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

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

Coherent energy and force uncertainty in deep learning force fields

In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quantify aleatoric uncertainty in the predicted energies, a widely used modeling approach involves predicting both a mean and variance for each energy value. However, this model is not differentiable under the usual white noise assumption, so energy uncertainty does not naturally translate to force uncertainty. In this work we propose a machine learning potential energy model in which energy and force aleatoric uncertainty are linked through a spatially correlated noise process. We demonstrate our approach on an equivariant messages passing neural network potential trained on energies and forces on two out-of-equilibrium molecular datasets. Furthermore, we also show how to obtain epistemic uncertainties in this setting based on a Bayesian interpretation of deep ensemble models.Comment: Presented at Advancing Molecular Machine Learning - Overcoming Limitations [ML4Molecules], ELLIS workshop, VIRTUAL, December 8, 2023, unofficial NeurIPS 2023 side-even

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

Skovens tilbagegang i Slesvig og Holsten

Forfatteren gennemgår i hovedtræk skovenes udvikling i Slesvig-Holsten siden oldtiden. Langt tilbage var der stor skovrigdom. Meget blev ryddet, men skovene voksede igen under agrarkrisen i 1300-tallet. Derefter kom der igen pres på skovene. De bulhuse, som prægede meget af landsdelen, krævede meget træ, og det samme gjorde digebyggeri i marsken, anlæg af fæstninger og træ til skibe og trækul. I det 17. århundrede drev besættelsesstyrker rovdrift på skovene. Fra 1600-tallet og frem skulle en række forordninger standse ødelæggelserne, og der blev taget inititiativ til at plante skov, så udviklingen kan siges at vende i det 18. århundrede, omend det først slog reelt igennem i det 19

CePdAl - a Kondo lattice with partial frustration

Magnetic frustration, which is well-defined in insulating systems with localized magnetic moments, yields exotic ground states like spin ices, spin glasses, or spin liquids. In metals magnetic frustration is less well defined because of the incipient delocalization of magnetic moments by the interaction with conduction electrons, viz., the Kondo effect. Hence, the Kondo effect and magnetic frustration are antithetic phenomena. Here we present experimental data of electrical resistivity, magnetization, specific heat and neutron diffraction on CePdAl, which is one of the rare examples of a geometrically frustrated Kondo lattice, demonstrating that the combination of Kondo effect and magnetic frustration leads to an unusual ground state.Comment: 8 pages, 6 figure

From Offshore Operation to Onshore Simulator: Using Visualized Ethnographic Outcomes to Work with Systems Developers

This paper focuses on the process of translating insights from a Computer Supported Cooperative Work (CSCW)-based study, conducted on a vessel at sea, into a model that can assist systems developers working with simulators, which are used by vessel operators for training purposes on land. That is, the empirical study at sea brought about rich insights into cooperation, which is important for systems developers to know about and consider in their designs. In the paper, we establish a model that primarily consists of a ‘computational artifact’. The model is designed to support researchers working with systems developers. Drawing on marine examples, we focus on the translation process and investigate how the model serves to visualize work activities; how it addresses relations between technical and computational artifacts, as well as between functions in technical systems and functionalities in cooperative systems. In turn, we link design back to fieldwork studies

Small class sizes for improving student achievement in primary and secondary schools: a systematic review

Reducing class size is seen as a way of improving student performance. But larger class sizes help control education budgets. The evidence suggests at best a small effect on reading achievement. There is a negative, but statistically insignificant, effect on mathematics, so it cannot be ruled out that some children may be adversely affected. A total of 127 studies, consisting of 148 papers, met the inclusion criteria. These 127 studies analysed 55 different populations from 41 different countries. A large number of studies (45) analysed data from the Student Teacher Achievement Ratio (STAR) experiment which was for class size reduction in grade K-3 in the US in the eighties. However only ten studies, including four of the STAR programme, could be included in the meta-analysis. For the non-STAR studies the primary study effect sizes for reading were close to zero but the weighted average was positive and statistically significant. There was some inconsistency in the direction of the primary study effect sizes for mathematics and the weighted average effect was negative and statistically non-significant. The STAR results are more positive, but do not change the overall finding. All reported results from the studies analysing STAR data indicated a positive effect of smaller class sizes for both reading and maths, but the average effects are small

Njord: a fishing trawler dataset

Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and classification of fish from images or videos using machine learning or other analysis methods attracts growing attention. Surprisingly, little work has been done that considers what is happening on board the fishing vessels. On the deck of the boats, a lot of data and important information are generated with potential applications, such as automatic detection of accidents or automatic reporting of fish caught. This paper presents Njord, a fishing trawler dataset consisting of surveillance videos from a modern off-shore fishing trawler at sea. The main goal of this dataset is to show the potential and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several possible research questions this dataset could help answer