Pressure–Temperature Phase Diagram of Lithium, Predicted by Embedded Atom Model Potentials

Dorrell, Jordan; Partay, L.B.

doi:10.1021/acs.jpcb.0c03882

Cited by 19 publications

(16 citation statements)

References 52 publications

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“…10 shows, NS not only identifies the most stable structure, but provides information on the thermodynamically relevant metastable phases as well. Similar extensive phase diagram comparison studies have been performed for a range of EAM type potentials of iron [85] and lithium [52].…”

Section: Phase Diagram Of Materialsmentioning

confidence: 88%

“…In addition, there is a systematic shift, usually underestimating the temperature of the vaporisation transition, and overestimating that of the melting transition. The magnitude of this effect varies depending on the studied system and pressure, but is generally between 3 and 8% if using 64 atoms [51,52]. Since its introduction by Skilling, several modifications and improvements have been suggested to increase the efficiency of the sampling algorithm.…”

Section: Parameters and Performancementioning

confidence: 99%

“…At each enthalpy value, the relative widths of the basins correspond to their relative configuration space volumes, and the overall width is scaled to be exponential with enthalpy. The ground-state mixed stacking basin is not closed at the bottom to indicate that we have no data on its minimum enthalpy, although it is likely to be close to the lowest sampled value (EAM) [52] is shown in Fig. 11, using the similarity defined by the configuration-averaged SOAP overlap [87,88] with n max = 8, l max = 8, and a cut-off of three times the nearest-neighbor distance of 2.55Å(from the NS trajectory's minimum energy configuration).…”

Section: Phase Diagram Of Materialsmentioning

confidence: 99%

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Nested sampling for materials

2021

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We review the materials science applications of the nested sampling (NS) method, which was originally conceived for calculating the evidence in Bayesian inference. We describe how NS can be adapted to sample the potential energy surface (PES) of atomistic systems, providing a straightforward approximation for the partition function and allowing the evaluation of thermodynamic variables at arbitrary temperatures. After an overview of the basic method, we describe a number of extensions, including using variable cells for constant pressure sampling, the semi-grand-canonical approach for multicomponent systems, parallelizing the algorithm, and visualizing the results. We cover the range of materials applications of NS from the past decade, from exploring the PES of Lennard–Jones clusters to that of multicomponent condensed phase systems. We highlight examples how the information gained via NS promotes the understanding of materials properties through a novel way of visualizing the PES, identifying thermodynamically relevant basins, and calculating the entire pressure–temperature(–composition) phase diagram. Graphic abstract

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Section: Phase Diagram Of Materialsmentioning

confidence: 88%

Section: Parameters and Performancementioning

confidence: 99%

Section: Phase Diagram Of Materialsmentioning

confidence: 99%

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Nested sampling for materials

2021

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“…Proving capable of reproducing experimental observations and being relatively simple to implement, the Embedded-Atom Model is now routinely used in molecular dynamic simulations [18,27]. In particular, it has been applied in a variety of metallic systems [21], including alkali metals Li, Na, K [19,26,37], transition metals Fe, Ni, Cu, Pd, Ag, Pt, Au [13,22,26,27], post-transition metals Al, Pb [13,24,34], the metalloid Si [3], and some of their alloys [13,25].…”

Section: Introductionmentioning

confidence: 99%

Lattice ground states for embedded-atom models in 2D and 3D

2021

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The Embedded-Atom Model (EAM) provides a phenomenological description of atomic arrangements in metallic systems. It consists of a configurational energy depending on atomic positions and featuring the interplay of two-body atomic interactions and nonlocal effects due to the corresponding electronic clouds. The purpose of this paper is to mathematically investigate the minimization of the EAM energy among lattices in two and three dimensions. We present a suite of analytical and numerical results under different reference choices for the underlying interaction potentials. In particular, Gaussian, inverse-power, and Lennard-Jones-type interactions are addressed.

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“…In [22] the phase diagram of lithium is examined in a wide temperature and pressure range from first-principles complementing experiment. Recently, in [23] authors compute the former transition lines with the existing classical potentials.…”

Section: Introductionmentioning

confidence: 99%

Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams

et al. 2021

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Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free-energy functions using data of various origins. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on a Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with the existing studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.

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Pressure–Temperature Phase Diagram of Lithium, Predicted by Embedded Atom Model Potentials

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 19 publications

References 52 publications

Nested sampling for materials

Nested sampling for materials

Lattice ground states for embedded-atom models in 2D and 3D

Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams

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