Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials. In this work we extend the neural message passing model with an edge update network which allows the information exchanged between atoms to depend on the hidden state of the receiving atom. We benchmark the proposed model on three publicly available datasets (QM9, The Materials Project and OQMD) and show that the proposed model yields superior prediction of formation energies and other properties on all three datasets in comparison with the best published results. Furthermore we investigate different methods for constructing the graph used to represent crystalline structures and we find that using a graph based on K-nearest neighbors achieves better prediction accuracy than using maximum distance cutoff or the Voronoi tessellation graph

Benchmark density functional theory calculations for nano-scale conductance

We present a set of benchmark calculations for the Kohn-Sham elastic transmission function of five representative single-molecule junctions. The transmission functions are calculated using two different density functional theory (DFT) methods, namely an ultrasoft pseudopotential plane wave code in combination with maximally localized Wannier functions, and the norm-conserving pseudopotential code Siesta which applies an atomic orbital basis set. For all systems we find that the Siesta transmission functions converge toward the plane-wave result as the Siesta basis is enlarged. Overall, we find that an atomic basis with double-zeta and polarization is sufficient (and in some cases even necessary) to ensure quantitative agreement with the plane-wave calculation. We observe a systematic down shift of the Siesta transmission functions relative to the plane-wave results. The effect diminishes as the atomic orbital basis is enlarged, however, the convergence can be rather slow.Comment: 10 pages, 7 figure

Hybrid Neural Networks with Attention-based Multiple Instance Learning for Improved Grain Identification and Grain Yield Predictions

Agriculture is a critical part of the world's food production, being a vital aspect of all societies. Procedures need to be adjusted to their specific environment because of their climate and field condition disparity. Existing research has demonstrated the potential of grain yield predictions on Norwegian farms. However, this research is limited to regional analytics, which is unable to acquire sufficient plant growth factors influenced by field conditions and farmers' decisions. One factor critical for yield prediction is the crop type planted on a per-field basis. This research effort proposes a novel approach for improving crop yield predictions using a hybrid deep neural network utilizing temporal satellite imagery from a remote sensing system. Additionally, We apply a variety of data, including grain production, meteorological data, and geographical data. The crop yield prediction system is supported by a field-based crop type classification model, which supplies features related to crop type and field area. Our crop classification system takes advantage of both raw satellite images as well as carefully chosen vegetation indices. Further, we propose a multi-class attention-based deep multiple instance learning model to utilize semi-labeled datasets, fully benefiting Norwegian data acquisition. Our best crop classification model, which consists of a time distributed network and a gated recurrent unit, classifies crop types with an accuracy of 70\% and is currently state-of-the-art for country-wide crop type mapping in Norway. Lastly, our yield prediction system enables realistic in-season early predictions that could benefit actors in real-life scenarios

Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with Density Functional Theory (DFT), but the necessary computer time sets limitations for the investigated material space. Therefore, the development of machine learning models for prediction of DFT calculated properties are currently of interest. A particular challenge for \emph{new} materials is that the atomic positions are generally not known. We present a machine learning model for the prediction of DFT-calculated formation energies based on Voronoi quotient graphs and local symmetry classification without the need for detailed information about atomic positions. The model is implemented as a message passing neural network and tested on the Open Quantum Materials Database (OQMD) and the Materials Project database. The test mean absolute error is 20 meV on the OQMD database and 40 meV on Materials Project Database. The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set. Pretraining on OQMD and subsequent training on 100 selenides result in a mean absolute error below 0.1 eV for the formation energy of the selenides.Comment: 14 pages including references and 13 figure

Definition of a scoring parameter to identify low-dimensional materials components

The last decade has seen intense research in materials with reduced dimensionality. The low dimensionality leads to interesting electronic behavior due to electronic confinement and reduced screening. The investigations have to a large extent focused on 2D materials both in their bulk form, as individual layers a few atoms thick, and through stacking of 2D layers into heterostructures. The identification of low-dimensional compounds is therefore of key interest. Here, we perform a geometric analysis of material structures, demonstrating a strong clustering of materials depending on their dimensionalities. Based on the geometric analysis, we propose a simple scoring parameter to identify materials of a particular dimension or of mixed dimensionality. The method identifies spatially connected components of the materials and gives a measure of the degree of "1D-ness," "2D-ness," etc., for each component. The scoring parameter is applied to the Inorganic Crystal Structure Database and the Crystallography Open Database ranking the materials according to their degree of dimensionality. In the case of 2D materials the scoring parameter is seen to clearly separate 2D from non-2D materials and the parameter correlates well with the bonding strength in the layered materials. About 3000 materials are identified as one-dimensional, while more than 9000 are mixed-dimensionality materials containing a molecular (0D) component. The charge states of the components in selected highly ranked materials are investigated using density functional theory and Bader analysis showing that the spatially separated components have either zero charge, corresponding to weak interactions, or integer charge, indicating ionic bonding

Conventional radiography requires a MRI-estimated bone volume loss of 20% to 30% to allow certain detection of bone erosions in rheumatoid arthritis metacarpophalangeal joints

The aim of this study was to demonstrate the ability of conventional radiography to detect bone erosions of different sizes in metacarpophalangeal (MCP) joints of rheumatoid arthritis (RA) patients using magnetic resonance imaging (MRI) as the standard reference. A 0.2 T Esaote dedicated extremity MRI unit was used to obtain axial and coronal T1-weighted gradient echo images of the dominant 2nd to 5th MCP joints of 69 RA patients. MR images were obtained and evaluated for bone erosions according to the OMERACT recommendations. Conventional radiographs of the 2nd to 5th MCP joints were obtained in posterior-anterior projection and evaluated for bone erosions. The MRI and radiography readers were blinded to each other's assessments. Grade 1 MRI erosions (1% to 10% of bone volume eroded) were detected by radiography in 20%, 4%, 7% and 13% in the 2nd, 3rd, 4th and 5th MCP joint, respectively. Corresponding results for grade 2 erosions (11% to 20% of bone volume eroded) were 42%, 10%, 60% and 24%, and for grade 3 erosions (21% to 30% of bone volume eroded) 75%, 67%, 75% and 100%. All grade 4 (and above) erosions were detected on radiographs. Conventional radiography required a MRI-estimated bone erosion volume of 20% to 30% to allow a certain detection, indicating that MRI is a better method for detection and grading of minor erosive changes in RA MCP joints