Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li7La3Zr2O12 solid electrolytes.

Batteries based on solid-state electrolytes, including Li7La3Zr2O12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. These examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

Paradigms of frustration in superionic solid electrolytes.

Superionic solid electrolytes have widespread use in energy devices, but the fundamental motivations for fast ion conduction are often elusive. In this Perspective, we draw upon atomistic simulations of a wide range of superionic conductors to illustrate some ways frustration can lower diffusion cation barriers in solids. Based on our studies of halides, oxides, sulfides and hydroborates and a survey of published reports, we classify three types of frustration that create competition between different local atomic preferences, thereby flattening the diffusive energy landscape. These include chemical frustration, which derives from competing factors in the anion-cation interaction; structural frustration, which arises from lattice arrangements that induce site distortion or prevent cation ordering; and dynamical frustration, which is associated with temporary fluctuations in the energy landscape due to anion reorientation or cation reconfiguration. For each class of frustration, we provide detailed simulation analyses of various materials to show how ion mobility is facilitated, resulting in stabilizing factors that are both entropic and enthalpic in origin. We propose the use of these categories as a general construct for classifying frustration in superionic conductors and discuss implications for future development of suitable descriptors and improvement strategies. This article is part of the Theo Murphy meeting issue 'Understanding fast-ion conduction in solid electrolytes'.

Impacts of vacancy-induced polarization and distortion on diffusion in solid electrolyte Li3OCl.

Lithium-rich oxychloride antiperovskites are promising solid electrolytes for enabling next-generation batteries. Here, we report a comprehensive study varying Li+ concentrations in [Formula: see text] using ab initio molecular dynamics simulations. The simulations accurately capture the complex interactions between Li+ vacancies ([Formula: see text]), the dominant mobile species in [Formula: see text]. The [Formula: see text] polarize and distort the host lattice, inducing additional non-vacancy-mediated diffusion mechanisms and correlated diffusion events that reduce the activation energy barrier at concentrations as low as 1.5% [Formula: see text]. Our analyses of discretized diffusion events in both space and time illustrate the critical interplay between correlated dynamics, polarization and local distortion in promoting ionic conductivity in [Formula: see text]. This article is part of the Theo Murphy meeting issue 'Understanding fast-ion conduction in solid electrolytes'.

Microbe-Assisted Nanocomposite Anodes for Aqueous Li-Ion Batteries.

With the rapid increase in the use of lithium-ion batteries (LIBs), the development of safe LIBs has become an important social issue. Replacing flammable organic liquid electrolytes in current LIBs with water can be an alternative route to resolve this safety concern. The water-in-salt (WIS) electrolytes received great attention as next-generation electrolytes due to their large electrochemical stability window. However, their high cathodic limit remains as a challenge, impeding the use of low-potential anodes. Here, we report the first biodirected synthesis of carbonaceous layers on anodes to use them as interlayers that prevent a direct contact of water molecules to anode particles. High-aspect ratio microbes are utilized as precursors of carbonaceous layers on TiO2 nanoparticles (m-TiO2) to enhance the conductivity and to reduce the electrolysis of WIS electrolytes. We selected the cylindrical shape of microbes that offers geometric diversity, providing us a toolkit to investigate the effect of microbe length in forming the network in binary composites and their impacts on the battery performance with WIS electrolytes. Using microbes with varying aspect ratios, the optimal microbe size to maximize the battery performance is determined. The effects of storage time on microbe size are also studied. Compared to uncoated TiO2 anodes, m-TiO2 exhibited 49% higher capacity at the 40th cycle and enhanced the cycle life close to anodes made with a conventional carbon precursor while using an 11% less amount of carbon. We performed density functional theory calculations to unravel the underlying mechanism of the performance improvement using microbe-derived carbon layers. Computational results show that high amounts of pyridinic nitrogen present in the peptide bonds in microbes are expected to slow down the water diffusion. Our findings provide key insights into the design of an interlayer for WIS anodes and open an avenue to fabricate energy storage materials using biomaterials.

Investigation of the Importance of Protein 3D Structure for Assessing Conservation of Lysine Acetylation Sites in Protein Homologs.

Acetylation is a protein post-translational modification (PTM) that can affect a variety of cellular processes. In bacteria, two PTM Nε-acetylation mechanisms have been identified: non-enzymatic/chemical acetylation via acetyl phosphate or acetyl coenzyme A and enzymatic acetylation via protein acetyltransferases. Prior studies have shown that extensive acetylation of Nε-lysine residues of numerous proteins from a variety of bacteria occurs via non-enzymatic acetylation. In Escherichia coli, new Nε-lysine acetyltransferases (KATs) that enzymatically acetylate other proteins have been identified, thus expanding the repertoire of protein substrates that are potentially regulated by acetylation. Therefore, we designed a study to leverage the wealth of structural data in the Protein Data Bank (PDB) to determine: (1) the 3D location of lysine residues on substrate proteins that are acetylated by E. coli KATs, and (2) investigate whether these residues are conserved on 3D structures of their homologs. Five E. coli KAT substrate proteins that were previously identified as being acetylated by YiaC and had 3D structures in the PDB were selected for further analysis: adenylate kinase (Adk), isocitrate dehydrogenase (Icd), catalase HPII (KatE), methionyl-tRNA formyltransferase (Fmt), and a peroxide stress resistance protein (YaaA). We methodically compared over 350 protein structures of these E. coli enzymes and their homologs; to accurately determine lysine residue conservation requires a strategy that incorporates both flexible structural alignments and visual inspection. Moreover, our results revealed discrepancies in conclusions about lysine residue conservation in homologs when examining linear amino acid sequences compared to 3D structures.

Ten simple rules for an inclusive summer coding program for non-computer-science undergraduates.

Since 2015, we have run a free 9-week summer program that provides non-computer science (CS) undergraduates at San Francisco State University (SFSU) with experience in coding and doing research. Undergraduate research experiences remain very limited at SFSU and elsewhere, so the summer program provides opportunities for many more students beyond the mentoring capacity of our university laboratories. In addition, we were concerned that many students from historically underrepresented (HU) groups may be unable to take advantage of traditional summer research programs because these programs require students to relocate or be available full time, which is not feasible for students who have family, work, or housing commitments. Our program, which is local and part-time, serves about 5 times as many students as a typical National Science Foundation (NSF) Research Experiences for Undergraduates (REU) program, on a smaller budget. Based on our experiences, we present 10 simple rules for busy faculty who want to create similar programs to engage non-CS HU undergraduates in computational research. Note that while some of the strategies we implement are based on evidence-based publications in the social sciences or education research literature, the original suggestions we make here are based on our trial-and-error experiences, rather than formal hypothesis testing.