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

DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction

We introduce DeepDFT, a deep learning model for predicting the electronic charge density around atoms, the fundamental variable in electronic structure simulations from which all ground state properties can be calculated. The model is formulated as neural message passing on a graph, consisting of interacting atom vertices and special query point vertices for which the charge density is predicted. The accuracy and scalability of the model are demonstrated for molecules, solids and liquids. The trained model achieves lower average prediction errors than the observed variations in charge density obtained from density functional theory simulations using different exchange correlation functionals.Comment: Workshop paper presented at Machine Learning for Molecules Workshop at NeurIPS 2020. Implementation and pretrained model are available at https://github.com/peterbjorgensen/DeepDF

Critical Path Driven Cosynthesis for Heterogeneous Target Architectures

This paper presents a critical path driven algorithm to produce a static schedule of a single-rate system onto a heterogeneous target architecture. Our algorithm is a list based scheduling algorithm which concurrently assigns tasks to processors and allocates nets to interprocessor communication. Experimental results show that our algorithm is able to find good results, as compared to other methods, in small amount of CPU time. 1. Introduction Embedded systems are usually implemented using a mixture of technologies including off-the-shelf components, such as microprocessors, and dedicated hardware, such as full- or semi-custom ASICs. This results in a heterogeneous architecture, in which also the communication links between the components uses different technologies, i.e. point-topoint communication and busses with various bandwidths. In this paper we address the problem of cosynthesis of single-rate systems onto a heterogeneous target architecture. In particular, we solve the problem..

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

Prioritering af investeringer

Trafikstyrelsen fik i 2008 udarbejdet rapporten ”Masterplan for trafikterminaler i Øst-danmark” i samarbejde med Banedanmark, DSB og Movia. I Masterplanen analyseredes udvalgte trafikterminaler i landsdelen, med det formål at tilvejebringe et fælles grundlag for forbedring af disse. Masterplanen havde primært karakter af et idékatalog, og indeholdte ingen prioritering af tiltagene på tværs af stationerne. For at få et overblik over, hvilke tiltag og/eller stationer der kan forventes at give den højeste kundetilfredshed og passagerfremgang, bad Terminalsamarbejdet Incentive Partners og Public Arkitekter om at udvikle en metode til at prioritere de forskellige tiltag. Projektet et eksempel på, hvordan man med relativt enkle midler kan opstille et velfunderet beslutningsgrundlag, således at man bedre bliver i stand til at prioritere de investeringer, som skaber mest mulig værdi for kunderne. Projektet giver således også nogle fingerpeg om, hvordan man bedst og billigst kan indrette den kollektive trafik med henblik på at tiltrække flere passagerer

Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces

Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to detect when predictions are unreliable to avoid wrong or misleading results. Here, we present a complete framework for training and recalibrating graph neural network ensemble models to produce accurate predictions of energy and forces with calibrated uncertainty estimates. The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy. The method is demonstrated and evaluated on two challenging, publicly available datasets, ANI-1x (Smith et al.) and Transition1x (Schreiner et al.), both containing diverse conformations far from equilibrium. A detailed analysis of the predictive performance and uncertainty calibration is provided. In all experiments, the proposed method achieved low prediction error and good uncertainty calibration, with predicted uncertainty correlating with expected error, on energy and forces. To the best of our knowledge, the method presented in this paper is the first to consider a complete framework for obtaining calibrated epistemic and aleatoric uncertainty predictions on both energy and forces in ML potentials