PhD Project - Computational discovery of novel oxide-based X-ray detectors at University of Birmingham, listed on FindAPhD.com

Calculation of the center of mass of a group of particles in a periodically repeating cell is an important aspect of chemical and physical simulations. One popu

Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable force fields for organic molecules created using state-of-the-art machine learning technology and first-principles reference data computed with a high level of quantum mechanical theory. MACE-OFF demonstrates the remarkable capabilities of short-range models by accurately predicting a wide variety of gas- and condensed-phase properties of molecular systems. It produces accurate, easy-to-converge dihedral torsion scans of unseen molecules as well as reliable descriptions of molecular crystals and liquids, including quantum nuclear effects. We further demonstrate the capabilities of MACE-OFF by determining free energy surfaces in explicit solvent as well as the folding dynamics of peptides and nanosecond simulations of a fully solvated protein. These developments enable first-principles simulations of molecular systems for the broader chemistry community at high accuracy and relatively low computational cost.

A PhD stipend or integrated PhD stipend in computational materials chemistry is available. Development of new sustainable energy materials, including materia...

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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,...

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...

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.

A postdoc position in computational materials chemistry is available to design sustainable materials with tailored disorder using computational chemistry and...

The gender gap in faculty attrition worsens after tenure, according to a new study