Evidence map›Paper›PMID 24907992›Full record

ArticleThe Journal of chemical physics2014

Order-parameter-aided temperature-accelerated sampling for the exploration of crystal polymorphism and solid-liquid phase transitions.

Tang-Qing Yu, Pei-Yang Chen, Ming Chen, Amit Samanta, Eric Vanden-Eijnden, Mark Tuckerman

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Article in The Journal of chemical physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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0cells of the map it votes in
6citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Tang-Qing YuCourant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.
Pei-Yang ChenDepartment of Chemistry, New York University, New York, New York 10003, USA.
Ming ChenDepartment of Chemistry, New York University, New York, New York 10003, USA.
Amit SamantaProgram in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA and Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
Eric Vanden-EijndenCourant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.
Mark TuckermanCourant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.

Funding

Transition Path Theory and Markovian Milestoning for Prediction of Protein-Ligand Binding Kinetics in Molecular SimulationsR01GM100472 · NIGMS · DREXEL UNIVERSITY · PI ABRAMS, CAMERON F · 2011 to 2020
$2.5M
NIGMS NIH HHS R01 GM100472NIGMS NIH HHS R01GM100472
6 · The paper itself

Abstract

The problem of predicting polymorphism in atomic and molecular crystals constitutes a significant challenge both experimentally and theoretically. From the theoretical viewpoint, polymorphism prediction falls into the general class of problems characterized by an underlying rough energy landscape, and consequently, free energy based enhanced sampling approaches can be brought to bear on the problem. In this paper, we build on a scheme previously introduced by two of the authors in which the lengths and angles of the supercell are targeted for enhanced sampling via temperature accelerated adiabatic free energy dynamics [T. Q. Yu and M. E. Tuckerman, Phys. Rev. Lett. 107, 015701 (2011)]. Here, that framework is expanded to include general order parameters that distinguish different crystalline arrangements as target collective variables for enhanced sampling. The resulting free energy surface, being of quite high dimension, is nontrivial to reconstruct, and we discuss one particular strategy for performing the free energy analysis. The method is applied to the study of polymorphism in xenon crystals at high pressure and temperature using the Steinhardt order parameters without and with the supercell included in the set of collective variables. The expected fcc and bcc structures are obtained, and when the supercell parameters are included as collective variables, we also find several new structures, including fcc states with hcp stacking faults. We also apply the new method to the solid-liquid phase transition in copper at 1300 K using the same Steinhardt order parameters. Our method is able to melt and refreeze the system repeatedly, and the free energy profile can be obtained with high efficiency.

Indexed as

CrystallizationEnergy TransferThermodynamicsComputer SimulationPhase TransitionTemperature

Identifiers

PMID24907992
PMCPMC4048450

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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.