{"id":"https://openalex.org/W7135201610","doi":"https://doi.org/10.26434/chemrxiv.15000732/v3","title":"Geometric Encoding of Cyclic Mechanical Fatigue in Model Glasses: Graph Neural Networks as Structural Probes for Battery-Relevant Degradation","display_name":"Geometric Encoding of Cyclic Mechanical Fatigue in Model Glasses: Graph Neural Networks as Structural Probes for Battery-Relevant Degradation","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7135201610","doi":"https://doi.org/10.26434/chemrxiv.15000732/v3"},"language":"en","primary_location":{"id":"doi:10.26434/chemrxiv.15000732/v3","doi":"https://doi.org/10.26434/chemrxiv.15000732/v3","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15000732/v3","pdf_url":null,"source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.26434/chemrxiv.15000732/v3","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109704071","display_name":"Saurab Singh","orcid":"https://orcid.org/0009-0005-5046-7383","observed_orcids":["https://orcid.org/0009-0005-5046-7383"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Saurab Singh","raw_affiliation_strings":["Independent Researcher"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Researcher","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5109704071"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9595000147819519,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9595000147819519,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10544","display_name":"Glass properties and applications","score":0.011099999770522118,"subfield":{"id":"https://openalex.org/subfields/2503","display_name":"Ceramics and Composites"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10281","display_name":"Advanced Battery Materials and Technologies","score":0.004699999932199717,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/glass-electrolytes","display_name":"glass electrolytes","score":0.7879999876022339},{"id":"https://openalex.org/keywords/solid-state-batteries","display_name":"solid-state batteries","score":0.9409999847412109},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.9449999928474426},{"id":"https://openalex.org/keywords/structural-deterioration","display_name":"structural deterioration","score":0.5080000162124634},{"id":"https://openalex.org/keywords/bond-stretching","display_name":"bond stretching","score":0.3880000114440918},{"id":"https://openalex.org/keywords/feature-ablation","display_name":"feature ablation","score":0.5009999871253967},{"id":"https://openalex.org/keywords/permutation-feature-importance","display_name":"permutation importance","score":0.5709999799728394},{"id":"https://openalex.org/keywords/battery-degradation-monitoring","display_name":"battery degradation monitoring","score":0.3840000033378601},{"id":"https://openalex.org/keywords/charge-discharge-cycles","display_name":"charge-discharge cycling","score":0.49300000071525574},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.4740000069141388}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5936999917030334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5849999785423279},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4961000084877014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4943999946117401},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45669999718666077},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43779999017715454},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4171999990940094},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38499999046325684},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.27090001106262207},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.26434/chemrxiv.15000732/v3","doi":"https://doi.org/10.26434/chemrxiv.15000732/v3","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15000732/v3","pdf_url":null,"source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.26434/chemrxiv.15000732/v3","doi":"https://doi.org/10.26434/chemrxiv.15000732/v3","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15000732/v3","pdf_url":null,"source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.5690000057220459,"id":"https://metadata.un.org/sdg/7"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7171720797","https://openalex.org/W7166643270","https://openalex.org/W7125186877","https://openalex.org/W4412234007","https://openalex.org/W7099394608","https://openalex.org/W2560456234","https://openalex.org/W7204873304","https://openalex.org/W2927535353","https://openalex.org/W6903874751","https://openalex.org/W7171560999"],"abstract_inverted_index":{"Solid-state":[0],"battery":[1],"glass":[2],"electrolytes":[3],"degrade":[4],"under":[5,94,100,171],"repeated":[6],"charge":[7],"and":[8,53,66,140,202,301,342,379,426,442,467,471],"discharge":[9],"cycling,":[10],"yet":[11],"the":[12,109,114,156,213,232,251,265,278,289,304,309,329,333,343,394,450,456],"atomic-scale":[13],"structural":[14,158,270,296,404,477],"changes":[15],"underlying":[16],"this":[17],"fatigue":[18,262,351,481],"remain":[19],"poorly":[20],"characterised.":[21],"Here":[22],"we":[23,58],"investigate":[24],"whether":[25],"cyclic":[26],"mechanical":[27,464],"fatigue\u2014simulated":[28],"as":[29,81,293,312,353],"oscillatory":[30],"volumetric":[31],"strain":[32],"applied":[33],"to":[34,85,144,208,321,429,449,463,479],"a":[35,39,50,54,178,228,294,359,469],"Lennard-Jones":[36],"model":[37],"glass\u2014encodes":[38],"learnable":[40],"geometric":[41,372],"signature":[42],"detectable":[43],"by":[44,136],"graph":[45],"neural":[46],"networks":[47],"(GNNs).":[48],"Using":[49],"GATv2":[51,266],"architecture":[52],"systematic":[55],"ablation":[56],"study,":[57],"find":[59],"that":[60,108,151,190,212,264,328,350,393],"classification":[61,134,193,234,418],"between":[62,338],"pristine":[63],"(cycle":[64],"0)":[65],"heavily":[67],"fatigued":[68],"(cycles":[69],"300\u2013400)":[70],"glasses":[71],"reaches":[72,419],"91.67":[73],"\u00b1":[74,87,92,169,196,421,433],"4.22%":[75],"accuracy":[76,135,194],"using":[77],"8-dimensional":[78],"bond-length":[79,153,298],"statistics":[80],"node":[82],"features,":[83],"rising":[84],"98.33":[86],"1.49%":[88],"(AUC":[89,423],"=":[90,148,167,238,248,257,411,424,431],"0.9975":[91],"0.0022)":[93],"per-sample":[95],"instance":[96,172],"normalisation.":[97,173],"The":[98],"improvement":[99],"normalisation\u2014which":[101],"explicitly":[102],"removes":[103],"all":[104,365,414],"global":[105],"magnitude":[106],"information\u2014establishes":[107],"discriminative":[110],"signal":[111],"resides":[112],"in":[113,122,261,273,380,483],"relational":[115],"geometry":[116,275],"of":[117,160,183,382,445],"local":[118,243],"bond":[119],"networks,":[120],"not":[121],"trivial":[123],"density":[124],"or":[125],"volume":[126],"changes.":[127],"Ablating":[128],"distribution-shape":[129],"features":[130,185,215,331],"(skewness,":[131],"quartiles)":[132],"reduces":[133],"5.3":[137],"percentage":[138],"points":[139],"collapses":[141],"cycle-number":[142],"regression":[143,204,427],"near-random":[145],"performance":[146],"(R\u00b2":[147],"0.006),":[149],"confirming":[150,327],"asymmetric":[152],"damage":[154,378],"is":[155],"dominant":[157,233],"fingerprint":[159],"fatigue.":[161],"Continuous":[162],"degradation":[163],"tracking":[164],"achieves":[165],"R\u00b2":[166,205,430],"0.311":[168],"0.039":[170],"Permutation":[174],"importance":[175],"analysis":[176,348],"identifies":[177],"minimal":[179],"sufficient":[180],"descriptor":[181],"set":[182],"four":[184],"{r_max,":[186],"r\u0304,":[187],"\u03c3_r,":[188],"\u03b3\u0303}":[189],"exactly":[191],"preserves":[192],"(91.67":[195],"2.36%)":[197],"while":[198,250,316],"halving":[199],"feature":[200,235,291],"dimensionality":[201],"improving":[203],"from":[206],"0.062":[207],"0.264":[209],"without":[210],"normalisation\u2014demonstrating":[211],"dropped":[214],"were":[216],"actively":[217],"degrading":[218],"convergence":[219,441],"rather":[220,276,399],"than":[221,277,400],"contributing":[222],"signal.":[223],"Per-atom":[224],"energy":[225,245,253,314],"validation":[226],"reveals":[227,349],"striking":[229],"hierarchy":[230],"inversion:":[231],"(r_max,":[236],"\u0394AUC":[237],"0.601)":[239],"correlates":[240],"negligibly":[241],"with":[242,308,358,435],"potential":[244],"(Spearman":[246],"\u03c1":[247,256],"\u22120.093),":[249],"strongest":[252],"proxy":[254],"(r_min,":[255],"\u22120.812)":[258],"ranks":[259],"seventh":[260],"discrimination\u2014establishing":[263],"probe":[267],"detects":[268],"irreversible":[269],"history":[271,462],"encoded":[272],"stretched-bond":[274],"instantaneous":[279],"energetic":[280],"state.":[281],"A":[282],"Kovacs":[283,306],"memory":[284],"effect":[285],"experiment":[286],"further":[287],"validates":[288,413],"8D":[290],"vector":[292],"genuine":[295],"probe:":[297],"standard":[299],"deviation":[300],"skewness":[302],"track":[303],"NVT":[305],"relaxation":[307],"same":[310],"timescale":[311],"macroscopic":[313],"recovery,":[315],"mean":[317],"non-affine":[318],"displacement":[319],"continues":[320],"grow":[322],"linearly":[323],"after":[324],"bulk":[325,340],"equilibration,":[326],"GNN":[330],"occupy":[332],"physically":[334],"privileged":[335],"observational":[336],"space":[337],"thermodynamic":[339],"observables":[341],"full":[344],"microscopic":[345],"configuration.":[346],"Attention-weight":[347],"manifests":[352],"isolated,":[354],"point-like":[355],"single-atom":[356],"defects":[357],"flat":[360],"spatial":[361],"correlation":[362],"length":[363],"across":[364],"400":[366],"cycles":[367],"(\u03be":[368],"\u2248":[369,390],"0.05\u03c3),":[370],"providing":[371],"evidence":[373],"against":[374],"Shear":[375],"Transformation":[376],"Zone-mediated":[377],"favour":[381],"diffuse":[383],"plastic":[384],"rearrangement;":[385],"Jaccard":[386],"hotspot":[387],"persistence":[388],"(J":[389],"0.08\u20130.12)":[391],"confirms":[392],"damaged-atom":[395],"population":[396],"rotates":[397],"continuously":[398],"accumulating":[401],"at":[402,409],"static":[403],"weak":[405],"points.":[406],"Finite-size":[407],"scaling":[408],"N":[410],"1024":[412],"qualitative":[415],"findings:":[416],"instance-normalised":[417],"100.00":[420],"0.00%":[422],"1.000)":[425],"improves":[428],"0.428":[432],"0.041,":[434],"normalisation":[436],"delivering":[437],"approximately":[438],"6\u00d7":[439],"faster":[440],"complete":[443],"elimination":[444],"fold":[446],"instabilities":[447],"relative":[448],"unnormalised":[451],"baseline.":[452],"These":[453],"findings":[454],"extend":[455],"geometric-encoding":[457],"framework":[458],"established":[459],"for":[460,474],"thermal":[461],"cycling":[465],"history,":[466],"provide":[468],"methodological":[470],"physical":[472],"foundation":[473],"applying":[475],"GNN-based":[476],"probes":[478],"non-destructive":[480],"diagnostics":[482],"experimentally":[484],"characterised":[485],"solid":[486],"electrolyte":[487],"systems.":[488]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-03-10T00:00:00"}
