Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future

Scripts and Models for "Predicting electronic structures at any length scale with machine learning"

Scripts and Models for "Predicting the Electronic Structure of Matter on Ultra-Large Scales" This data set contains scripts and models to reproduce the results of our manuscript "Physics-informed Machine Learning Models for Scalable Density Functional Theory Calculations". The scripts are supposed to be used in conjunction with the ab-initio data sets also published alongside our research article. Requirements python>=3.7.x mala>=1.1.0 ase numpy Contents | Folder name | Description | |------------------|--------------------------------------------------| | data_analysis/ | Run script for RDF calculations | | model_inference/ | Run script to run inference based on MALA models | | model_training/ | Run script to train MALA models | | trained_models/ | Trained models for beryllium and aluminium

Spectroscopic Characterization of Rocksalt-Type Aluminum Nitride

Starting from nanocrystalline and submicron wurtzite-aluminum nitride (w-AlN) powder rocksalt structure (rs-AlN) samples were synthesized by two different methods of shock wave recovery experiments. The resulting samples contained up to 86% rs-AlN, stable at room temperature, giving for the first time the possibility to comprehensively characterize the material by powder X-ray diffraction, Fourier transform infrared (IR), Raman, and ²⁷Al NMR spectroscopy. Raman and IR modes were calculated by density functional theory, allowing for the interpretation of the respective experimental spectra. By ²⁷Al NMR the chemical shift of rs-AlN was determined, and the quadrupolar coupling constant was estimated