Evidence map›Paper›PMID 35668085›Full record

ArticleNature communications2022

Machine learning the metastable phase diagram of covalently bonded carbon.

Srilok Srinivasan, Rohit Batra, Duan Luo, Troy Loeffler, Sukriti Manna, Henry Chan, Liuxiang Yang, Wenge Yang, Jianguo Wen, Pierre Darancet and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Intelligent Computing for Future Layered Cathodes in Sodium-Ion Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Srilok SrinivasanCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA.ORCID http://orcid.org/0000-0003-2493-1291
Rohit BatraCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA.
Duan LuoCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA.ORCID http://orcid.org/0000-0001-5926-9870
Troy LoefflerCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA.
Sukriti MannaCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA.
Henry ChanCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA.ORCID http://orcid.org/0000-0002-8198-7737
Liuxiang YangCenter for High Pressure Science and Technology Advanced Research, 100193, Beijing, P. R. China.ORCID http://orcid.org/0000-0001-7737-3073
Wenge YangCenter for High Pressure Science and Technology Advanced Research, 100193, Beijing, P. R. China.ORCID http://orcid.org/0000-0001-8436-8731
Jianguo WenCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA. jwen@anl.gov.ORCID http://orcid.org/0000-0002-3755-0044
Pierre DarancetCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA. pdarancet@anl.gov.ORCID http://orcid.org/0000-0002-5846-1673
Subramanian K R S SankaranarayananCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA. skrssank@uic.edu.ORCID http://orcid.org/0000-0002-9708-396X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID35668085
PMCPMC9170764

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.