Coarse-grained crystal graph neural networks for reticular materials design
arXiv:2310.19500 · doi:10.1021/acs.jcim.3c02083
Abstract
Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine relative ease of synthesis and an impressive range of applications in various fields, from gas storage to biomedicine. Diverse properties arise from the variation of building units$\unicode{x2013}$metal centers and organic linkers$\unicode{x2013}$in almost infinite chemical space. Such variation substantially complicates experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from incorporation of irrelevant and redundant features such as full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design.
arXiv comment19 pages, 5 figures
References in corpus (22)
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- The Open Catalyst 2020 (OC20) Dataset and Community Challenges
- Atomistic Line Graph Neural Network for Improved Materials Property Predictions
- Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
- Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
- Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
- ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis
- Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
- Machine Learning Energies of 2 M Elpasolite (ABCD) Crystals
- Prediction model of band-gap for AX binary compounds by combination of density functional theory calculations and machine learning techniques
- Developing an improved Crystal Graph Convolutional Neural Network framework for accelerated materials discovery
- Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction
- A critical examination of compound stability predictions from machine-learned formation energies
- Deep Neural Networks for Accurate Predictions of Garnet Stability
- Orbital Graph Convolutional Neural Network for Material Property Prediction
- Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability
- Toward Accurate Interpretable Predictions of Materials Properties within Transformer Language Models
- Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks
- MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
- Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks