interplay between strong intra-layer bonding and weak inter-layer van der Waals interactions.
This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal property prediction a...
arXiv:2607. 07935v1 Announce Type: cross Abstract: We present path_boost, a Python package for interpretable supervised learning on graph-structured input data.
arXiv:2607. 02212v1 Announce Type: cross Abstract: Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information into a single representation, obscuring whether a prediction is driven by global chemistry, molecular structure, or both.
arXiv:2607. 08996v1 Announce Type: cross Abstract: Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties.
arXiv:2608.23874v1 Announce Type: cross Abstract: Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental...
arXiv:2606. 19133v1 Announce Type: cross Abstract: Scalable prediction of optical spectra is a critical component of high-throughput materials screening for optoelectronic applications such as solar cells.
arXiv:2606. 19374v1 Announce Type: cross Abstract: Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding.