An automated approach for developing neural network interatomic potentials with FLAME

Mirhosseini, Hossein; Tahmasbi, Hossein; Kuchana, Sai Ram; Ghasemi, S. Alireza; Kühne, Thomas D.

doi:10.1016/j.commatsci.2021.110567

Cited by 9 publications

(3 citation statements)

References 72 publications

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“…In spite of the intrinsic limitations of the PBE functional, the adopted numerical methodology has proven to be entirely adequate for a robust physicochemical interpretation of the vibrational spectra in a well-defined crystal environment. In hindsight, the wealth of experimental data collected in this work could further serve as a high-quality benchmark for much-needed developments of accurate classical force-fields for PILs, − thereby paving the way for temporally and spatially resolved MD simulations transcending a well-defined crystal environment or the quality of semilocal DFT functionals.…”

Section: Discussionmentioning

confidence: 97%

Spectroscopic Signatures of Hydrogen-Bonding Motifs in Protonic Ionic Liquid Systems: Insights from Diethylammonium Nitrate in the Solid State

et al. 2021

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Diethylammonium nitrate, [N 0 0 2 2 ][NO 3 ], and its perdeuterated analogue, [N D D 2 2 ] [NO 3 ], were structurally characterized and studied by infrared, Raman, and inelastic neutron scattering (INS) spectroscopy. Using these experimental data along with state-of-the-art computational materials modeling, we report unambiguous spectroscopic signatures of hydrogen-bonding interactions between the two counterions. An exhaustive assignment of the spectral features observed with each technique has been provided, and a number of distinct modes related to NH···O dynamics have been identified. We put a particular emphasis on a detailed interpretation of the high-resolution, broadband INS experiments. In particular, the INS data highlight the importance of conformational degrees of freedom within the alkyl chains, a ubiquitous feature of ionic liquid (IL) systems. These findings also enable an in-depth physicochemical understanding of protonic IL systems, a first and necessary step to the tailoring of hydrogen-bonding networks in this important class of materials.

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Section: Discussionmentioning

confidence: 97%

Spectroscopic Signatures of Hydrogen-Bonding Motifs in Protonic Ionic Liquid Systems: Insights from Diethylammonium Nitrate in the Solid State

et al. 2021

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“…114 Anharmonic lattice-dynamics calculations, however, easily become prohibitively expensive in solid-state calculations, and so using the ML-trained force fields instead can be considered as the most perspective strategy. 115 To date, the anharmonic corrections are usually obtained through the independent mode framework, i.e. , by solving the one-dimensional Schrödinger equation for each mode separately (see, e.g.…”

Section: Crystal Structure Evaluation: Zeroth-order Csp and Beyondmentioning

confidence: 99%

Along the road to crystal structure prediction (CSP) of pharmaceutical-like molecules

Dudek

Drużbicki

2022

CrystEngComm

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“…Hence, the simulation capability that has been made available by ML potentials makes larger scale molecular dynamics simulations with DFT accuracy reachable [30][31][32][33][34][35][36][37][38][39]. That is why they have received a great amount of interest in the community and their implementation in different fields matures rapidly [40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential

Hamedani

Byggmästar

Djurabekova

et al. 2021

Phys. Rev. Materials

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Characterization of the primary damage is the starting point in describing and predicting the irradiation-induced damage in materials. So far, primary damage has been described by traditional interatomic potentials in molecular dynamics simulations. Here, we employ a Gaussian approximation machine-learning potential (GAP) to study the primary damage in silicon with close to ab initio precision level. We report detailed analysis of cascade simulations derived from our modified Si GAP, which has already shown its reliability for simulating radiation damage in silicon. Major differences in the picture of primary damage predicted by machine-learning potential compared to classical potentials are atomic mixing, defect state at the heat spike phase, defect clustering, and re-crystallization rate. Atomic mixing is higher in the GAP description by a factor of two. GAP shows considerably higher number of coordination defects at the heat spike phase and the number of displaced atoms is noticeably greater in GAP. Surviving defects are dominantly isolated defects and small clusters, rather than large clusters, in GAP's prediction. The pattern by which the cascades are evolving is also different in GAP, having more expanded form compared to the locally compact form with classical potentials. Moreover, recovery of the generated defects at the heat spike phase take places with higher efficiency in GAP. We also provide the attributes of the new defect cluster that we had introduced in our previous study. A cluster of four defects, in which a central vacancy is surrounded by three split interstitials, where the surrounding atoms are all 4-folded bonded. The cluster shows higher occurrence in simulations with the GAP potential. The formation energy of the defect is 5.57 eV and it remains stable up to 700 K, at least for 30 ps. The Arrhenius equation predicts the lifetime of the cluster to be 0.0725 µs at room temperature.

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An automated approach for developing neural network interatomic potentials with FLAME

Cited by 9 publications

References 72 publications

Spectroscopic Signatures of Hydrogen-Bonding Motifs in Protonic Ionic Liquid Systems: Insights from Diethylammonium Nitrate in the Solid State

Spectroscopic Signatures of Hydrogen-Bonding Motifs in Protonic Ionic Liquid Systems: Insights from Diethylammonium Nitrate in the Solid State

Along the road to crystal structure prediction (CSP) of pharmaceutical-like molecules

Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential

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