Conductivity prediction model for ionic liquids using machine learning.

Ionic liquids (ILs) are salts, composed of asymmetric cations and anions, typically existing as liquids at ambient temperatures. They have found widespread applications in energy storage devices, dye-sensitized solar cells, and sensors because of their high ionic conductivity and inherent thermal stability. However, measuring the conductivity of ILs by physical methods is time-consuming and expensive, whereas the use of computational screening and testing methods can be rapid and effective. In this study, we used experimentally measured and published data to construct a deep neural network capable of making rapid and accurate predictions of the conductivity of ILs. The neural network is trained on 406 unique and chemically diverse ILs. This model is one of the most chemically diverse conductivity prediction models to date and improves on previous studies that are constrained by the availability of data, the environmental conditions, or the IL base. Feature engineering techniques were employed to identify key chemo-structural characteristics that correlate positively or negatively with the ionic conductivity. These features are capable of being used as guidelines to design and synthesize new highly conductive ILs. This work shows the potential for machine-learning models to accelerate the rate of identification and testing of tailored, high-conductivity ILs.

Hypomorphic bimA(APC3) alleles cause errors in chromosome metabolism that activate the DNA damage checkpoint blocking cytokinesis in Aspergillus nidulans.

The Aspergillus nidulans sepI(+) gene has been implicated in the coordination of septation with nuclear division and cell growth. We find that the temperature-sensitive (ts) sepI1 mutation represents a novel allele of bimA(APC3), which encodes a conserved component of the anaphase-promoting complex/cyclosome (APC/C). We have characterized the septation, nuclear division, cell-cycle checkpoint defects, and DNA sequence alterations of sepI1 (renamed bimA10) and two other ts lethal bimA(APC3) alleles, bimA1 and bimA9. Our observations that bimA9 and bimA10 strains had morphologically abnormal nuclei, chromosome segregation defects, synthetic phenotypes with mutations in the DNA damage checkpoint genes uvsB(MEC1/rad3) or uvsD(+), and enhanced sensitivity to hydroxyurea strongly suggest that these strains accumulate errors in DNA metabolism. We found that the aseptate phenotype of bimA9 and bimA10 strains was substantially relieved by mutations in uvsB(MEC1/rad3) or uvsD(+), suggesting that the presence of a functional DNA damage checkpoint inhibits septation in these bimA(APC3) strains. Our results demonstrate that mutations in bimA(APC3) lead to errors in DNA metabolism that indirectly block septation.