{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T02:11:49Z","timestamp":1790820709336,"version":"4.1.0"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100018703","name":"HORIZON EUROPE European Innovation Council","doi-asserted-by":"crossref","award":["101046369"],"award-info":[{"award-number":["101046369"]}],"id":[{"id":"10.13039\/100018703","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["RTG2767"],"award-info":[{"award-number":["RTG2767"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002957","name":"Technische Universit\u00e4t Dresden","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002957","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Computational design of sensing materials remains fundamentally challenging due to the vast configurational landscape and the absence of robust property correlations that enable efficient molecular representation. These challenges become particularly critical in healthcare applications, where reliable and interpretable molecular recognition is essential for non-invasive diagnostics. To address these challenges, we developed MORE-ML, a quantum-informed AI framework that combines electronic-structure-derived properties of e-nose molecular building blocks with machine learning (ML) methods to uncover sensing mechanisms and guide the design of new systems. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between body odor volatilomes (BOV) molecules and mucin-derived receptors, both in the gas phase and when deposited on graphene. MORE-QX provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of the property space revealed weak correlations between quantum-mechanical (QM) properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Through explainable AI, we reduced the high-dimensional QM property space to a compact and physically interpretable set of descriptors, revealing the properties that most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for artificial sensing materials in BOV sensing. This approach establishes a foundation for advancing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications.<\/jats:p>","DOI":"10.1186\/s13321-026-01232-3","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T06:02:47Z","timestamp":1780034567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards the design of artificial sensing materials via quantum-informed explainable AI"],"prefix":"10.1186","volume":"18","author":[{"given":"Li","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Medrano Sandonas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Croy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gianaurelio","family":"Cuniberti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"1232_CR1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.inffus.2020.01.011","volume":"59","author":"J Zhang","year":"2020","unstructured":"Zhang J, Yin Z, Chen P, Nichele S (2020) Emotion recognition using multi-modal data and machine learning techniques: a tutorial and review. Inf Fusion 59:103\u2013126","journal-title":"Inf Fusion"},{"issue":"12","key":"1232_CR2","doi-asserted-by":"publisher","first-page":"18671","DOI":"10.1021\/acsnano.1c10313","volume":"15","author":"J Wang","year":"2021","unstructured":"Wang J, Wang C, Cai P, Luo Y, Cui Z, Loh XJ, Chen X (2021) Artificial sense technology: emulating and extending biological senses. ACS Nano 15(12):18671\u201318678","journal-title":"ACS Nano"},{"key":"1232_CR3","first-page":"1","volume":"99","author":"MM Ali","year":"2020","unstructured":"Ali MM, Hashim N, Abd Aziz S, Lasekan O (2020) Principles and recent advances in electronic nose for quality inspection of agricultural and food products. Trends Food Sci Technol 99:1\u201310","journal-title":"Trends Food Sci Technol"},{"key":"1232_CR4","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.carbon.2020.11.001","volume":"173","author":"S Huang","year":"2021","unstructured":"Huang S, Panes-Ruiz LA, Croy A, L\u00f6ffler M, Khavrus V, Bezugly V, Cuniberti G (2021) Highly sensitive room temperature ammonia gas sensor using pristine graphene: the role of biocompatible stabilizer. Carbon 173:262\u2013270","journal-title":"Carbon"},{"issue":"4","key":"1232_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202200016","volume":"4","author":"S Huang","year":"2022","unstructured":"Huang S, Croy A, Panes-Ruiz LA, Khavrus V, Bezugly V, Ibarlucea B, Cuniberti G (2022) Machine learning-enabled smart gas sensing platform for identification of industrial gases. Adv Intell Syst 4(4):2200016","journal-title":"Adv Intell Syst"},{"key":"1232_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.trac.2024.117958","volume":"180","author":"Y Li","year":"2024","unstructured":"Li Y, Wang Z, Zhao T, Li H, Jiang J, Ye J (2024) Electronic nose for the detection and discrimination of volatile organic compounds: application, challenges, and perspectives. TrAC Trend Anal Chem 180:117958","journal-title":"TrAC Trend Anal Chem"},{"issue":"3","key":"1232_CR7","doi-asserted-by":"publisher","DOI":"10.1088\/1752-7163\/abf1d0","volume":"15","author":"N Drabi\u0144ska","year":"2021","unstructured":"Drabi\u0144ska N, Flynn C, Ratcliffe N, Belluomo I, Myridakis A, Gould O, Fois M, Smart A, Devine T, Costello BDL (2021) A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome. J Breath Res 15(3):034001","journal-title":"J Breath Res"},{"issue":"4","key":"1232_CR8","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1021\/acscentsci.8b00879","volume":"5","author":"DK Trivedi","year":"2019","unstructured":"Trivedi DK, Sinclair E, Xu Y, Sarkar D, Walton-Doyle C, Liscio C, Banks P, Milne J, Silverdale M, Kunath T et al (2019) Discovery of volatile biomarkers of Parkinson\u2019s disease from sebum. ACS Cent Sci 5(4):599\u2013606","journal-title":"ACS Cent Sci"},{"issue":"1","key":"1232_CR9","doi-asserted-by":"publisher","first-page":"43","DOI":"10.2217\/nnm.12.105","volume":"8","author":"U Tisch","year":"2013","unstructured":"Tisch U, Schlesinger I, Ionescu R, Nassar M, Axelrod N, Robertman D, Tessler Y, Azar F, Marmur A, Aharon-Peretz J et al (2013) Detection of Alzheimer\u2019s and Parkinson\u2019s disease from exhaled breath using nanomaterial-based sensors. Nanomed 8(1):43\u201356","journal-title":"Nanomed"},{"issue":"10","key":"1232_CR10","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1002\/adhm.201500128","volume":"4","author":"D Karnaushenko","year":"2015","unstructured":"Karnaushenko D, Ibarlucea B, Lee S, Lin G, Baraban L, Pregl S, Melzer M, Makarov D, Weber WM, Mikolajick T et al (2015) Light weight and flexible high-performance diagnostic platform. Adv Healthc Mater 4(10):1517\u20131525","journal-title":"Adv Healthc Mater"},{"issue":"6852","key":"1232_CR11","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1038\/35093026","volume":"413","author":"S Firestein","year":"2001","unstructured":"Firestein S (2001) How the olfactory system makes sense of scents. Nature 413(6852):211\u2013218","journal-title":"Nature"},{"issue":"2","key":"1232_CR12","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/S1474-4422(15)00396-8","volume":"15","author":"J Morgan","year":"2016","unstructured":"Morgan J (2016) Joy of super smeller: sebum clues for PD diagnostics. Lancet Neurol 15(2):138\u2013139","journal-title":"Lancet Neurol"},{"issue":"38","key":"1232_CR13","doi-asserted-by":"publisher","DOI":"10.1002\/chem.202300897","volume":"29","author":"Y Bakhatan","year":"2023","unstructured":"Bakhatan Y, Alshanski I, Chan C-K, Lo W-C, Lu P-W, Liao P-H, Wang C-C, Hurevich M (2023) Accelerated solid phase glycan synthesis: ASGS. Chem Eur J 29(38):202300897","journal-title":"Chem Eur J"},{"issue":"13","key":"1232_CR14","doi-asserted-by":"publisher","first-page":"9313","DOI":"10.1021\/acs.joc.3c00878","volume":"88","author":"Y Sukhran","year":"2023","unstructured":"Sukhran Y, Alshanski I, Filiba O, Mackintosh MJ, Schapiro I, Hurevich M (2023) Unexpected nucleophile masking in acyl transfer to sterically crowded and conformationally restricted galactosides. J Org Chem 88(13):9313\u20139320","journal-title":"J Org Chem"},{"issue":"2","key":"1232_CR15","doi-asserted-by":"publisher","DOI":"10.1063\/5.0132177","volume":"10","author":"S Huang","year":"2023","unstructured":"Huang S, Croy A, Bierling AL, Khavrus V, Panes-Ruiz LA, Dianat A, Ibarlucea B, Cuniberti G (2023) Machine learning-enabled graphene-based electronic olfaction sensors and their olfactory performance assessment. Appl Phys Rev 10(2):021406","journal-title":"Appl Phys Rev"},{"key":"1232_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/chem.202502553","volume":"31","author":"A Shitrit","year":"2025","unstructured":"Shitrit A, Sukhran Y, Tverdokhleb N, Chen L, Dianat A, Gutierrez R, K\u00f6rbel S, Croy A, Cuniberti G, Hurevich M et al (2025) Monosaccharide-derived enantioselectivity in SWCNT chemoresistive VOC sensing. Chem Eur J 31:e02553","journal-title":"Chem Eur J"},{"key":"1232_CR17","doi-asserted-by":"crossref","unstructured":"Cuniberti G, Fagas G, Richter K (2005) Introducing molecular electronics: a brief overview. Introducing molecular electronics. pp 1\u201310","DOI":"10.1007\/3-540-31514-4_1"},{"issue":"28","key":"1232_CR18","doi-asserted-by":"publisher","first-page":"8776","DOI":"10.1039\/D1TC01857D","volume":"9","author":"VS Bhati","year":"2021","unstructured":"Bhati VS, Kumar M, Banerjee R (2021) Gas sensing performance of 2D nanomater\/metal oxide nanocomposites: a review. J Mater Chem C 9(28):8776\u20138808","journal-title":"J Mater Chem C"},{"key":"1232_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbi.2024.102870","volume":"87","author":"T Ginex","year":"2024","unstructured":"Ginex T, V\u00e1zquez J, Estarellas C, Luque FJ (2024) Quantum mechanical-based strategies in drug discovery: finding the pace to new challenges in drug design. Curr Opin Struct Biol 87:102870","journal-title":"Curr Opin Struct Biol"},{"key":"1232_CR20","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1039\/D4MD00869C","volume":"16","author":"PA Vargas-Rosales","year":"2025","unstructured":"Vargas-Rosales PA, Caflisch A (2025) The physics-AI dialogue in drug design. RSC Med Chem 16:1499\u20131515","journal-title":"RSC Med Chem"},{"key":"1232_CR21","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1039\/D5DD00411J","volume":"5","author":"A Hinostroza Caldas","year":"2026","unstructured":"Hinostroza Caldas A, Kokorin A, Tkatchenko A, Medrano Sandonas L (2026) Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction. Digit Discov 5:803\u2013818","journal-title":"Digit Discov"},{"key":"1232_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbi.2022.102417","volume":"75","author":"M Manathunga","year":"2022","unstructured":"Manathunga M, G\u00f6tz AW, Merz KM (2022) Computer-aided drug design, quantum-mechanical methods for biological problems. Curr Opin Struct Biol 75:102417","journal-title":"Curr Opin Struct Biol"},{"issue":"1","key":"1232_CR23","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1038\/s41597-025-04616-6","volume":"12","author":"L Chen","year":"2025","unstructured":"Chen L, Medrano Sandonas L, Traber P, Dianat A, Tverdokhleb N, Hurevich M, Yitzchaik S, Gutierrez R, Croy A, Cuniberti G (2025) MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics. Sci Data 12(1):324","journal-title":"Sci Data"},{"issue":"18","key":"1232_CR24","doi-asserted-by":"publisher","DOI":"10.1088\/0957-4484\/20\/18\/185504","volume":"20","author":"Y-H Zhang","year":"2009","unstructured":"Zhang Y-H, Chen Y-B, Zhou K-G, Liu C-H, Zeng J, Zhang H-L, Peng Y (2009) Improving gas sensing properties of graphene by introducing dopants and defects: a first-principles study. Nanotechnology 20(18):185504","journal-title":"Nanotechnology"},{"issue":"1","key":"1232_CR25","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1021\/acssensors.4c03254","volume":"10","author":"Z Wang","year":"2025","unstructured":"Wang Z, Hu X, Zhou Y (2025) Accelerated screening of highly sensitive gas sensor materials for greenhouse gases based on DFT and machine learning methods. ACS Sens 10(1):563\u2013572","journal-title":"ACS Sens"},{"issue":"1","key":"1232_CR26","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1021\/nl072364w","volume":"8","author":"T Wehling","year":"2008","unstructured":"Wehling T, Novoselov K, Morozov S, Vdovin E, Katsnelson M, Geim A, Lichtenstein A (2008) Molecular doping of graphene. Nano Lett 8(1):173\u2013177","journal-title":"Nano Lett"},{"issue":"2","key":"1232_CR27","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1039\/D0TC04840B","volume":"9","author":"M Mathew","year":"2021","unstructured":"Mathew M, Rout CS (2021) Schottky diodes based on 2D materials for environmental gas monitoring: a review on emerging trends, recent developments and future perspectives. J Mater Chem C 9(2):395\u2013416","journal-title":"J Mater Chem C"},{"issue":"9","key":"1232_CR28","doi-asserted-by":"publisher","first-page":"5520","DOI":"10.1021\/acs.chemrev.5b00630","volume":"116","author":"U Ryde","year":"2016","unstructured":"Ryde U, S\u00f6derhjelm P (2016) Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem Rev 116(9):5520\u20135566","journal-title":"Chem Rev"},{"issue":"1","key":"1232_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-63587-9","volume":"16","author":"M Puleva","year":"2025","unstructured":"Puleva M, Medrano Sandonas L, L\u0151rincz BD, Charry J, Rogers DM, Nagy PR, Tkatchenko A (2025) Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions. Nat Commun 16(1):8583","journal-title":"Nat Commun"},{"key":"1232_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.apsusc.2021.151687","volume":"575","author":"A Jeindl","year":"2022","unstructured":"Jeindl A, H\u00f6rmann L, Hofmann OT (2022) How much does surface polymorphism influence the work function of organic\/metal interfaces? AAppl Surf Sci 575:151687","journal-title":"AAppl Surf Sci"},{"key":"1232_CR31","unstructured":"Medrano Sandonas L, Balcells D, Bochkarev A, Cole JM, Deringer VL, Dobrautz W, Ehrenhofer A, Frank T, Friederich P, Friedrich R, George J, Ghiringhelli L, Caldas AH, Juraskova V, Kneiding H, Lysogorskiy Y, Margraf JT, T\u00fcrk H, Lilienfeld A, Todorovi\u0107 M, Tkatchenko A, Rossi M, Cuniberti G (2026) Perspective: Towards sustainable exploration of chemical spaces with machine learning. arXiv:2604.00069 arXiv preprint"},{"issue":"1","key":"1232_CR32","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41524-023-01121-5","volume":"9","author":"J Lan","year":"2023","unstructured":"Lan J, Palizhati A, Shuaibi M, Wood BM, Wander B, Das A, Uyttendaele M, Zitnick CL, Ulissi ZW (2023) AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials. npj Comput Mater 9(1):172","journal-title":"npj Comput Mater"},{"issue":"5","key":"1232_CR33","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1038\/s43588-023-00437-y","volume":"3","author":"S Pablo-Garc\u00eda","year":"2023","unstructured":"Pablo-Garc\u00eda S, Morandi S, Vargas-Hern\u00e1ndez RA, Jorner K, Ivkovi\u0107 \u017d, L\u00f3pez N, Aspuru-Guzik A (2023) Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks. Nat Comput Sci 3(5):433\u2013442","journal-title":"Nat Comput Sci"},{"issue":"1","key":"1232_CR34","doi-asserted-by":"publisher","first-page":"3232","DOI":"10.1038\/s41467-025-58499-7","volume":"16","author":"J Chen","year":"2025","unstructured":"Chen J, Huang X, Hua C, He Y, Schwaller P (2025) A multi-modal transformer for predicting global minimum adsorption energy. Nat Commun 16(1):3232","journal-title":"Nat Commun"},{"issue":"9","key":"1232_CR35","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1038\/s41929-018-0142-1","volume":"1","author":"K Tran","year":"2018","unstructured":"Tran K, Ulissi ZW (2018) Active learning across intermetallics to guide discovery of electrocatalysts for $$\\text{ CO}_2$$ reduction and $${\\text{ H}_2}$$ evolution. Nat Catal 1(9):696\u2013703","journal-title":"Nat Catal"},{"issue":"7807","key":"1232_CR36","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1038\/s41586-020-2242-8","volume":"581","author":"M Zhong","year":"2020","unstructured":"Zhong M, Tran K, Min Y, Wang C, Wang Z, Dinh C-T, De Luna P, Yu Z, Rasouli AS, Brodersen P et al (2020) Accelerated discovery of $${\\text{ CO}_2}$$ electrocatalysts using active machine learning. Nature 581(7807):178\u2013183","journal-title":"Nature"},{"issue":"1","key":"1232_CR37","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/s41467-020-20342-6","volume":"12","author":"V Fung","year":"2021","unstructured":"Fung V, Hu G, Ganesh P, Sumpter BG (2021) Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun 12(1):88","journal-title":"Nat Commun"},{"issue":"7","key":"1232_CR38","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1038\/s43588-022-00280-7","volume":"2","author":"W Xu","year":"2022","unstructured":"Xu W, Reuter K, Andersen M (2022) Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation. Nat Comput Sci 2(7):443\u2013450","journal-title":"Nat Comput Sci"},{"issue":"12","key":"1232_CR39","doi-asserted-by":"publisher","first-page":"2320232121","DOI":"10.1073\/pnas.2320232121","volume":"121","author":"Z Li","year":"2024","unstructured":"Li Z, Zhao C, Wang H, Ding Y, Chen Y, Schwaller P, Yang K, Hua C, He Y (2024) Interpreting chemisorption strength with automl-based feature deletion experiments. Proc Natl Acad Sci USA 121(12):2320232121","journal-title":"Proc Natl Acad Sci USA"},{"issue":"39","key":"1232_CR40","doi-asserted-by":"publisher","first-page":"10702","DOI":"10.1039\/D3SC03598K","volume":"14","author":"L Medrano Sandonas","year":"2023","unstructured":"Medrano Sandonas L, Hoja J, Ernst BG, V\u00e1zquez-Mayagoitia \u00c1, DiStasio RA, Tkatchenko A (2023) Freedom of design in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties. Chem Sci 14(39):10702\u201310717","journal-title":"Chem Sci"},{"key":"1232_CR41","unstructured":"Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 30"},{"issue":"1","key":"1232_CR42","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1038\/s41597-024-03521-8","volume":"11","author":"L Medrano Sandonas","year":"2024","unstructured":"Medrano Sandonas L, Van Rompaey D, Fallani A, Hilfiker M, Hahn D, Perez-Benito L, Verhoeven J, Tresadern G, Kurt Wegner J, Ceulemans H, Tkatchenko A (2024) Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules. Sci Data 11(1):742","journal-title":"Sci Data"},{"issue":"1","key":"1232_CR43","doi-asserted-by":"publisher","DOI":"10.1063\/5.0273102","volume":"163","author":"F Ravera","year":"2025","unstructured":"Ravera F, Medrano Sandonas L, Gutierrez R, Graziano M, Cuniberti G (2025) Are nonequilibrium effects relevant for chiral molecule discrimination? J Chem Phys 163(1):014702","journal-title":"J Chem Phys"},{"issue":"4","key":"1232_CR44","doi-asserted-by":"publisher","DOI":"10.1002\/anie.202214477","volume":"62","author":"C Plett","year":"2023","unstructured":"Plett C, Grimme S (2023) Automated and efficient generation of general molecular aggregate structures. Angew Chem Int Ed Engl 62(4):202214477","journal-title":"Angew Chem Int Ed Engl"},{"issue":"11","key":"1232_CR45","doi-asserted-by":"publisher","DOI":"10.1063\/5.0197592","volume":"160","author":"P Pracht","year":"2024","unstructured":"Pracht P, Grimme S, Bannwarth C, Bohle F, Ehlert S, Feldmann G, Gorges J, M\u00fcller M, Neudecker T, Plett C, Spicher S, Steinbach P, Weso\u0142owski PA, Zeller F (2024) Crest\u2013a program for the exploration of low-energy molecular chemical space. J Chem Phys 160(11):114110","journal-title":"J Chem Phys"},{"issue":"3","key":"1232_CR46","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1021\/acs.jctc.8b01176","volume":"15","author":"C Bannwarth","year":"2019","unstructured":"Bannwarth C, Ehlert S, Grimme S (2019) GFN2-xTB\u2013an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J Chem Theory Comput 15(3):1652\u20131671","journal-title":"J Chem Theory Comput"},{"issue":"1","key":"1232_CR47","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1002\/wcms.81","volume":"2","author":"F Neese","year":"2012","unstructured":"Neese F (2012) The ORCA program system. Wiley Interdiscip Rev Comput Mol Sci 2(1):73\u201378","journal-title":"Wiley Interdiscip Rev Comput Mol Sci"},{"key":"1232_CR48","doi-asserted-by":"publisher","first-page":"124101","DOI":"10.1063\/1.5143190","volume":"152","author":"B Hourahine","year":"2020","unstructured":"Hourahine B, Aradi B, Blum V, Bonafe F, Buccheri A, Camacho C, Cevallos C, Deshaye M, Dumitric\u0103 T, Dominguez A et al (2020) DFTB+, a software package for efficient approximate density functional theory based atomistic simulations. J Chem Phys. https:\/\/doi.org\/10.1063\/1.5143190","journal-title":"J Chem Phys"},{"issue":"1","key":"1232_CR49","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1038\/s41597-025-04720-7","volume":"12","author":"S Ganscha","year":"2025","unstructured":"Ganscha S, Unke OT, Ahlin D, Maennel H, Kashubin S, M\u00fcller K-R (2025) The QCML dataset, Quantum chemistry reference data from 33.5 M dft and 14.7 B semi-empirical calculations. Sci Data 12(1):406","journal-title":"Sci Data"},{"issue":"1","key":"1232_CR50","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1038\/s41597-022-01390-7","volume":"9","author":"C Isert","year":"2022","unstructured":"Isert C, Atz K, Jim\u00e9nez-Luna J, Schneider G (2022) QMugs, quantum mechanical properties of drug-like molecules. Sci Data 9(1):273","journal-title":"Sci Data"},{"issue":"16","key":"1232_CR51","doi-asserted-by":"publisher","first-page":"11169","DOI":"10.1103\/PhysRevB.54.11169","volume":"54","author":"G Kresse","year":"1996","unstructured":"Kresse G, Furthm\u00fcller J (1996) Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 54(16):11169","journal-title":"Phys Rev B"},{"issue":"1","key":"1232_CR52","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/0927-0256(96)00008-0","volume":"6","author":"G Kresse","year":"1996","unstructured":"Kresse G, Furthm\u00fcller J (1996) Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput Mater Sci 6(1):15\u201350","journal-title":"Comput Mater Sci"},{"issue":"22","key":"1232_CR53","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202210788","volume":"35","author":"J Schmidt","year":"2023","unstructured":"Schmidt J, Hoffmann N, Wang H-C, Borlido P, Carri\u00e7o PJ, Cerqueira TF, Botti S, Marques MA (2023) Machine-learning-assisted determination of the global zero-temperature phase diagram of materials. Adv Mater 35(22):2210788","journal-title":"Adv Mater"},{"key":"1232_CR54","unstructured":"Barroso-Luque L, Shuaibi M, Fu X, Wood BM, Dzamba M, Gao M, Rizvi A, Zitnick CL, Ulissi ZW (2024) Open materials 2024 (omat24) inorganic materials dataset and models.arXiv:2410.12771 arXiv preprint"},{"issue":"9","key":"1232_CR55","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1038\/s42256-023-00716-3","volume":"5","author":"B Deng","year":"2023","unstructured":"Deng B, Zhong P, Jun K, Riebesell J, Han K, Bartel CJ, Ceder G (2023) CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat Mach Intell 5(9):1031\u20131041","journal-title":"Nat Mach Intell"},{"issue":"23","key":"1232_CR56","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.94.235418","volume":"94","author":"S De Waele","year":"2016","unstructured":"De Waele S, Lejaeghere K, Sluydts M, Cottenier S (2016) Error estimates for density-functional theory predictions of surface energy and work function. Phys Rev B 94(23):235418","journal-title":"Phys Rev B"},{"issue":"3","key":"1232_CR57","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevApplied.19.037001","volume":"19","author":"L Lin","year":"2023","unstructured":"Lin L, Jacobs R, Ma T, Chen D, Booske J, Morgan D (2023) Work function: Fundamentals, measurement, calculation, engineering, and applications. Phys Rev Appl 19(3):037001","journal-title":"Phys Rev Appl"},{"issue":"51","key":"1232_CR58","doi-asserted-by":"publisher","first-page":"44873","DOI":"10.1021\/acsami.7b12218","volume":"9","author":"A Gankin","year":"2017","unstructured":"Gankin A, Sfez R, Mervinetsky E, Buchwald J, Dianat A, Medrano Sandonas L, Gutierrez R, Cuniberti G, Yitzchaik S (2017) Molecular and ionic dipole effects on the electronic properties of Si-\/SiO2-grafted alkylamine monolayers. ACS Appl Mater Interfaces 9(51):44873\u201344879","journal-title":"ACS Appl Mater Interfaces"},{"issue":"3","key":"1232_CR59","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.commatsci.2005.04.010","volume":"36","author":"G Henkelman","year":"2006","unstructured":"Henkelman G, Arnaldsson A, J\u00f3nsson H (2006) A fast and robust algorithm for bader decomposition of charge density. Comput Mater Sci 36(3):354\u2013360","journal-title":"Comput Mater Sci"},{"key":"1232_CR60","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access 6:52138\u201352160","journal-title":"IEEE access"},{"issue":"1","key":"1232_CR61","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S-I (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56\u201367","journal-title":"Nat Mach Intell"},{"issue":"6","key":"1232_CR62","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1021\/accountsmr.1c00244","volume":"3","author":"F Oviedo","year":"2022","unstructured":"Oviedo F, Ferres JL, Buonassisi T, Butler KT (2022) Interpretable and explainable machine learning for materials science and chemistry. Acc Mater Res 3(6):597\u2013607","journal-title":"Acc Mater Res"},{"key":"1232_CR63","doi-asserted-by":"crossref","unstructured":"Cooper A, Doyle O, Bourke A (2021) Supervised clustering for subgroup discovery: an application to COVID-19 symptomatology. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp 408\u2013422","DOI":"10.1007\/978-3-030-93733-1_29"},{"issue":"5","key":"1232_CR64","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1039\/D3TA06316J","volume":"12","author":"A Usuga","year":"2024","unstructured":"Usuga A, Praveen C, Comas-Vives A (2024) Local descriptors-based machine learning model refined by cluster analysis for accurately predicting adsorption energies on bimetallic alloys. J Mater Chem A 12(5):2708\u20132721","journal-title":"J Mater Chem A"},{"issue":"3","key":"1232_CR65","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1073\/pnas.1006652108","volume":"108","author":"JK N\u00f8rskov","year":"2011","unstructured":"N\u00f8rskov JK, Abild-Pedersen F, Studt F, Bligaard T (2011) Density functional theory in surface chemistry and catalysis. Proc Natl Acad Sci USA 108(3):937\u2013943","journal-title":"Proc Natl Acad Sci USA"},{"key":"1232_CR66","doi-asserted-by":"crossref","unstructured":"Zhou C, Yang W, Zhu H (2015) Mechanism of charge transfer and its impacts on fermi-level pinning for gas molecules adsorbed on monolayer WS$$_2$$. J Chem Phys 142(21):","DOI":"10.1063\/1.4922049"},{"issue":"22","key":"1232_CR67","doi-asserted-by":"publisher","DOI":"10.3390\/nano14221778","volume":"14","author":"L Chen","year":"2024","unstructured":"Chen L, Bodesheim D, Ranjbar A, Dianat A, Biele R, Gutierrez R, Khazaei M, Cuniberti G (2024) Computational design of the electronic response for volatile organic compounds interacting with doped graphene substrates. Nanomaterials 14(22):1778","journal-title":"Nanomaterials"},{"issue":"7","key":"1232_CR68","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.92.075411","volume":"92","author":"M Khazaei","year":"2015","unstructured":"Khazaei M, Arai M, Sasaki T, Ranjbar A, Liang Y, Yunoki S (2015) OH-terminated two-dimensional transition metal carbides and nitrides as ultralow work function materials. Phys Rev B 92(7):075411","journal-title":"Phys Rev B"},{"issue":"19","key":"1232_CR69","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.68.195408","volume":"68","author":"T-C Leung","year":"2003","unstructured":"Leung T-C, Kao C, Su W, Feng Y, Chan CT (2003) Relationship between surface dipole, work function and charge transfer: Some exceptions to an established rule. Phys Rev B 68(19):195408","journal-title":"Phys Rev B"},{"issue":"11","key":"1232_CR70","doi-asserted-by":"publisher","first-page":"4755","DOI":"10.1039\/D4SC08617A","volume":"16","author":"S Kim","year":"2025","unstructured":"Kim S, Schroeder CM, Jackson NE (2025) Functional monomer design for synthetically accessible polymers. Chem Sci 16(11):4755\u20134767","journal-title":"Chem Sci"},{"issue":"1","key":"1232_CR71","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"1232_CR72","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the $$22^{\\rm Nd}$$ Acm Sigkdd International Conference on Knowledge Discovery and Data mining, pp. 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"1232_CR73","unstructured":"Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) Catboost: unbiased boosting with categorical features. Adv Neural Inf Process Syst 31"},{"key":"1232_CR74","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: A highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30"},{"issue":"12","key":"1232_CR75","doi-asserted-by":"publisher","first-page":"2326","DOI":"10.1021\/acs.jpclett.5b00831","volume":"6","author":"K Hansen","year":"2015","unstructured":"Hansen K, Biegler F, Ramakrishnan R, Pronobis W, Von Lilienfeld OA, Muller K-R, Tkatchenko A (2015) Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space. J Phys Chem Lett 6(12):2326\u20132331","journal-title":"J Phys Chem Lett"},{"key":"1232_CR76","doi-asserted-by":"publisher","first-page":"13754","DOI":"10.1039\/C6CP00415F","volume":"18","author":"S De","year":"2016","unstructured":"De S, Bart\u00f3k AP, Cs\u00e1nyi G, Ceriotti M (2016) Comparing molecules and solids across structural and alchemical space. Phys Chem Chem Phys 18:13754\u201313769","journal-title":"Phys Chem Chem Phys"},{"issue":"21","key":"1232_CR77","doi-asserted-by":"publisher","first-page":"17598","DOI":"10.1021\/jacs.4c07099","volume":"147","author":"DP Kov\u00e1cs","year":"2025","unstructured":"Kov\u00e1cs DP, Moore JH, Browning NJ, Batatia I, Horton JT, Pu Y, Kapil V, Witt WC, Magd\u0103u I-B, Cole DJ, Cs\u00e1nyi G (2025) Mace-off: short-range transferable machine learning force fields for organic molecules. J Am Chem Soc 147(21):17598\u201317611","journal-title":"J Am Chem Soc"},{"issue":"20","key":"1232_CR78","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1021\/jz401721r","volume":"4","author":"DA Egger","year":"2013","unstructured":"Egger DA, Zojer E (2013) Anticorrelation between the evolution of molecular dipole moments and induced work function modifications. J Phys Chem Lett 4(20):3521\u20133526","journal-title":"J Phys Chem Lett"},{"issue":"5","key":"1232_CR79","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1021\/acs.jctc.5b00099","volume":"11","author":"R Ramakrishnan","year":"2015","unstructured":"Ramakrishnan R, Dral PO, Rupp M, Lilienfeld OA (2015) Big data meets quantum chemistry approximations: the $$\\delta$$-machine learning approach. J Chem Theory Comput 11(5):2087\u20132096","journal-title":"J Chem Theory Comput"},{"key":"1232_CR80","doi-asserted-by":"publisher","first-page":"10775","DOI":"10.1039\/D2CP00834C","volume":"24","author":"K Atz","year":"2022","unstructured":"Atz K, Isert C, B\u00f6cker MNA, Jim\u00e9nez-Luna J, Schneider G (2022) $$\\delta$$-quantum machine-learning for medicinal chemistry. Phys Chem Chem Phys 24:10775\u201310783","journal-title":"Phys Chem Chem Phys"},{"key":"1232_CR81","doi-asserted-by":"publisher","first-page":"3949","DOI":"10.1039\/D6CP00038J","volume":"28","author":"L Medrano Sandonas","year":"2026","unstructured":"Medrano Sandonas L, Puleva M, Erarslan Z, Parra Payano R, St\u00f6hr M, Cuniberti G, Tkatchenko A (2026) Advancing density functional tight-binding method for large organic molecules through equivariant neural networks. Phys Chem Chem Phys 28:3949\u20133962","journal-title":"Phys Chem Chem Phys"},{"key":"1232_CR82","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevMaterials.2.083802","volume":"2","author":"R Ouyang","year":"2018","unstructured":"Ouyang R, Curtarolo S, Ahmetcik E, Scheffler M, Ghiringhelli LM (2018) Sisso: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys Rev Mater 2:083802","journal-title":"Phys Rev Mater"},{"issue":"1","key":"1232_CR83","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/s41699-025-00586-w","volume":"9","author":"Z Cai","year":"2025","unstructured":"Cai Z, Kim H (2025) Recent advances in mxene gas sensors: synthesis, composites, and mechanisms. npj 2D Mater Appl 9(1):66","journal-title":"npj 2D Mater Appl"},{"key":"1232_CR84","doi-asserted-by":"publisher","DOI":"10.1002\/smtd.202402179","volume":"10","author":"S Yu","year":"2025","unstructured":"Yu S, Li P, Ding H, Liang C, Wang X (2025) 2D MXenes-based gas sensors: progress, applications, and challenges. Small Methods 10:2402179","journal-title":"Small Methods"},{"issue":"16","key":"1232_CR85","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1021\/acs.accounts.4c00323","volume":"57","author":"A Mirzaei","year":"2024","unstructured":"Mirzaei A, Kim J-Y, Kim HW, Kim SS (2024) Resistive gas sensors based on 2D TMDs and MXenes. Acc Chem Res 57(16):2395\u20132413","journal-title":"Acc Chem Res"},{"issue":"14","key":"1232_CR86","doi-asserted-by":"publisher","first-page":"16728","DOI":"10.1002\/adfm.202516728","volume":"36","author":"D Jana","year":"2026","unstructured":"Jana D, Mukherjee S, Litvinov D, Grzeszczyk M, Grebenchuk S, \u0160i\u0161kins M, Gavriliuc V, Ouyang Y, Chen C, Ye Y et al (2026) Two-dimensional materials as a multiproperty sensing platform. Adv Funct Mater 36(14):16728","journal-title":"Adv Funct Mater"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-026-01232-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-026-01232-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-026-01232-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:03:30Z","timestamp":1780927410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13321-026-01232-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,29]]},"references-count":86,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1232"],"URL":"https:\/\/doi.org\/10.1186\/s13321-026-01232-3","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,29]]},"assertion":[{"value":"17 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"75"}}