Alex Squires

Good @thomasyoungcentre.bsky.social symposium this afternoon on the modelling of battery cathodes and solid-state electrolytes.

Thanks to Marie-Liesse, Alex and Ben for coming to London and present their work.

#chemsky #compchem

TYC Symposium: Batteries - THOMAS YOUNG CENTRE Marie-Liesse Doublet, Montpelier - The Mirage of Anionic Redox for High-Energy Batteries Alexander Squires, Birmingham - Mixed-anion NaTaOxCl6-2x oxychlorides: From crystalline to amorphous networks f...

Next Thursday afternoon, 30/10, the @thomasyoungcentre.bsky.social is organising a symposium in London on modelling battery materials with as speakers Marie-Liesse Doublet (Montpellier), Alex Squires (Birmingham) and Benjamin Morgan (Bath).

Guidelines for robust and reproducible point defect simulations in crystals Many physical properties of functional materials are governed by their impurities rather than their bulk characteristics. Defects in crystals can activate electronic and ionic conductivity, create act...

Defect calculations have many pitfalls and key considerations for achieving good accuracy 🎯

In this perspective, we discuss these issues, how to avoid and how we can make defect simulations more reproducible – particularly important with more ML developments! 📊

chemrxiv.org/engage/chemr...

And because the problem is full of coupled calculations, one poorly described defect might throw out your quantitative thermodynamics completely

I think for me, the main issue that makes it hard to implement for automation and reproducibility is handling edge cases, which are common. Even doped is far from fool-proof and requires a lot of on-the-fly application of domain knowledge to get useful results.

Intrinsic point defect tolerance in selenium for indoor and tandem photovoltaics Selenium has reemerged as a promising absorber material for tandem and indoor photovoltaic (PV) devices due to its elemental simplicity, unique structural features, and wide band gap. However, despite...

Intrinsic & extrinsic (dopant) defect chemistry of trigonal Selenium for PV, incl metastable states & non-radiative recombination ⬇️

Combined theory & expt analysis, we find an intrinsic tolerance to 𝘱𝘰𝘪𝘯𝘵 defects, with GBs/interfaces the limiting factor for PV 📈
pubs.rsc.org/en/Content/A...

Very gracious for David to let me off the leash on this one. Kick-started an agyrodite obsession (though I may be a bit late to the party on this one)

@agsquires.bsky.social has just published this lovely work with wolfgang zeier! pubs.acs.org/doi/10.1021/... @uobchemistry.bsky.social

Seán's efforts with doped are heroic. I can only apologise for some of my commits 👀

But it's now never been easier to start charging about in chemical potential space to find out what your defects are doing!

On the Estimation of Centre of Mass in Periodic Systems Calculation of the centre of mass of a group of particles in a periodically-repeating cell is an important aspect of chemical and physical simulation. One popular approach, described by Bai and Breen,...

Excited to share Harry Richardson’s first paper, which has gone live on arXiv. If you have ever calculated the centre of mass of a simulated molecule, I would recommend giving it a read. arxiv.org/abs/2501.14578 #compchem

Accurate Estimation of Diffusion Coefficients and their Uncertainties from Computer Simulation Self-diffusion coefficients, D*, are routinely estimated from molecular dynamics simulations by fitting a linear model to the observed mean squared displacements (MSDs) of mobile species. MSDs derived from simulations exhibit statistical noise that causes uncertainty in the resulting estimate of D*. An optimal scheme for estimating D* minimizes this uncertainty, i.e., it will have high statistical efficiency, and also gives an accurate estimate of the uncertainty itself. We present a scheme for estimating D* from a single simulation trajectory with a high statistical efficiency and accurately estimating the uncertainty in the predicted value. The statistical distribution of MSDs observable from a given simulation is modeled as a multivariate normal distribution using an analytical covariance matrix for an equivalent system of freely diffusing particles, which we parametrize from the available simulation data. We use Bayesian regression to sample the distribution of linear models that are compatible with this multivariate normal distribution to obtain a statistically efficient estimate of D* and an accurate estimate of the associated statistical uncertainty.

Ending the year with the nice closure of finally getting a long running project published. doi.org/10.1021/acs....