During the cycling of a lithium-ion battery, the active storage materials experience a volume change due to the intercalation process, often causing fracture, loss of contact among the active particles, and finally the degradation of the whole electrode. Here, we present a model for the lithium diffusion and stress generation in a particle of active material having a phase change. In our approach the driving force for diffusion is deduced from basic thermodynamics and statistical physics. The parameters of our model may be obtained from experimental measurements, or from ab-initio density functional theory calculations if the values are experimentally not accessible, as in the evaluation of new, as yet unsynthesized, computer-designed materials. We present results from simulations representing graphite known to experience phase-changes or staging. In some of our simulations, the particles are coupled to a battery simulator to apply conditions experienced in a functioning cell. We find that staging causes a significant increase in particle stress in comparison to when it is absent.

The single-particle density of states and the tunneling conductance are studied for a twodimensional BCS-like Hamiltonian with a d x 2 −y 2 -gap and phase fluctuations. The latter are treated by a classical Monte Carlo simulation of an XY model. Comparison of our results with recent scanning tunneling spectra of Bi-based high-Tc cuprates supports the idea that the pseudogap behavior observed in these experiments can be understood as arising from phase fluctuations of a d x 2 −y 2 pairing gap whose amplitude forms on an energy scale set by T M F c well above the actual superconducting transition.

Over-lithiated transition metal oxides are currently the most promising high energy cathode materials. DFT calculations show that Li2MnO3 becomes increasingly unstable upon delithiation and experiences a driving force for either oxygen release from the surface or peroxide formation in the bulk. Both mechanisms are shown to be detrimental for the electrochemistry.

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71,178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasihar-monic contributions.