Machine learning interatomic potential for simulations of carbon at extreme conditions

Willman, Jonathan; Cong, Kien Nguyen; Williams, Ashley; Belonoshko, Anatoly B.; Moore, Stan Gerald; Thompson, Aidan P.; Wood, Mitchell; Oleynik, Ivan

doi:10.1103/physrevb.106.l180101

Cited by 27 publications

(10 citation statements)

References 55 publications

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“…Nevertheless, changing the PS through extreme conditions would require the production of new stoichiometries that compete with the more than three and a half million of different formulas reported over the history of chemistry. Extreme conditions is also an active subject of research in quantum chemistry 47 as understanding the dramatic change on the energy gaps between core and valence electrons is of central importance for chemistry and planetary exploration.…”

Section: Resultsmentioning

confidence: 99%

The six stages of the convergence of the periodic system to its final structure

et al. 2023

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The periodic system encodes order and similarity among chemical elements arising from known substances at a given time that constitute the chemical space. Although the system has incorporated new elements, the connection with the remaining space is still to be analysed, which leads to the question of how the exponentially growing space has affected the periodic system. Here we show, by analysing the space between 1800 and 2021, that the system has converged towards its current stable structure through six stages, respectively characterised by the finding of elements (1800–1826), the emergence of the core structure of the system (1826–1860), its organic chemistry bias (1860–1900) and its further stabilisation (1900–1948), World War 2 new chemistry (1948–1980) and the system final stabilisation (1980–). Given the self-reinforced low diversity of the space and the limited chemical possibilities of the elements to be synthesised, we hypothesise that the periodic system will remain largely untouched.

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Section: Resultsmentioning

confidence: 99%

The six stages of the convergence of the periodic system to its final structure

et al. 2023

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“…[58][59][60][61] Finally, the ability of NN potentials to identify minimum energy carbon structures is of critical importance for a variety of chemical and materials applications. Recent work on large-scale MD simulations of carbon systems 62,63 has been largely driven by the interest in understanding carbon cluster formation post combustion. Yet, despite these potentials having passed many large-scale tests, such as the prediction of the carbon phase diagram, perhaps the addition of low-energy carbon clusters would provide an even more stringent test for such potentials.…”

Section: Discussionmentioning

confidence: 99%

Neural network atomistic potentials for global energy minima search in carbon clusters

Tkachenko,

Nebgen

et al. 2023

Phys. Chem. Chem. Phys.

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“…Finally, even though carbon does not readily form structures such as fcc, bcc, and sc, it is advisible to reproduce properties of these structures as well, as they may occur in MD simulations under nonequilibrium conditions or at high pressure. For example, atomistic models of amorphous carbon are often generated by melting an sc lattice of C atoms, ,, and the sc phase is even found to be stable at extreme pressures. , …”

Section: Validationmentioning

confidence: 99%

Atomic Cluster Expansion for Quantum-Accurate Large-Scale Simulations of Carbon

Qamar

Mrovec

Lysogorskiy

et al. 2023

J. Chem. Theory Comput.

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We present an atomic cluster expansion (ACE) for carbon that improves over available classical and machine learning potentials. The ACE is parametrized from an exhaustive set of important carbon structures over extended volume and energy ranges, computed using density functional theory (DFT). Rigorous validation reveals that ACE accurately predicts a broad range of properties of both crystalline and amorphous carbon phases while being several orders of magnitude more computationally efficient than available machine learning models. We demonstrate the predictive power of ACE on three distinct applications: brittle crack propagation in diamond, the evolution of amorphous carbon structures at different densities and quench rates, and the nucleation and growth of fullerene clusters under high-pressure and high-temperature conditions.

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Machine learning interatomic potential for simulations of carbon at extreme conditions

Cited by 27 publications

References 55 publications

The six stages of the convergence of the periodic system to its final structure

The six stages of the convergence of the periodic system to its final structure

Neural network atomistic potentials for global energy minima search in carbon clusters

Atomic Cluster Expansion for Quantum-Accurate Large-Scale Simulations of Carbon

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