A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations – this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of machine-learning surrogate models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.
We present a numerical modeling workflow based on machine learning (ML) which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.
Conditional Point Sampling (CoPS) is a recently developed stochastic media transport algorithm that has demonstrated a high degree of accuracy in 1-D and 3-D calculations for binary mixtures with Markovian mixing statistics. In theory, CoPS has the capacity to be accurate for material structures beyond just those with Markovian statistics. However, realizing this capability will require development of conditional probability functions (CPFs) that are based, not on explicit Markovian properties, but rather on latent properties extracted from material structures. Here, we describe a first step towards extracting these properties by developing CPFs using deep neural networks (DNNs). Our new approach lays the groundwork for enabling accurate transport on many classes of stochastic media. We train DNNs on ternary stochastic media with Markovian mixing statistics and compare their CPF predictions to those made by existing CoPS CPFs, which are derived based on Markovian mixing properties. We find that the DNN CPF predictions usually outperform the existing approximate CPF predictions, but with wider variance. In addition, even when trained on only one material volume realization, the DNN CPFs are shown to make accurate predictions on other realizations that have the same internal mixing behavior. We show that it is possible to form a useful CoPS CPF by using a DNN to extract correlation properties from realizations of stochastically mixed media, thus establishing a foundation for creating CPFs for mixtures other than those with Markovian mixing, where it may not be possible to derive an accurate analytical CPF.
This report summarizes the results of an LDRD focused on developing and demonstrating statistically rigorous methods for analyzing and comparing complex activities from remote sensing data. Identifying activity from remote sensing data, particularly those that play out over time and span multiple locations, often requires extensive manual effort because of the variety of features that describe the activity and the required domain expertise. Our results suggest that there are some hidden challenges in extracting and representing activities in sensor data. In particular, we found that the variability in the underlying behaviors can be difficult to overcome statistically, and the report identifies several examples of the issue. We discuss key lessons learned in the context of the project, and finally conclude with recommendations on next steps and future work.