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New GNN models advance crystal property prediction and molecular simulations

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new graph neural network (GNN) models for predicting crystal properties. One approach, CPGN, uses a multi-scale GNN to jointly learn atomic, bond, and coordination-polyhedron representations, outperforming existing models on benchmark datasets for formation energy and band gap prediction. Another development, MolCryst-MLIPs, offers an open database of fine-tuned MACE models for molecular crystals, which are capable of resolving polymorphic energy landscapes and maintaining structural integrity during simulations. AI

IMPACT Advances in GNNs and MLIPs could accelerate materials discovery and design by improving the accuracy and efficiency of property prediction.

RANK_REASON Two research papers introducing new models and datasets for material property prediction.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New GNN models advance crystal property prediction and molecular simulations

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sanjay Chakraborty ·

    Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

    arXiv:2607.24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science. While graph neural networks (GNNs) such as CGCNN, MEGNet, ALIGNN, and SchNet have shown strong performance, they primarily repre…

  2. arXiv cs.LG TIER_1 English(EN) · Adam Lahouari, Shen Ai, Jihye Han, Jillian Hoffstadt, Philipp Hoellmer, Charlotte Infante, Pulkita Jain, Sangram Kadam, Maya M. Martirossyan, Amara McCune, Hypatia Newton, Shlok J. Paul, Willmor Pena, Jonathan Raghoonanan, Sumon Sahu, Oliver Tan, Andrea … ·

    MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals

    arXiv:2604.13897v2 Announce Type: replace Abstract: We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises fine-tuned MACE models for nine molecular crystal systems---Benzamide, Benzoi…

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