Machine learning methods are being explored in many areas of science, with
the aim of finding solution to problems that evade traditional scientific
approaches due to their complexity. In general, an order parameter capable of
identifying two different phases of matter separated by a correspond- ing phase
transition is constructed based on symmetry arguments. This parameter measures
the degree of order as the phase transition proceeds. However, when the two
distinct phases are highly disordered it is not trivial to identify broken
symmetries with which to find an order parameter. This poses an excellent
problem to be addressed using machine learning procedures. Room tem- perature
liquid water is hypothesized to be a supercritical liquid, with fluctuations of
two different molecular orders associated to two parent liquid phases, one with
high density and another one with low density. The validity of this hypothesis
is linked to the existence of an order parameter capable of identifying the two
distinct liquid phases and their fluctuations. In this work we show how two
different machine learning procedures are capable of recognizing local order in
liquid water. We argue that when in order to learn relevant features from this
complexity, an initial, physically motivated preparation of the available data
is as important as the quality of the data set, and that machine learning can
become a successful analysis tool only when coupled to high level physical
information.
%0 Generic
%1 soto2017unveiling
%A Soto, Adrián
%A Lu, Deyu
%A Yoo, Shinjae
%A Fernández-Serra, Mariví
%D 2017
%K liquid machine_learning order_parameter water
%T Unveiling two types of local order in liquid water using machine
learning
%U http://arxiv.org/abs/1707.04593
%X Machine learning methods are being explored in many areas of science, with
the aim of finding solution to problems that evade traditional scientific
approaches due to their complexity. In general, an order parameter capable of
identifying two different phases of matter separated by a correspond- ing phase
transition is constructed based on symmetry arguments. This parameter measures
the degree of order as the phase transition proceeds. However, when the two
distinct phases are highly disordered it is not trivial to identify broken
symmetries with which to find an order parameter. This poses an excellent
problem to be addressed using machine learning procedures. Room tem- perature
liquid water is hypothesized to be a supercritical liquid, with fluctuations of
two different molecular orders associated to two parent liquid phases, one with
high density and another one with low density. The validity of this hypothesis
is linked to the existence of an order parameter capable of identifying the two
distinct liquid phases and their fluctuations. In this work we show how two
different machine learning procedures are capable of recognizing local order in
liquid water. We argue that when in order to learn relevant features from this
complexity, an initial, physically motivated preparation of the available data
is as important as the quality of the data set, and that machine learning can
become a successful analysis tool only when coupled to high level physical
information.
@misc{soto2017unveiling,
abstract = {Machine learning methods are being explored in many areas of science, with
the aim of finding solution to problems that evade traditional scientific
approaches due to their complexity. In general, an order parameter capable of
identifying two different phases of matter separated by a correspond- ing phase
transition is constructed based on symmetry arguments. This parameter measures
the degree of order as the phase transition proceeds. However, when the two
distinct phases are highly disordered it is not trivial to identify broken
symmetries with which to find an order parameter. This poses an excellent
problem to be addressed using machine learning procedures. Room tem- perature
liquid water is hypothesized to be a supercritical liquid, with fluctuations of
two different molecular orders associated to two parent liquid phases, one with
high density and another one with low density. The validity of this hypothesis
is linked to the existence of an order parameter capable of identifying the two
distinct liquid phases and their fluctuations. In this work we show how two
different machine learning procedures are capable of recognizing local order in
liquid water. We argue that when in order to learn relevant features from this
complexity, an initial, physically motivated preparation of the available data
is as important as the quality of the data set, and that machine learning can
become a successful analysis tool only when coupled to high level physical
information.},
added-at = {2017-07-18T09:59:22.000+0200},
author = {Soto, Adrián and Lu, Deyu and Yoo, Shinjae and Fernández-Serra, Mariví},
biburl = {https://www.bibsonomy.org/bibtex/2caa2ba8e260f1871a23dc7c41476b66f/suqbar},
description = {1707.04593.pdf},
interhash = {187dc1fbddf8591b2aaee01b7197c299},
intrahash = {caa2ba8e260f1871a23dc7c41476b66f},
keywords = {liquid machine_learning order_parameter water},
note = {cite arxiv:1707.04593Comment: 14 pages, 12 figures, 2 appendices},
timestamp = {2017-07-18T09:59:22.000+0200},
title = {Unveiling two types of local order in liquid water using machine
learning},
url = {http://arxiv.org/abs/1707.04593},
year = 2017
}