A deep learning interatomic potential developed for atomistic simulation of carbon materials

Wang, Jinjin; Shen, Hong; Yang, Riyi; Xie, Kun; Zhang, Chao; Chen, Liang‐Yao; Ho, Kai‐Ming; Wang, Cai‐Zhuang; Wang, Song-You

doi:10.1016/j.carbon.2021.09.062

Cited by 43 publications

(27 citation statements)

References 49 publications

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“…In the past three years, DPs have been applied in a number of systems in materials science including (1) elemental bulk systems, (2) multi-element bulk systems, (3) aqueous systems, (4) molecular systems and clusters, and ( 5) surfaces and lowdimensional systems. Table 2 shows a list of the material systems to which DPs have been applied (as of the writing of [187] Ice [188,189] Molecular systems and clusters Organic molecules [99,[190][191][192][193][194][195] Metal and alloy clusters [119,196] Surfaces and low-dimensional systems Metal and alloy surfaces [103,119,129] Graphane [125,197] Monolayer In 2 Se 3 [198] 2D Co-Fe-B [199] this paper). We choose several examples from each category to briefly discuss the corresponding DP application and how DP aids materials science research.…”

Section: Dp Applications In Materials Sciencementioning

confidence: 99%

“…In addition, the local structure of liquid Ga and the nucleation into α-Ga, β-Ga were also studied [123]. For carbon, a DP was developed by Wang et al [125] to simulate the structural properties of 12 different bulk and low-dimensional carbon structures. For Si, which has both covalent and metallic bonding behaviour, the first DP was trained based on datasets generated by classical metadynamics simulations [126].…”

Section: Elemental Bulk Systemsmentioning

confidence: 99%

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Deep potentials for materials science

Zhang²,

et al. 2022

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To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

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Section: Dp Applications In Materials Sciencementioning

confidence: 99%

Section: Elemental Bulk Systemsmentioning

confidence: 99%

Deep potentials for materials science

Zhang²,

et al. 2022

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“…See [103] for details. [176] (Hf0.2Zr0.2Ta0.2Nb0.2Ti0.2)X (X=C or B2) [177,178] Aqueous Systems water [96,[179][180][181][182][183][184][185][186][187][188][189][190]] zinc ion in water [191] water-vapor interface [192,193] water-TiO2 interface [194] ice [195,196] Molecular Systems and Clusters organic molecules [101,[197][198][199][200][201][202]] metal and alloy clusters [126,203] Surfaces and Low-dimensional Systems metal and alloy surfaces [105,126,136] graphane [132,204] monolayer In2Se3…”

Section: A Elemental Bulk Systemsmentioning

confidence: 99%

“…al. [132] to simulate the structural properties of 12 different bulk and lowdimensional carbon structures. For Si, which has both covalent and metallic bonding behavior, the first DP was trained based on datasets generated by classical metadynamics simulations [133].…”

Section: Initialisationmentioning

confidence: 99%

Deep Potentials for Materials Science

Wen¹,

Zhang²,

Wang³

et al. 2022

Preprint

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“…Recent NNPs aim at improved transferability by employing comprehensive reference datasets. These models in-clude PANNA [42], which was built using an iterative self-consistent workflow, and DeepMD [43], which was trained on a large dataset of bulk and low dimensional phases as well as snapshots from ab initio molecular dynamics (AIMD). Parallel to the NNPs, several GAP parametrizations for carbon were also developed.…”

Section: Introductionmentioning

confidence: 99%

Atomic cluster expansion for quantum-accurate large-scale simulations of carbon

Qamar¹,

Mrovec²,

Lysogorskiy³

et al. 2022

Preprint

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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 parameterized from an exhaustive set of important carbon structures at extended volume and energy range, computed using density functional theory (DFT). Rigorous validation reveals that ACE predicts accurately 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, evolution of amorphous carbon structures at different densities and quench rates and nucleation and growth of fullerene clusters under high pressure and temperature conditions.

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A deep learning interatomic potential developed for atomistic simulation of carbon materials

Cited by 43 publications

References 49 publications

Deep potentials for materials science

Deep potentials for materials science

Deep Potentials for Materials Science

Atomic cluster expansion for quantum-accurate large-scale simulations of carbon

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