{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/222efac933067c17e4cd9b0e1ecd366ec/simtechpuma", "tags" : [ "EXC2075","PN3","PN3A-4","slected" ], "intraHash" : "22efac933067c17e4cd9b0e1ecd366ec", "interHash" : "cb8f6c71f4734d3fe915dd0dccf2086f", "label" : "High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", "user" : "simtechpuma", "description" : "", "date" : "2025-02-14 11:17:09", "changeDate" : "2025-02-14 11:17:09", "count" : 5, "pub-type": "article", "journal": "npj Computational Materials", "year": "2023", "url": "https://doi.org/10.1038/s41524-022-00956-8", "author": [ "Jong Hyun Jung","Prashanth Srinivasan","Axel Forslund","Blazej Grabowski" ], "authors": [ {"first" : "Jong Hyun", "last" : "Jung"}, {"first" : "Prashanth", "last" : "Srinivasan"}, {"first" : "Axel", "last" : "Forslund"}, {"first" : "Blazej", "last" : "Grabowski"} ], "volume": "9","number": "1","pages": "3","abstract": "Accurate prediction of thermodynamic properties requires an extremely accurate representation of the free-energy surface. Requirements are twofold---first, the inclusion of the relevant finite-temperature mechanisms, and second, a dense volume--temperature grid on which the calculations are performed. A systematic workflow for such calculations requires computational efficiency and reliability, and has not been available within an ab initio framework so far. Here, we elucidate such a framework involving direct upsampling, thermodynamic integration and machine-learning potentials, allowing us to incorporate, in particular, the full effect of anharmonic vibrations. The improved methodology has a five-times speed-up compared to state-of-the-art methods. We calculate equilibrium thermodynamic properties up to the melting point for bcc Nb, magnetic fcc Ni, fcc Al, and hcp Mg, and find remarkable agreement with experimental data. A strong impact of anharmonicity is observed specifically for Nb. The introduced procedure paves the way for the development of ab initio thermodynamic databases.", "issn" : "2057-3960", "doi" : "10.1038/s41524-022-00956-8", "bibtexKey": "Jung2023" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/287f3a38e8d43c2711bff361e7c7e8a59/simtechpuma", "tags" : [ "EXC2075","PN6","PN6-6","slected" ], "intraHash" : "87f3a38e8d43c2711bff361e7c7e8a59", "interHash" : "347d569c3aae7cd4934c93a157da3cfe", "label" : "S4: Self-Supervised learning of Spatiotemporal Similarity", "user" : "simtechpuma", "description" : "", "date" : "2025-02-14 11:14:18", "changeDate" : "2025-02-14 11:14:18", "count" : 11, "pub-type": "article", "journal": "IEEE Transactions on Visualization and Computer Graphics", "year": "2021", "url": "", "author": [ "Gleb Tkachev","Steffen Frey","Thomas Ertl" ], "authors": [ {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Thomas", "last" : "Ertl"} ], "abstract": "We introduce an ML-driven approach that enables interactive example-based queries for similar behavior in ensembles of spatiotemporal scientific data. This addresses an important use case in the visual exploration of simulation and experimental data, where data is often large, unlabeled and has no meaningful similarity measures available. We exploit the fact that nearby locations often exhibit similar behavior and train a Siamese Neural Network in a self-supervised fashion, learning an expressive latent space for spatiotemporal behavior. This space can be used to find similar behavior with just a few user-provided examples. We evaluate this approach on several ensemble datasets and compare with multiple existing methods, showing both qualitative and quantitative results.", "isbn" : "10.1109/TVCG.2021.3101418", "bibtexKey": "tkachev2021selfsupervised" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/203c00fc6353384ba89e15455ba36bf4a/simtechpuma", "tags" : [ "EXC2075","PN3","PN3-8(II)","slected" ], "intraHash" : "03c00fc6353384ba89e15455ba36bf4a", "interHash" : "ca9fa9dbda23e144481ba0100ab82b97", "label" : "Probing Self-Diffusion of Guest Molecules in a Covalent Organic Framework: Simulation and Experiment", "user" : "simtechpuma", "description" : "", "date" : "2025-02-14 11:14:18", "changeDate" : "2025-02-14 11:14:18", "count" : 6, "pub-type": "article", "journal": "ACS Nano","publisher":"American Chemical Society (ACS)", "year": "2024", "url": "http://dx.doi.org/10.1021/acsnano.3c12167", "author": [ "Lars Grunenberg","Christopher Keßler","Tiong Wei Teh","Robin Schuldt","Fabian Heck","Johannes Kästner","Joachim Groß","Niels Hansen","Bettina V. Lotsch" ], "authors": [ {"first" : "Lars", "last" : "Grunenberg"}, {"first" : "Christopher", "last" : "Keßler"}, {"first" : "Tiong Wei", "last" : "Teh"}, {"first" : "Robin", "last" : "Schuldt"}, {"first" : "Fabian", "last" : "Heck"}, {"first" : "Johannes", "last" : "Kästner"}, {"first" : "Joachim", "last" : "Groß"}, {"first" : "Niels", "last" : "Hansen"}, {"first" : "Bettina V.", "last" : "Lotsch"} ], "volume": "18","number": "25","pages": "16091\u201316100", "issn" : "1936-086X", "doi" : "10.1021/acsnano.3c12167", "bibtexKey": "Grunenberg_2024" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/28da203b6974ed564e652588fd606d8e6/simtechpuma", "tags" : [ "EXC2075","PN2","PN2-5","slected" ], "intraHash" : "8da203b6974ed564e652588fd606d8e6", "interHash" : "5cfde6ff2e0d4779533cad5bb4465b95", "label" : "The T1150A cancer mutant of the protein lysine dimethyltransferase NSD2 can introduce H3K36 trimethylation", "user" : "simtechpuma", "description" : "", "date" : "2025-02-14 11:14:18", "changeDate" : "2025-02-14 11:14:18", "count" : 10, "pub-type": "article", "journal": "Journal of Biological Chemistry", "year": "2023", "url": "https://www.sciencedirect.com/science/article/pii/S0021925823018240", "author": [ "Mina S. Khella","Philipp Schnee","Sara Weirich","Tan Bui","Alexander Bröhm","Pavel Bashtrykov","Jürgen Pleiss","Albert Jeltsch" ], "authors": [ {"first" : "Mina S.", "last" : "Khella"}, {"first" : "Philipp", "last" : "Schnee"}, {"first" : "Sara", "last" : "Weirich"}, {"first" : "Tan", "last" : "Bui"}, {"first" : "Alexander", "last" : "Bröhm"}, {"first" : "Pavel", "last" : "Bashtrykov"}, {"first" : "Jürgen", "last" : "Pleiss"}, {"first" : "Albert", "last" : "Jeltsch"} ], "volume": "299","number": "6","pages": "104796--","abstract": "Protein lysine methyltransferases (PKMTs) play essential roles in gene expression regulation and cancer development. Somatic mutations in PKMTs are frequently observed in cancer cells. In biochemical experiments, we show here that the NSD1 mutations Y1971C, R2017Q, and R2017L observed mostly in solid cancers are catalytically inactive suggesting that NSD1 acts as a tumor suppressor gene in these tumors. In contrast, the frequently observed T1150A in NSD2 and its T2029A counterpart in NSD1, both observed in leukemia, are hyperactive and introduce up to three methyl groups in H3K36 in biochemical and cellular assays, while wildtype NSD2 and NSD1 only introduce up to two methyl groups. In Molecular Dynamics simulations, we determined key mechanistic and structural features controlling the product specificity of this class of enzymes. Simulations with NSD2 revealed that H3K36me3 formation is possible due to an enlarged active site pocket of T1150A and loss of direct contacts of T1150 to critical residues which regulate the product specificity of NSD2. Bioinformatic analyses of published data suggested that the generation of H3K36me3 by NSD2 T1150A could alter gene regulation by antagonizing H3K27me3 finally leading to the upregulation of oncogenes.", "orcid" : "0000-0003-1045-8202", "issn" : "00219258", "doi" : "https://doi.org/10.1016/j.jbc.2023.104796", "bibtexKey": "khella2023t1150a" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/275764a868f3139eb9732353dba44bea0/simtechpuma", "tags" : [ "EXC2075","PN3","PN3-4","PN3-4(II)","slected" ], "intraHash" : "75764a868f3139eb9732353dba44bea0", "interHash" : "74625c1305179252c4880187864a3fcf", "label" : "The reduction behavior of sulfurized polyacrylonitrile (SPAN) in lithium\u2013sulfur batteries using a carbonate electrolyte: a computational study", "user" : "simtechpuma", "description" : "", "date" : "2025-02-14 11:14:18", "changeDate" : "2025-02-14 11:14:18", "count" : 6, "pub-type": "article", "journal": "Phys. Chem. Chem. Phys.", "year": "2024", "url": "", "author": [ "S. V. Klostermann","J. Kappler","A. Waigum","M. R. Buchmeiser","A. Köhn","J. Kästner" ], "authors": [ {"first" : "S. V.", "last" : "Klostermann"}, {"first" : "J.", "last" : "Kappler"}, {"first" : "A.", "last" : "Waigum"}, {"first" : "M. R.", "last" : "Buchmeiser"}, {"first" : "A.", "last" : "Köhn"}, {"first" : "J.", "last" : "Kästner"} ], "volume": "26","pages": "9998-10007", "doi" : "10.1039/D3CP06248A", "bibtexKey": "klo24" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c0203b229f9524f712d15ffbe47db0b0/simtechpuma", "tags" : [ "EXC2075","PN7","PN7-6","slected" ], "intraHash" : "c0203b229f9524f712d15ffbe47db0b0", "interHash" : "d6b780508badd621361b9c0639481d09", "label" : "Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme", "user" : "simtechpuma", "description" : "", "date" : "2025-02-14 11:14:18", "changeDate" : "2025-02-14 11:14:18", "count" : 6, "pub-type": "article", "journal": "IFAC-PapersOnLine","publisher":"Elsevier BV", "year": "2022", "url": "https://doi.org/10.1016%2Fj.ifacol.2022.09.109", "author": [ "J. Kneifl","J. Hay","J. Fehr" ], "authors": [ {"first" : "J.", "last" : "Kneifl"}, {"first" : "J.", "last" : "Hay"}, {"first" : "J.", "last" : "Fehr"} ], "volume": "55","number": "20","pages": "283--288","note": "preprint: https://arxiv.org/abs/2110.13583", "doi" : "10.1016/j.ifacol.2022.09.109", "bibtexKey": "Kneifl_2022" } ] }