Thermodynamical and Dynamical Instabilities from Ab initio Electronic-Structure Calculations

NR 2014080

Thermodynamical and Dynamical Instabilities from Ab initio Electronic-Structure Calculations

NR 2014080

Screening of bimetallic electrocatalysts for water purification with machine learning

Electrocatalysis provides a potential solution to NO3 - pollution in wastewater by converting it to innocuous N2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. In response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate NO3 - reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu-Co and Cu-Ag based compounds that merit further investigation

Towards a Mechanistic Model of Solid-Electrolyte Interphase Formation and Evolution in Lithium-ion Batteries

The formation of passivation films by interfacial reactions, though critical for applications ranging from advanced alloys to electrochemical energy storage, is often poorly understood. In this work, we explore the formation of an exemplar passivation film, the solid-electrolyte interphase (SEI), which is responsible for stabilizing lithium-ion batteries. Using stochastic simulations based on quantum chemical calculations and data-driven chemical reaction networks, we directly model competition between SEI products at a mechanistic level for the first time. Our results recover the Peled-like separation of the SEI into inorganic and organic domains resulting from rich reactive competition without fitting parameters to experimental inputs. By conducting accelerated simulations at elevated temperature, we track SEI evolution, confirming the postulated reduction of lithium ethylene monocarbonate to dilithium ethylene monocarbonate and H2. These findings furnish fundamental insights into the dynamics of SEI formation and illustrate a path forward towards a predictive understanding of electrochemical passivation

Quantum chemical calculations of lithium-ion battery electrolyte and interphase species.

Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte (LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural, thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties

High-throughput computational study of halide double perovskite inorganic compounds

Double perovskite halides are a class of materials with diverse chemistries that are amenable to solution-based synthesis routes, and display a range of properties for a variety of potential applications. Starting from a consideration of the octahedral and tolerance factors of ∼2000 candidate double perovskite compounds, we compute structural, electronic, and transport properties of ∼1000 using first-principles calculations based on density-functional-theory methods. The computational results have been assembled in a database that is accessible through the Materials Project online. As one potential application, double perovskites are candidates in the search for lead-free halide photovoltaic absorbers. We present the application of our database to aid the discovery of new double perovskite halide photovoltaic materials, by combining the results with optical absorption and phonon stability calculations. From three distinct classes of chemistries, 11 compounds were identified as promising solar absorbers and the complex chemical trends for band gap within each of these are analyzed, to provide guidelines for the use of substitutional alloying as a means of further tuning the electronic structure. Other possible applications of the database are also discussed.NRF (Natl Research Foundation, S’pore)Accepted versio