Computational screening techniques have been found to be an effective alternative to the trial and error of experimentation for discovery of new materials. With increased interest in development of advanced electrical energy storage systems, it is essential to find new electrolytes that function effectively. This Perspective reviews various methods for screening electrolytes and then describes a hierarchical computational scheme to screen multiple properties of advanced electrical energy storage electrolytes using high-throughput quantum chemical calculations. The approach effectively down-selects a large pool of candidates based on successive property evaluation. As an example, results of screening are presented for redox potentials, solvation energies, and structural changes of ∼1400 organic molecules for nonaqueous redox flow batteries. Importantly, on the basis of high-throughput screening, in silico design of suitable candidate molecules for synthesis and electrochemical testing can be achieved. We anticipate that the computational approach described in this Perspective coupled with experimentation will have a significant role to play in the discovery of materials for future energy needs.

SUMMARYThis paper introduces FireWorks, a workflow software for running high-throughput calculation workflows at supercomputing centers. FireWorks has been used to complete over 50 million CPU-hours worth of computational chemistry and materials science calculations at the National Energy Research Supercomputing Center. It has been designed to serve the demanding high-throughput computing needs of these applications, with extensive support for (i) concurrent execution through job packing, (ii) failure detection and correction, (iii) provenance and reporting for long-running projects, (iv) automated duplicate detection, and (v) dynamic workflows (i.e., modifying the workflow graph during runtime). We have found that these features are highly relevant to enabling modern data-driven and high-throughput science applications, and we discuss our implementation strategy that rests on Python and NoSQL databases (MongoDB). Finally, we present performance data and limitations of our approach along with planned future work.

Through coupled experimental analysis and computational techniques, we uncover the origin of anodic stability for a range of nonaqueous zinc electrolytes. By examination of electrochemical, structural, and transport properties of nonaqueous zinc electrolytes with varying concentrations, it is demonstrated that the acetonitrile-Zn(TFSI)2, acetonitrile-Zn(CF3SO3)2, and propylene carbonate-Zn(TFSI)2 electrolytes can not only support highly reversible Zn deposition behavior on a Zn metal anode (≥99% of Coulombic efficiency) but also provide high anodic stability (up to ∼3.8 V vs Zn/Zn(2+)). The predicted anodic stability from DFT calculations is well in accordance with experimental results, and elucidates that the solvents play an important role in anodic stability of most electrolytes. Molecular dynamics (MD) simulations were used to understand the solvation structure (e.g., ion solvation and ionic association) and its effect on dynamics and transport properties (e.g., diffusion coefficient and ionic conductivity) of the electrolytes. The combination of these techniques provides unprecedented insight into the origin of the electrochemical, structural, and transport properties in nonaqueous zinc electrolytes.

Promising sulfur spinel systems with facile cation mobility are revealed for multivalent cathode applications based on systematical calculation and screening.