William W. Tipton

A grand canonical genetic algorithm for the prediction of multi-component phase diagrams and testing of empirical potentials

We present an evolutionary algorithm which predicts stable atomic structures and phase diagrams by searching the energy landscape of empirical and ab initio Hamiltonians. Composition and geometrical degrees of freedom may be varied simultaneously. We show that this method utilizes information from favorable local structure at one composition to predict that at others, achieving far greater efficiency of phase diagram prediction than a method which relies on sampling compositions individually. We detail this and a number of other efficiency-improving techniques implemented in the genetic algorithm for structure prediction code that is now publicly available. We test the efficiency of the software by searching the ternary Zr-Cu-Al system using an empirical embedded-atom model potential. In addition to testing the algorithm, we also evaluate the accuracy of the potential itself. We find that the potential stabilizes several correct ternary phases, while a few of the predicted ground states are unphysical. Our results suggest that genetic algorithm searches can be used to improve the methodology of empirical potential design.

Structure and Stability Prediction of Compounds with Evolutionary Algorithms

Crystal structure prediction is a long-standing challenge in the physical sciences. In recent years, much practical success has been had by framing it as a global optimization problem, leveraging the existence of increasingly robust and accurate free energy calculations. This optimization problem has often been solved using evolutionary algorithms (EAs). However, many choices are possible when designing an EA for structure prediction, and innovation in the field is ongoing. We review the current state of evolutionary algorithms for crystal structure and composition prediction and discuss the details of methodological and algorithmic choices. Finally, we review the application of these algorithms to many systems of practical and fundamental scientific interest.

Pressure-induced structural transitions in europium to 92 GPa

Synchrotron x-ray diffraction experiments have been carried out on europium metal at ambient temperature to pressures as high as 92 GPa (0.92 Mbar). Following the well-known bcc-to-hcp transition at 12 GPa, a mixed-phase region is observed from 18 to 66 GPa until finally a single orthorhombic (Pnma) phase persists from 66 to 92 GPa. These results are compared to predictions from density functional theory calculations. Under pressure the relatively large molar volume

Computationally driven experimental discovery of the CeIr4In compound

{V}_{\mathrm{mol}}

Importance of high-angular-momentum channels in pseudopotentials for quantum Monte Carlo

of divalent Eu is rapidly diminished, equaling or falling below

ReaxFF molecular dynamics simulations on lithiated sulfur cathode materials

{V}_{\mathrm{mol}}(P)

Ab initio based empirical potential used to study the mechanical properties of molybdenum

for neighboring trivalent lanthanides above 15 GPa. The present results suggest that above 15 GPa Eu is neither divalent nor fully trivalent to pressures as high as 92 GPa.

Structures, phase stabilities, and electrical potentials of Li-Si battery anode materials

We present a combined experimental and computational methodology for the discovery of new materials. Density functional theory (DFT) formation energy calculations allow us to predict the stability of various hypothetical structures. We demonstrate this approach by computationally predicting the Ce-Ir-In ternary phase diagram. We predict previously-unknown compounds CeIr

Grand-canonical evolutionary algorithm for the prediction of two-dimensional materials

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Ab initio prediction of the Li 5 Ge 2 Zintl compound

In and Ce