{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T12:36:06Z","timestamp":1768653366428,"version":"3.49.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["EXC 2075-390740016"],"award-info":[{"award-number":["EXC 2075-390740016"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Baden-Wuerttemberg","award":["Landesgraduiertenf\u00f6rderungsgesetz"],"award-info":[{"award-number":["Landesgraduiertenf\u00f6rderungsgesetz"]}]},{"DOI":"10.13039\/501100003130","name":"Fonds Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["1S58718N"],"award-info":[{"award-number":["1S58718N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009534","name":"Universit\u00e4t Stuttgart","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009534","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><jats:p>Particle-Based (PB) simulations, including Molecular Dynamics (MD), provide access to system observables that are not easily available experimentally. However, in most cases, PB data needs to be processed after a simulation to extract these observables. One of the main challenges in post-processing PB simulations is managing the large amounts of data typically generated without incurring memory or computational capacity limitations. In this work, we introduce the post-processing tool: MDSuite. This software, developed in Python, combines state-of-the-art computing technologies such as TensorFlow, with modern data management tools such as HDF5 and SQL for a fast, scalable, and accurate PB data processing engine. This package, built around the principles of FAIR data, provides a memory safe, parallelized, and GPU accelerated environment for the analysis of particle simulations. The software currently offers 17 calculators for the computation of properties including diffusion coefficients, thermal conductivity, viscosity, radial distribution functions, coordination numbers, and more. Further, the object-oriented framework allows for the rapid implementation of new calculators or file-readers for different simulation software. The Python front-end provides a familiar interface for many users in the scientific community and a mild learning curve for the inexperienced. Future developments will include the introduction of more analysis associated with ab-initio methods, colloidal\/macroscopic particle methods, and extension to experimental data.<\/jats:p>","DOI":"10.1186\/s13321-023-00687-y","type":"journal-article","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T05:03:14Z","timestamp":1676091794000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MDSuite: comprehensive post-processing tool for particle simulations"],"prefix":"10.1186","volume":"15","author":[{"given":"Samuel","family":"Tovey","sequence":"first","affiliation":[]},{"given":"Fabian","family":"Zills","sequence":"additional","affiliation":[]},{"given":"Francisco","family":"Torres-Herrador","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Lohrmann","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Br\u00fcckner","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Holm","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"687_CR1","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1039\/D0SM01554G","volume":"17","author":"P Kreissl","year":"2021","unstructured":"Kreissl P, Holm C, Weeber R (2021) Frequency-dependent magnetic susceptibility of magnetic nanoparticles in a polymer solution: a simulation study. Soft Matter 17:174\u2013183. https:\/\/doi.org\/10.1039\/D0SM01554G","journal-title":"Soft Matter"},{"key":"687_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/pr9010071","author":"OMH Salo-Ahen","year":"2021","unstructured":"Salo-Ahen OMH, Alanko I, Bhadane R, Bonvin AMJJ, Honorato RV, Hossain S, Juffer AH, Kabedev A, Lahtela-Kakkonen M, Larsen AS, Lescrinier E, Marimuthu P, Mirza MU, Mustafa G, Nunes-Alves A, Pantsar T, Saadabadi A, Singaravelu K, Vanmeert M (2021) Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes. https:\/\/doi.org\/10.3390\/pr9010071","journal-title":"Processes"},{"issue":"1","key":"687_CR3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/1741-7007-9-71","volume":"9","author":"JD Durrant","year":"2011","unstructured":"Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9(1):71. https:\/\/doi.org\/10.1186\/1741-7007-9-71","journal-title":"BMC Biol"},{"issue":"9","key":"687_CR4","doi-asserted-by":"publisher","first-page":"4035","DOI":"10.1021\/acs.jmedchem.5b01684","volume":"59","author":"M De Vivo","year":"2016","unstructured":"De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59(9):4035\u20134061. https:\/\/doi.org\/10.1021\/acs.jmedchem.5b01684","journal-title":"J Med Chem"},{"key":"687_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.ejmech.2014.08.004","volume":"91","author":"H Zhao","year":"2015","unstructured":"Zhao H, Caflisch A (2015) Molecular dynamics in drug design. Eur J Med Chem 91:4\u201314. https:\/\/doi.org\/10.1016\/j.ejmech.2014.08.004","journal-title":"Eur J Med Chem"},{"issue":"20","key":"687_CR6","doi-asserted-by":"publisher","DOI":"10.1063\/5.0069340","volume":"155","author":"J Zeman","year":"2021","unstructured":"Zeman J, Kondrat S, Holm C (2021) Ionic screening in bulk and under confinement. J Chem Phys 155(20):204501. https:\/\/doi.org\/10.1063\/5.0069340","journal-title":"J Chem Phys"},{"issue":"17","key":"687_CR7","doi-asserted-by":"publisher","first-page":"4278","DOI":"10.1021\/acs.jpclett.1c00901","volume":"12","author":"G Sivaraman","year":"2021","unstructured":"Sivaraman G, Guo J, Ward L, Hoyt N, Williamson M, Foster I, Benmore C, Jackson N (2021) Automated development of molten salt machine learning potentials: application to LICL. J Phys Chem Lett 12(17):4278\u20134285. https:\/\/doi.org\/10.1021\/acs.jpclett.1c00901. (PMID: 33908789)","journal-title":"J Phys Chem Lett"},{"issue":"3","key":"687_CR8","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1021\/acs.jctc.7b00903","volume":"14","author":"F Uhlig","year":"2018","unstructured":"Uhlig F, Zeman J, Smiatek J, Holm C (2018) First-principles parametrization of polarizable coarse-grained force fields for ionic liquids. J Chem Theory Comput 14(3):1471\u20131486. https:\/\/doi.org\/10.1021\/acs.jctc.7b00903. (PMID: 29357238)","journal-title":"J Chem Theory Comput"},{"issue":"4","key":"687_CR9","doi-asserted-by":"publisher","DOI":"10.1088\/2515-7655\/abb011","volume":"2","author":"VL Deringer","year":"2020","unstructured":"Deringer VL (2020) Modelling and understanding battery materials with machine-learning-driven atomistic simulations. J Phys Energy 2(4):041003. https:\/\/doi.org\/10.1088\/2515-7655\/abb011. (Publisher: IOP Publishing)","journal-title":"J Phys Energy"},{"issue":"7","key":"687_CR10","doi-asserted-by":"publisher","first-page":"4569","DOI":"10.1021\/acs.chemrev.8b00239","volume":"119","author":"AA Franco","year":"2019","unstructured":"Franco AA, Rucci A, Brandell D, Frayret C, Gaberscek M, Jankowski P, Johansson P (2019) Boosting rechargeable batteries R &D by multiscale modeling: myth or reality? Chem Rev 119(7):4569\u20134627. https:\/\/doi.org\/10.1021\/acs.chemrev.8b00239","journal-title":"Chem Rev"},{"issue":"41","key":"687_CR11","doi-asserted-by":"publisher","first-page":"2002373","DOI":"10.1002\/aenm.202002373","volume":"10","author":"Y Sun","year":"2020","unstructured":"Sun Y, Yang T, Ji H, Zhou J, Wang Z, Qian T, Yan C (2020) Boosting the optimization of lithium metal batteries by molecular dynamics simulations: a perspective. Adv Energy Mater 10(41):2002373. https:\/\/doi.org\/10.1002\/aenm.202002373","journal-title":"Adv Energy Mater"},{"issue":"1","key":"687_CR12","doi-asserted-by":"publisher","first-page":"10736","DOI":"10.1038\/s41598-018-28869-x","volume":"8","author":"A Muralidharan","year":"2018","unstructured":"Muralidharan A, Chaudhari MI, Pratt LR, Rempe SB (2018) Molecular dynamics of lithium ion transport in a model solid electrolyte interphase. Sci Rep 8(1):10736","journal-title":"Sci Rep"},{"issue":"10","key":"687_CR13","doi-asserted-by":"publisher","first-page":"9733","DOI":"10.1021\/acsnano.8b04785","volume":"12","author":"K Breitsprecher","year":"2018","unstructured":"Breitsprecher K, Holm C, Kondrat S (2018) Charge me slowly, I am in a hurry: optimizing charge-discharge cycles in nanoporous supercapacitors. ACS Nano 12(10):9733\u20139741. https:\/\/doi.org\/10.1021\/acsnano.8b04785","journal-title":"ACS Nano"},{"issue":"47","key":"687_CR14","doi-asserted-by":"publisher","first-page":"25760","DOI":"10.1021\/acs.jpcc.0c08870","volume":"124","author":"S Tovey","year":"2020","unstructured":"Tovey S, Narayanan Krishnamoorthy A, Sivaraman G, Guo J, Benmore C, Heuer A, Holm C (2020) DFT accurate interatomic potential for molten NaCl from machine learning. J Phys Chem C 124(47):25760\u201325768. https:\/\/doi.org\/10.1021\/acs.jpcc.0c08870","journal-title":"J Phys Chem C"},{"issue":"1","key":"687_CR15","doi-asserted-by":"publisher","first-page":"6085","DOI":"10.1038\/s41467-020-19903-6","volume":"11","author":"K Breitsprecher","year":"2020","unstructured":"Breitsprecher K, Janssen M, Srimuk P, Mehdi BL, Presser V, Holm C, Kondrat S (2020) How to speed up ion transport in nanopores. Nat Commun 11(1):6085. https:\/\/doi.org\/10.1038\/s41467-020-19903-6","journal-title":"Nat Commun"},{"issue":"2","key":"687_CR16","doi-asserted-by":"publisher","first-page":"3063","DOI":"10.1093\/mnras\/stab3631","volume":"510","author":"V Zaverkin","year":"2021","unstructured":"Zaverkin V, Molpeceres G, K\u00e4stner J (2021) Neural-network assisted study of nitrogen atom dynamics on amorphous solid water\u2014II. Diffusion. Mon Notices Royal Astron Soc 510(2):3063\u20133070. https:\/\/doi.org\/10.1093\/mnras\/stab3631","journal-title":"Diffusion. Mon Notices Royal Astron Soc"},{"issue":"1","key":"687_CR17","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1038\/s41524-020-00367-7","volume":"6","author":"G Sivaraman","year":"2020","unstructured":"Sivaraman G, Krishnamoorthy AN, Baur M, Holm C, Stan M, Cs\u00e1nyi G, Benmore C, V\u00e1zquez-Mayagoitia \u00c1 (2020) Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide. NPJ Comput Mater 6(1):104. https:\/\/doi.org\/10.1038\/s41524-020-00367-7","journal-title":"NPJ Comput Mater"},{"key":"687_CR18","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.1c00853","author":"V Zaverkin","year":"2022","unstructured":"Zaverkin V, Netz J, Zills F, K\u00f6hn A, K\u00e4stner J (2022) Thermally averaged magnetic anisotropy tensors via machine learning based on Gaussian moments. J Chem Theory Comput. https:\/\/doi.org\/10.1021\/acs.jctc.1c00853. (( PMID: 34882425))","journal-title":"J Chem Theory Comput"},{"key":"687_CR19","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.carbon.2016.08.024","volume":"109","author":"C de Tomas","year":"2016","unstructured":"de Tomas C, Suarez-Martinez I, Marks NA (2016) Graphitization of amorphous carbons: a comparative study of interatomic potentials. Carbon 109:681\u2013693","journal-title":"Carbon"},{"issue":"22","key":"687_CR20","doi-asserted-by":"publisher","DOI":"10.1063\/1.5000911","volume":"147","author":"S Desai","year":"2017","unstructured":"Desai S, Li C, Shen T, Strachan A (2017) Molecular modeling of the microstructure evolution during carbon fiber processing. J Chem Phys 147(22):224705. https:\/\/doi.org\/10.1063\/1.5000911. (Publisher: American Institute of Physics)","journal-title":"J Chem Phys"},{"key":"687_CR21","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1016\/j.ijheatmasstransfer.2013.11.065","volume":"70","author":"RN Salaway","year":"2014","unstructured":"Salaway RN, Zhigilei LV (2014) Molecular dynamics simulations of thermal conductivity of carbon nanotubes: Resolving the effects of computational parameters. Int J Heat Mass Transfer 70:954\u2013964","journal-title":"Int J Heat Mass Transfer"},{"issue":"10","key":"687_CR22","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1002\/jcc.21787","volume":"32","author":"N Michaud-Agrawal","year":"2011","unstructured":"Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O (2011) MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem 32(10):2319\u20132327. https:\/\/doi.org\/10.1002\/jcc.21787","journal-title":"J Comput Chem"},{"issue":"4","key":"687_CR23","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1021\/acs.jcim.9b00066","volume":"59","author":"MT Humbert","year":"2019","unstructured":"Humbert MT, Zhang Y, Maginn EJ (2019) PyLAT: python LAMMPS analysis tools. J Chem Inf Model 59(4):1301\u20131305. https:\/\/doi.org\/10.1021\/acs.jcim.9b00066","journal-title":"J Chem Inf Model"},{"issue":"8","key":"687_CR24","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.1016\/j.bpj.2015.08.015","volume":"109","author":"RT McGibbon","year":"2015","unstructured":"McGibbon RT, Beauchamp K, Harrigan M, Klein C, Swails J, Hern\u00e1ndez C, Schwantes C, Wang L-P, Lane T, Pande V (2015) Mdtraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys J 109(8):1528\u20131532. https:\/\/doi.org\/10.1016\/j.bpj.2015.08.015","journal-title":"Biophys J"},{"issue":"7","key":"687_CR25","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.1021\/ct400341p","volume":"9","author":"DR Roe","year":"2013","unstructured":"Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084\u20133095. https:\/\/doi.org\/10.1021\/ct400341p. (PMID: 26583988)","journal-title":"J Chem Theory Comput"},{"key":"687_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2020.107275","volume":"254","author":"V Ramasubramani","year":"2020","unstructured":"Ramasubramani V, Dice BD, Harper ES, Spellings MP, Anderson JA, Glotzer SC (2020) freud: A software suite for high throughput analysis of particle simulation data. Comput Phys Commun 254:107275. https:\/\/doi.org\/10.1016\/j.cpc.2020.107275","journal-title":"Comput Phys Commun"},{"key":"687_CR27","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/0263-7855(96)00018-5","volume":"14","author":"W Humphrey","year":"1996","unstructured":"Humphrey W, Dalke A, Schulten K (1996) VMD\u2014visual molecular dynamics. J Mol Graph 14:33\u201338","journal-title":"J Mol Graph"},{"key":"687_CR28","doi-asserted-by":"publisher","unstructured":"David L Dotson, Sean L Seyler, Max Linke, Richard J Gowers (2016) Oliver Beckstein: datreant: persistent, Pythonic trees for heterogeneous data. In: Sebastian Benthall, Scott Rostrup (eds.) Proceedings of the 15th Python in Science Conference, pp. 51\u201356. https:\/\/doi.org\/10.25080\/Majora-629e541a-007","DOI":"10.25080\/Majora-629e541a-007"},{"key":"687_CR29","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.commatsci.2018.01.035","volume":"146","author":"CS Adorf","year":"2018","unstructured":"Adorf CS, Dodd PM, Ramasubramani V, Glotzer SC (2018) Simple data and workflow management with the signac framework. Comput Mater Sci 146:220\u2013229. https:\/\/doi.org\/10.1016\/j.commatsci.2018.01.035","journal-title":"Comput Mater Sci"},{"key":"687_CR30","unstructured":"Bayer M (2012) Sqlalchemy. In: Brown A, Wilson G. (eds.) The \narchitecture of open source applications volume II: structure, scale, and a few more fearless hacks. aosabook.org. http:\/\/aosabook.org\/en\/sqlalchemy.html. Accessed 03 Feb 2022."},{"issue":"1","key":"687_CR31","doi-asserted-by":"publisher","first-page":"160018","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"...Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, \u2019t Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The fair guiding principles for scientific data management and stewardship. Sci Data 3(1):160018. https:\/\/doi.org\/10.1038\/sdata.2016.18","journal-title":"Sci Data"},{"key":"687_CR32","unstructured":"Collette A (2013) Python and HDF5. O\u2019Reilly Media, Sebastopol."},{"issue":"6","key":"687_CR33","doi-asserted-by":"publisher","first-page":"1546","DOI":"10.1016\/j.cpc.2014.01.018","volume":"185","author":"P de Buyl","year":"2014","unstructured":"de Buyl P, Colberg PH, H\u00f6fling F (2014) H5md: a structured, efficient, and portable file format for molecular data. Comp Phys Commun 185(6):1546\u20131553. https:\/\/doi.org\/10.1016\/j.cpc.2014.01.018","journal-title":"Comp Phys Commun"},{"issue":"D1","key":"687_CR34","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1093\/nar\/gky1033","volume":"47","author":"S Kim","year":"2018","unstructured":"Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE (2018) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47(D1):1102\u20131109. https:\/\/doi.org\/10.1093\/nar\/gky1033","journal-title":"Nucleic Acids Res"},{"key":"687_CR35","doi-asserted-by":"publisher","unstructured":"Fraux G, Fine J, Ezavod, Barletta GP, Scalfi L, Dimura M: Chemfiles\/chemfiles: Version 0.9.3. https:\/\/doi.org\/10.5281\/zenodo.3653157.","DOI":"10.5281\/zenodo.3653157"},{"key":"687_CR36","doi-asserted-by":"publisher","unstructured":"Lindahl, Abraham, Hess, van\u00a0der Spoel (2021) ROMACS 2021.4 Manual. Zenodo https:\/\/doi.org\/10.5281\/zenodo.5636522","DOI":"10.5281\/zenodo.5636522"},{"issue":"16","key":"687_CR37","doi-asserted-by":"publisher","first-page":"1668","DOI":"10.1002\/jcc.20290","volume":"26","author":"DA Case","year":"2005","unstructured":"Case DA, Cheatham TE III, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The amber biomolecular simulation programs. J Comput Chem 26(16):1668\u20131688. https:\/\/doi.org\/10.1002\/jcc.20290","journal-title":"J Comput Chem"},{"issue":"2","key":"687_CR38","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1002\/jcc.540040211","volume":"4","author":"BR Brooks","year":"1983","unstructured":"Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) Charmm: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4(2):187\u2013217. https:\/\/doi.org\/10.1002\/jcc.540040211","journal-title":"J Comput Chem"},{"issue":"1","key":"687_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1006\/jcph.1995.1039","volume":"117","author":"S Plimpton","year":"1995","unstructured":"Plimpton S (1995) Fast parallel algorithms for short-range molecular dynamics. J Comput Phys 117(1):1\u201319. https:\/\/doi.org\/10.1006\/jcph.1995.1039","journal-title":"J Comput Phys"},{"key":"687_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2021.108171","volume":"271","author":"AP Thompson","year":"2022","unstructured":"Thompson AP, Aktulga HM, Berger R, Bolintineanu DS, Brown WM, Crozier PS, in \u2019t Veld, P.J., Kohlmeyer, A., Moore, S.G., Nguyen, T.D., Shan, R., Stevens, M.J., Tranchida, J., Trott, C., Plimpton SJ, (2022) Lammps\u2014a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp Phys Commun 271:108171. https:\/\/doi.org\/10.1016\/j.cpc.2021.108171","journal-title":"Comp Phys Commun"},{"issue":"14","key":"687_CR41","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1140\/epjst\/e2019-800186-9","volume":"227","author":"F Weik","year":"2019","unstructured":"Weik F, Weeber R, Szuttor K, Breitsprecher K, de Graaf J, Kuron M, Landsgesell J, Menke H, Sean D, Holm C (2019) Espresso 4.0\u2014an extensible software package for simulating soft matter systems. Eur Phys J Spec Top 227(14):1789\u20131816. https:\/\/doi.org\/10.1140\/epjst\/e2019-800186-9","journal-title":"Eur Phys J Spec Top"},{"key":"687_CR42","unstructured":"pandas\u00a0development team, T.: Pandas-dev\/pandas: Pandas. https:\/\/doi.org\/10.5281\/zenodo.3509134"},{"key":"687_CR43","doi-asserted-by":"publisher","unstructured":"Wes McKinney: Data Structures for Statistical Computing in Python. In: St\u00e9fan van\u00a0der Walt, Jarrod Millman (eds.) Proceedings of the 9th Python in Science Conference, pp. 56\u201361 (2010). https:\/\/doi.org\/10.25080\/Majora-92bf1922-00a","DOI":"10.25080\/Majora-92bf1922-00a"},{"issue":"3","key":"687_CR44","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCSE.2007.53","volume":"9","author":"F P\u00e9rez","year":"2007","unstructured":"P\u00e9rez F, Granger BE (2007) IPython: a system for interactive scientific computing. Comput Sci Eng 9(3):21\u201329. https:\/\/doi.org\/10.1109\/MCSE.2007.53. (Publisher: IEEE Computer Society)","journal-title":"Comput Sci Eng"},{"key":"687_CR45","unstructured":"Kluyver T, Ragan-Kelley B, P\u00e9rez F, Granger B, Bussonnier M, Frederic J, Kelley K, Hamrick J, Grout J, Corlay S, Ivanov P, Avila D, Abdalla S, Willing C, Team JD (2016) Jupyter Notebooks\u2014a publishing format for reproducible computational workflows. In: Loizides F, Scmidt B. (eds.) Positioning and power in academic publishing: players, agents and agendas, IOS Press, pp 87\u201390."},{"key":"687_CR46","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S et al (2015) TensorFlow: Large-scale machine learning on heterogeneous systems. Available via Tensorflow. https:\/\/www.tensorflow.org\/about\/bib. Accessed 04 Feb 2022."},{"key":"687_CR47","doi-asserted-by":"publisher","unstructured":"Frenkel D, Smit B (2002) Understanding molecular simulation, 2nd edn. Academic Press, San Diego. https:\/\/doi.org\/10.1016\/B978-012267351-1\/50006-7. Publication Title: Understanding Molecular Simulation (Second Edition)","DOI":"10.1016\/B978-012267351-1\/50006-7"},{"key":"687_CR48","unstructured":"Waseda Y (1980) The Structure of non-crystalline materials: liquids and amorphous solids. Advanced Book Program. McGraw-Hill International Book Company, New York."},{"issue":"5","key":"687_CR49","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1351\/pac199466051077","volume":"66","author":"P Muller","year":"1994","unstructured":"Muller P (1994) Glossary of terms used in physical organic chemistry (IUPAC Recommendations 1994). Pure Appl Chem 66(5):1077\u20131184. https:\/\/doi.org\/10.1351\/pac199466051077. (Place: Berlin, Boston Publisher: De Gruyter)","journal-title":"Pure Appl Chem"},{"issue":"3","key":"687_CR50","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1214\/aoms\/1177729392","volume":"23","author":"J Kiefer","year":"1952","unstructured":"Kiefer J, Wolfowitz J (1952) Stochastic estimation of the maximum of a regression function. Ann Math Stat 23(3):462\u2013466. https:\/\/doi.org\/10.1214\/aoms\/1177729392","journal-title":"Ann Math Stat"},{"issue":"8","key":"687_CR51","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1021\/ac60214a047","volume":"36","author":"A Savitzky","year":"1964","unstructured":"Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627\u20131639. https:\/\/doi.org\/10.1021\/ac60214a047","journal-title":"Anal Chem"},{"issue":"4","key":"687_CR52","doi-asserted-by":"publisher","DOI":"10.1063\/1.4789305","volume":"138","author":"J Smiatek","year":"2013","unstructured":"Smiatek J, Heuer A, Wagner H, Studer A, Hentschel C, Chi L (2013) Coat thickness dependent adsorption of hydrophobic molecules at polymer brushes. J Chem Phys 138(4):044904. https:\/\/doi.org\/10.1063\/1.4789305","journal-title":"J Chem Phys"},{"issue":"2","key":"687_CR53","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/16\/2\/025001","volume":"16","author":"J Smiatek","year":"2014","unstructured":"Smiatek J, Wohlfarth A, Holm C (2014) The solvation and ion condensation properties for sulfonated polyelectrolytes in different solvents-a computational study. New J Phys 16(2):025001. https:\/\/doi.org\/10.1088\/1367-2630\/16\/2\/025001. (Publisher: IOP Publishing)","journal-title":"New J Phys"},{"issue":"6","key":"687_CR54","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1063\/1.1748352","volume":"19","author":"JG Kirkwood","year":"1951","unstructured":"Kirkwood JG, Buff FP (1951) The statistical mechanical theory of solutions I. J Chem Phys 19(6):774\u2013777. https:\/\/doi.org\/10.1063\/1.1748352","journal-title":"J Chem Phys"},{"key":"687_CR55","doi-asserted-by":"publisher","first-page":"18924","DOI":"10.1039\/C7CP03717A","volume":"19","author":"T Kobayashi","year":"2017","unstructured":"Kobayashi T, Reid JESJ, Shimizu S, Fyta M, Smiatek J (2017) The properties of residual water molecules in ionic liquids: a comparison between direct and inverse kirkwood-buff approaches. Phys Chem Chem Phys 19:18924\u201318937. https:\/\/doi.org\/10.1039\/C7CP03717A","journal-title":"Phys Chem Chem Phys"},{"key":"687_CR56","unstructured":"Janke W (2002) Statistical analysis of simulations: data correlations and error estimation. In: Grotendorst J, Marx D, Muramatsu A (eds) Quantum Simulations of Complex Many-Body Systems: From Theory to Algorithms. NIC Series, vol 10. John von Neumann Institute for Computing, J\u00fclich, pp 423-445."},{"issue":"8","key":"687_CR57","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1063\/1.1700722","volume":"20","author":"MS Green","year":"1952","unstructured":"Green MS (1952) Markoff random processes and the statistical mechanics of time-dependent phenomena. J Chem Phys 20(8):1281\u20131295. https:\/\/doi.org\/10.1063\/1.1700722","journal-title":"J Chem Phys"},{"issue":"6","key":"687_CR58","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1143\/JPSJ.12.570","volume":"12","author":"R Kubo","year":"1957","unstructured":"Kubo R (1957) Statistical-mechanical theory of irreversible processes. I. General theory and simple applications to magnetic and conduction problems. J Phys Soc Japan 12(6):570\u2013586. https:\/\/doi.org\/10.1143\/JPSJ.12.570","journal-title":"J Phys Soc Japan"},{"key":"687_CR59","doi-asserted-by":"publisher","unstructured":"Kubo R, Toda M, Hashitsume N (1991) Statistical physics II: nonequilibrium statistical mechanics, 2nd edn. Springer Series in Solid-State Sciences, Springer Ser. Solid-State Statistical Physics. Springer, Berlin Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-58244-8","DOI":"10.1007\/978-3-642-58244-8"},{"issue":"1","key":"687_CR60","doi-asserted-by":"publisher","DOI":"10.1063\/1.4731450","volume":"137","author":"A Kinaci","year":"2012","unstructured":"Kinaci A, Haskins JB, \u00c7a\u011f\u0131n T (2012) On calculation of thermal conductivity from Einstein relation in equilibrium molecular dynamics. J Chem Phy 137(1):014106. https:\/\/doi.org\/10.1063\/1.4731450. (Publisher: American Institute of Physics)","journal-title":"J Chem Phy"},{"issue":"45","key":"687_CR61","doi-asserted-by":"publisher","first-page":"13212","DOI":"10.1021\/jp204182c","volume":"115","author":"HK Kashyap","year":"2011","unstructured":"Kashyap HK, Annapureddy HVR, Raineri FO, Margulis CJ (2011) How is charge transport different in ionic liquids and electrolyte solutions? J Phys Chem B 115(45):13212\u201313221. https:\/\/doi.org\/10.1021\/jp204182c. (PMID: 22022889)","journal-title":"J Phys Chem B"},{"key":"687_CR62","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1088\/0031-8949\/1991\/t39\/057","volume":"T39","author":"MJ Gillan","year":"1991","unstructured":"Gillan MJ (1991) The molecular dynamics calculation of transport coefficients. Phys Scripta T39:362\u2013366. https:\/\/doi.org\/10.1088\/0031-8949\/1991\/t39\/057. (Publisher: IOP Publishing)","journal-title":"Phys Scripta"},{"key":"687_CR63","doi-asserted-by":"crossref","unstructured":"Lam P, Dietrich J, Pearce DJ (2020) Putting the semantics into semantic versioning. arXiv:2008.07069","DOI":"10.1145\/3426428.3426922"},{"key":"687_CR64","unstructured":"Zhou Q-Y, Park J, Koltun V (2018) Open3D: a modern library for 3D data processing. arXiv:1801.09847"},{"key":"687_CR65","unstructured":"Bokeh Development Team (2018) Bokeh: python library for interactive visualization. https:\/\/bokeh.pydata.org\/en\/latest\/"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00687-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-023-00687-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00687-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T11:20:12Z","timestamp":1676287212000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-023-00687-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,11]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["687"],"URL":"https:\/\/doi.org\/10.1186\/s13321-023-00687-y","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,11]]},"assertion":[{"value":"10 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"19"}}