{"id":"https://openalex.org/W7148603822","doi":"https://doi.org/10.1109/hipcw66559.2025.00033","title":"On-the-Fly Machine Learning Interatomic Potential for Bulk PdH: Assessing Accuracy and Efficiency","display_name":"On-the-Fly Machine Learning Interatomic Potential for Bulk PdH: Assessing Accuracy and Efficiency","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7148603822","doi":"https://doi.org/10.1109/hipcw66559.2025.00033"},"language":null,"primary_location":{"id":"doi:10.1109/hipcw66559.2025.00033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hipcw66559.2025.00033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 32nd International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015929877","display_name":"Poonam Parkar","orcid":null},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Poonam Parkar","raw_affiliation_strings":["Indian Institute of Technology,Chemical Engineering Department,Mumbai,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Chemical Engineering Department,Mumbai,India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035644160","display_name":"Sudarshan Vijay","orcid":"https://orcid.org/0000-0001-8242-0161"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sudarshan Vijay","raw_affiliation_strings":["Indian Institute of Technology,Chemical Engineering Department,Mumbai,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Chemical Engineering Department,Mumbai,India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132799348","display_name":"Abhijit Chatterjee","orcid":null},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abhijit Chatterjee","raw_affiliation_strings":["Indian Institute of Technology,Chemical Engineering Department,Mumbai,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology,Chemical Engineering Department,Mumbai,India","institution_ids":["https://openalex.org/I162827531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5015929877"],"corresponding_institution_ids":["https://openalex.org/I162827531"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85742021,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"137","last_page":"138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14347","display_name":"Big Data and Digital Economy","score":0.03350000083446503,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14347","display_name":"Big Data and Digital Economy","score":0.03350000083446503,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.03240000084042549,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.03229999914765358,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3156000077724457},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.29339998960494995},{"id":"https://openalex.org/keywords/computational-learning-theory","display_name":"Computational learning theory","score":0.2754000127315521},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27140000462532043},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2635999917984009}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5424000024795532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46369999647140503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4627000093460083},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2815999984741211},{"id":"https://openalex.org/C50292564","wikidata":"https://www.wikidata.org/wiki/Q2462783","display_name":"Computational learning theory","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2590999901294708},{"id":"https://openalex.org/C2776372370","wikidata":"https://www.wikidata.org/wiki/Q3399989","display_name":"Interatomic potential","level":3,"score":0.2549999952316284},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hipcw66559.2025.00033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hipcw66559.2025.00033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 32nd International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.5774413347244263,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1966046644","https://openalex.org/W1981368803","https://openalex.org/W2007395042","https://openalex.org/W2057858097","https://openalex.org/W2060079271","https://openalex.org/W2079105963","https://openalex.org/W2083222334","https://openalex.org/W2087698390","https://openalex.org/W2113001143","https://openalex.org/W2342368503","https://openalex.org/W3046503435","https://openalex.org/W3133931590","https://openalex.org/W4391626247","https://openalex.org/W4392611955","https://openalex.org/W4408167357","https://openalex.org/W4409533457"],"related_works":[],"abstract_inverted_index":{"The":[0,81,174],"accurate":[1,97,129],"description":[2],"of":[3,99,148,156,167,191],"palladium":[4],"hydrides":[5,193],"(PdHx,":[6],"0\u2a7dx\u2a7d1)":[7],"is":[8,28,83],"essential":[9],"for":[10,31,72,186],"understanding":[11],"their":[12],"role":[13],"in":[14,44,92,141,154],"hydrogen":[15,114],"storage,":[16],"purification":[17],"and":[18,35,48,58,103,117,134,145,164,172,182],"energy":[19],"conversion":[20],"technologies.":[21],"Conventional":[22],"ab":[23],"initio":[24],"molecular":[25,135],"dynamics":[26,136],"(AIMD)":[27],"prohibitively":[29],"expensive":[30],"the":[32,54,55,188],"large":[33],"supercells":[34],"long":[36],"time":[37,143],"scales":[38],"required":[39],"to":[40,85,132],"achieve":[41],"statistical":[42],"accuracy":[43],"capturing":[45],"elastic,":[46],"thermodynamic,":[47],"transport":[49],"properties":[50],"as":[51,53],"well":[52],"complex":[56],"thermodynamics":[57],"diffusion":[59],"processes.":[60],"In":[61],"this":[62],"work,":[63],"we":[64,123],"develop":[65],"a":[66,93,180],"machine":[67],"learning":[68,79],"force":[69],"field":[70],"(MLFF)":[71],"bulk":[73],"PdHx":[74],"using":[75],"an":[76,106],"on-the-fly":[77],"active":[78],"framework.":[80],"MLFF":[82,126,178],"fitted":[84],"data":[86],"from":[87],"density":[88],"functional":[89],"theory":[90],"(DFT)":[91],"way":[94],"that":[95,125],"guarantees":[96],"reproduction":[98],"interatomic":[100],"forces,":[101],"energies":[102],"stresses":[104],"over":[105],"extensive":[107],"configurational":[108],"range":[109],"which":[110],"includes":[111],"strained":[112],"lattices,":[113],"concentration":[115],"variations":[116],"finite-temperature":[118,157],"behaviour.":[119],"By":[120],"benchmarking":[121],"calculations":[122,155],"show":[124],"provides":[127,179],"almost":[128],"results":[130],"comparable":[131],"DFT,":[133],"simulations":[137],"can":[138],"be":[139],"performed":[140],"nanosecond":[142],"scale":[144],"with":[146],"thousands":[147],"atoms.":[149],"We":[150],"demonstrate":[151],"its":[152],"performance":[153],"structural":[158],"stability,":[159],"volume":[160],"changes,":[161],"vibrational":[162],"properties,":[163],"elastic":[165],"constants":[166],"PdH,":[168],"emphasizing":[169],"both":[170],"efficiency":[171],"reliability.":[173],"fully":[175],"developed":[176],"PdH":[177],"transferable":[181],"computationally":[183],"efficient":[184],"tool":[185],"exploring":[187],"thermomechanical":[189],"behavior":[190],"metal":[192],"systems.":[194]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-04-03T00:00:00"}