{"title":"Mirages in the Energy Landscape of Soft Sphere Packings","authors":"Praharsh Suryadevara, Mathias Casiulis, Stefano Martiniani","doi":"arxiv-2409.12113","DOIUrl":null,"url":null,"abstract":"The energy landscape is central to understanding low-temperature and athermal\nsystems, like jammed soft spheres. The geometry of this high-dimensional energy\nsurface is controlled by a plethora of minima and their associated basins of\nattraction that escape analytical treatment and are thus studied numerically.\nWe show that the ODE solver with the best time-for-error for this problem,\nCVODE, is orders of magnitude faster than other steepest-descent solvers for\nsuch systems. Using this algorithm, we provide unequivocal evidence that\noptimizers widely used in computational studies destroy all semblance of the\ntrue landscape geometry, even in moderate dimensions. Using various geometric\nindicators, both low- and high-dimensional, we show that results on the\nfractality of basins of attraction originated from the use of inadequate\nmapping strategies, as basins are actually smooth structures with well-defined\nlength scales. Thus, a vast number of past claims on energy landscapes need to\nbe re-evaluated due to the use of inadequate numerical methods.","PeriodicalId":501520,"journal":{"name":"arXiv - PHYS - Statistical Mechanics","volume":"54 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Statistical Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.12113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The energy landscape is central to understanding low-temperature and athermal
systems, like jammed soft spheres. The geometry of this high-dimensional energy
surface is controlled by a plethora of minima and their associated basins of
attraction that escape analytical treatment and are thus studied numerically.
We show that the ODE solver with the best time-for-error for this problem,
CVODE, is orders of magnitude faster than other steepest-descent solvers for
such systems. Using this algorithm, we provide unequivocal evidence that
optimizers widely used in computational studies destroy all semblance of the
true landscape geometry, even in moderate dimensions. Using various geometric
indicators, both low- and high-dimensional, we show that results on the
fractality of basins of attraction originated from the use of inadequate
mapping strategies, as basins are actually smooth structures with well-defined
length scales. Thus, a vast number of past claims on energy landscapes need to
be re-evaluated due to the use of inadequate numerical methods.