Literature DB >> 35668085

Machine learning the metastable phase diagram of covalently bonded carbon.

Srilok Srinivasan1, Rohit Batra1, Duan Luo1, Troy Loeffler1, Sukriti Manna1,2, Henry Chan1,2, Liuxiang Yang3, Wenge Yang3, Jianguo Wen4, Pierre Darancet5,6, Subramanian K R S Sankaranarayanan7,8.   

Abstract

Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases to construct "metastable" phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). We incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n-diamond as a cubic-analog of diaphite-like lonsdaelite phase.
© 2022. The Author(s).

Entities:  

Year:  2022        PMID: 35668085      PMCID: PMC9170764          DOI: 10.1038/s41467-022-30820-8

Source DB:  PubMed          Journal:  Nat Commun        ISSN: 2041-1723            Impact factor:   17.694


  46 in total

1.  Modified phases of diamond formed under shock compression and rapid quenching.

Authors:  H Hirai; K Kondo
Journal:  Science       Date:  1991-08-16       Impact factor: 47.728

2.  Theoretical total-energy study of the transformation of graphite into hexagonal diamond.

Authors: 
Journal:  Phys Rev B Condens Matter       Date:  1987-05-15

3.  How evolutionary crystal structure prediction works--and why.

Authors:  Artem R Oganov; Andriy O Lyakhov; Mario Valle
Journal:  Acc Chem Res       Date:  2011-03-01       Impact factor: 22.384

4.  Topological Phases in Cove-Edged and Chevron Graphene Nanoribbons: Geometric Structures, [Formula: see text]2 Invariants, and Junction States.

Authors:  Yea-Lee Lee; Fangzhou Zhao; Ting Cao; Jisoon Ihm; Steven G Louie
Journal:  Nano Lett       Date:  2018-10-10       Impact factor: 11.189

5.  A generalized solid-state nudged elastic band method.

Authors:  Daniel Sheppard; Penghao Xiao; William Chemelewski; Duane D Johnson; Graeme Henkelman
Journal:  J Chem Phys       Date:  2012-02-21       Impact factor: 3.488

6.  K6 carbon: a metallic carbon allotrope in sp3 bonding networks.

Authors:  Chun-Yao Niu; Xin-Quan Wang; Jian-Tao Wang
Journal:  J Chem Phys       Date:  2014-02-07       Impact factor: 3.488

7.  Superhard F-carbon predicted by ab initio particle-swarm optimization methodology.

Authors:  Fei Tian; Xiao Dong; Zhisheng Zhao; Julong He; Hui-Tian Wang
Journal:  J Phys Condens Matter       Date:  2012-03-30       Impact factor: 2.333

8.  Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene.

Authors:  Aditya Koneru; Rohit Batra; Sukriti Manna; Troy D Loeffler; Henry Chan; Michael Sternberg; Anthony Avarca; Harpal Singh; Mathew J Cherukara; Subramanian K R S Sankaranarayanan
Journal:  J Phys Chem Lett       Date:  2022-02-17       Impact factor: 6.475

9.  Ab initio structure determination of n-diamond.

Authors:  Da Li; Fubo Tian; Binhua Chu; Defang Duan; Xiaojing Sha; Yunzhou Lv; Huadi Zhang; Nan Lu; Bingbing Liu; Tian Cui
Journal:  Sci Rep       Date:  2015-08-24       Impact factor: 4.379

10.  Mechanism for direct graphite-to-diamond phase transition.

Authors:  Hongxian Xie; Fuxing Yin; Tao Yu; Jian-Tao Wang; Chunyong Liang
Journal:  Sci Rep       Date:  2014-08-04       Impact factor: 4.379

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