Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks

AbstractDirectly manipulating the atomic structure to achieve a specific property is a long pursuit in the field of materials. However, hindered by the disordered, non-prototypical glass structure and the complex interplay between structure and property, such inverse design is dauntingly hard for glasses. Here, combining two cutting-edge techniques, graph neural networks and swap Monte Carlo, we develop a data-driven, property-oriented inverse design route that managed to improve the plastic resistance of Cu-Zr metallic glasses in a controllable way. Swap Monte Carlo, as a sampler, effectively explores the glass landscape, and graph neural networks, with high regression accuracy in predicting the plastic resistance, serves as a decider to guide the search in configuration space. Via an unconventional strengthening mechanism, a geometrically ultra-stable yet energetically meta-stable state is unraveled, contrary to the common belief that the higher the energy, the lower the plastic resistance. This demonstrates a vast configuration space that can be easily overlooked by conventional atomistic simulations. The data-driven techniques, structural search methods and optimization algorithms consolidate to form a toolbox, paving a new way to the design of glassy materials.

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Data-Driven Time Series Forecasting for Social Studies Using Spatio-Temporal Graph Neural Networks

10.1145/3462203.3475929 ◽

2021 ◽

Author(s):

Yi-Fan Li ◽

Bo Dong ◽

Latifur Khan ◽

Bhavani Thuraisingham ◽

Patrick T. Brandt ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Social Studies ◽

Time Series Forecasting ◽

Data Driven ◽

Temporal Graph ◽

Spatio Temporal ◽

Graph Neural Networks

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Geometry-Agnostic Data-Driven Thermal Modeling of Additive Manufacturing Processes using Graph Neural Networks

Additive Manufacturing ◽

10.1016/j.addma.2021.102449 ◽

2021 ◽

pp. 102449

Author(s):

Mojtaba Mozaffar ◽

Shuheng Liao ◽

Hui Lin ◽

Kornel Ehmann ◽

Jian Cao

Keyword(s):

Neural Networks ◽

Additive Manufacturing ◽

Thermal Modeling ◽

Data Driven ◽

Manufacturing Processes ◽

Graph Neural Networks

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Uncertainty-aware prediction of chemical reaction yields with graph neural networks

Journal of Cheminformatics ◽

10.1186/s13321-021-00579-z ◽

2022 ◽

Vol 14 (1) ◽

Author(s):

Youngchun Kwon ◽

Dongseon Lee ◽

Youn-Suk Choi ◽

Seokho Kang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Uncertainty Quantification ◽

Chemical Reaction ◽

Predictive Distribution ◽

Data Driven ◽

Permutation Invariance ◽

Molecular Graphs ◽

Benchmark Datasets ◽

Graph Neural Networks

AbstractIn this paper, we present a data-driven method for the uncertainty-aware prediction of chemical reaction yields. The reactants and products in a chemical reaction are represented as a set of molecular graphs. The predictive distribution of the yield is modeled as a graph neural network that directly processes a set of graphs with permutation invariance. Uncertainty-aware learning and inference are applied to the model to make accurate predictions and to evaluate their uncertainty. We demonstrate the effectiveness of the proposed method on benchmark datasets with various settings. Compared to the existing methods, the proposed method improves the prediction and uncertainty quantification performance in most settings.

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Learning from Substitutable and Complementary Relations for Graph-based Sequential Product Recommendation

ACM Transactions on Information Systems ◽

10.1145/3464302 ◽

2022 ◽

Vol 40 (2) ◽

pp. 1-28

Author(s):

Wei Zhang ◽

Zeyuan Chen ◽

Hongyuan Zha ◽

Jianyong Wang

Keyword(s):

Neural Networks ◽

Data Driven ◽

Seamless Integration ◽

Specific Product ◽

Product Recommendation ◽

Target User ◽

Complementary Graph ◽

Sequential Product ◽

Graph Neural Networks ◽

Public Datasets

Sequential product recommendation, aiming at predicting the products that a target user will interact with soon, has become a hotspot topic. Most of the sequential recommendation models focus on learning from users’ interacted product sequences in a purely data-driven manner. However, they largely overlook the knowledgeable substitutable and complementary relations between products. To address this issue, we propose a novel Substitutable and Complementary Graph-based Sequential Product Recommendation model, namely, SCG-SPRe. The innovations of SCG-SPRe lie in its two main modules: (1) The module of interactive graph neural networks jointly encodes the high-order product correlations in the substitutable graph and the complementary graph into two types of relation-specific product representations. (2) The module of kernel-enhanced transformer networks adaptively fuses multiple temporal kernels to characterize the unique temporal patterns between a candidate product to be recommended and any interacted product in a target behavior sequence. Thanks to the seamless integration of the two modules, SCG-SPRe obtains candidate-dependent user representations for different candidate products to compute the corresponding ranking scores. We conduct extensive experiments on three public datasets, demonstrating SCG-SPRe is superior to competitive sequential recommendation baselines and validating the benefits of explicitly modeling the product-product relations.

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Graph Neural Networks for Prediction of Fuel Ignition Quality

10.26434/chemrxiv.12280325.v1 ◽

2020 ◽

Author(s):

Artur Schweidtmann ◽

Jan Rittig ◽

Andrea König ◽

Martin Grohe ◽

Alexander Mitsos ◽

...

Keyword(s):

Neural Networks ◽

Octane Number ◽

Molecular Graph ◽

Chemical Properties ◽

Graph Representation ◽

Structure Property ◽

Oxygenated Hydrocarbons ◽

Physico Chemical ◽

Ignition Quality ◽

Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

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