@article{DurumericCharronTempletonetal.2023, author = {Durumeric, Aleksander and Charron, Nicholas and Templeton, Clark and Musil, F{\´e}lix and Bonneau, Klara and Pasos-Trejo, Aldo and Chen, Yaoyi and Kelkar, Atharva and No{\´e}, Frank and Clementi, Cecilia}, title = {Machine learned coarse-grained protein force-fields: Are we there yet?}, volume = {79}, journal = {Current Opinion in Structural Biology}, doi = {10.1016/j.sbi.2023.102533}, year = {2023}, abstract = {The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges.}, language = {en} } @article{MajewskiPerezThoelkeetal.2023, author = {Majewski, Maciej and P{\´e}rez, Adri{\`a} and Th{\"o}lke, Philipp and Doerr, Stefan and Charron, Nicholas and Giorgino, Toni and Husic, Brooke and Clementi, Cecilia and No{\´e}, Frank and De Fabritiis, Gianni}, title = {Machine learning coarse-grained potentials of protein thermodynamics}, volume = {14}, journal = {Nature Communications}, doi = {10.1038/s41467-023-41343-1}, year = {2023}, abstract = {A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.}, language = {en} } @article{LaeuterKramerRubinetal.2022, author = {L{\"a}uter, Matthias and Kramer, Tobias and Rubin, Martin and Altwegg, Kathrin}, title = {The ice composition close to the surface of comet 67P/Churyumov-Gerasimenko}, volume = {6}, journal = {ACS Earth and Space Chemistry}, number = {5}, arxiv = {http://arxiv.org/abs/2305.16201}, doi = {10.1021/acsearthspacechem.1c00378}, pages = {1189 -- 1203}, year = {2022}, abstract = {The relation between ice composition in the nucleus of comet 67P/Churyumov-Gerasimenko on the one hand and relative abundances of volatiles in the coma on the other hand is important for the interpretation of density measurements in the environment of the cometary nucleus. For the 2015 apparition, in situ measurements from the two ROSINA (Rosetta Orbiter Spectrometer for Ion and Neutral Analysis) sensors COPS (COmet Pressure Sensor) and DFMS (Double Focusing Mass Spectrometer) determined gas densities at the spacecraft position for the 14 gas species H2O, CO2, CO, H2S, O2, C2H6, CH3OH, H2CO, CH4, NH3, HCN, C2H5OH, OCS, and CS2. We derive the spatial distribution of the gas emissions on the complex shape of the nucleus separately for 50 subintervals of the two-year mission time. The most active patches of gas emission are identified on the surface. We retrieve the relation between solar irradiation and observed emissions from these patches. The emission rates are compared to a minimal thermophysical model to infer the surface active fraction of H2O and CO2. We obtain characteristic differences in the ice composition close to the surface between the two hemispheres with a reduced abundance of CO2 ice on the northern hemisphere (locations with positive latitude). We do not see significant differences for the ice composition on the two lobes of 67P/C-G.}, language = {en} } @article{RodriguesPelaDraxl2022, author = {Rodrigues Pel{\´a}, Ronaldo and Draxl, Claudia}, title = {Ehrenfest dynamics implemented in the all-electron package exciting}, volume = {4}, journal = {Electron. Struct.}, doi = {10.1088/2516-1075/ac7afc}, pages = {037001}, year = {2022}, abstract = {Ehrenfest dynamics combined with real-time time-dependent density functional theory has proven to be a reliable tool to study non-adiabatic molecular dynamics with a reasonable computational cost. Among other possibilities, it allows for assessing in real time electronic excitations generated by ultra-fast laser pulses, as e.g., in pump-probe spectroscopy, and their coupling to the nuclear vibrations even beyond the linear regime. In this work, we present its implementation in the all-electron full-potential package exciting. Three cases are presented as examples: diamond and cubic boron nitride (BN) relaxed after an initial lattice distortion, and cubic BN exposed to a laser pulse. Comparison with the Octopus code exhibits good agreement.}, language = {en} } @article{SalemSchintke2022, author = {Salem, Farouk and Schintke, Florian}, title = {Large-Scale Performance of the Data-Flow Scheduler (DFS) and FLESnet}, journal = {CBM Progress Report 2021}, doi = {10.15120/GSI-2022-00599}, pages = {170 -- 171}, year = {2022}, language = {en} } @article{GholamiSchintke2022, author = {Gholami, Masoud and Schintke, Florian}, title = {IOSIG: Declarative I/O-Stream Properties Using Pragmas}, volume = {22}, journal = {Datenbank-Spektrum}, number = {2}, publisher = {Springer Berlin Heidelberg}, doi = {10.1007/s13222-022-00419-w}, pages = {109 -- 119}, year = {2022}, language = {en} } @article{JiAnitaFournier2025, author = {Ji, Hongchen and Anita, Ragyanszki and Fournier, Rene}, title = {Computational Study of the Reactions of CH2 with HCNO and HNCO}, volume = {103}, journal = {Canadian Journal of Chemistry}, number = {7}, doi = {10.1139/cjc-2024-0203}, pages = {386 -- 395}, year = {2025}, abstract = {We present a computational approach for screening reaction mechanisms with machine learning estimates of energy barriers. A comprehensive screening of thousands of reactions identified the CH2 reactions with HCNO and HNCO as possible sources of relatively complex organic molecules in space. We report detailed reaction mechanisms, including TS, intermediate, and product energies, calculated with density functional theory and coupled cluster theory. Singlet CH2, located 9 kcal/mol above the triplet ground state, reacts with HCNO or HNCO without a barrier, producing four prod11 ucts: CH2NCHO, N-methyleneformamide, the thermodynamically favored product; NHCHCHO, imine acetaldehyde; NHCHOCH; and (CH2OC)NH, oxiran-2-ylazanide. The lowest energy pathway for CH2 + HCNO, involving a triplet-to-singlet crossing, has a barrier of 8 kcal/mol and leads to N -methyleneformamide, imine acetaldehyde, and NHCHOCH. The reaction of triplet CH2 with HNCO has a lowest energy pathway with a barrier of 11 kcal/mol, yielding CH2(CO)NH.}, language = {en} } @article{RayaMorenoDaschFarahanietal.2026, author = {Raya-Moreno, Mart{\´i} and Dasch, Noah Alexy and Farahani, Nasrin and Gonzalez Oliva, Ignacio and Gulans, Andris and Hossain, Manoar and Kleine, Hannah and Kuban, Martin and Lubeck, Sven and Maurer, Benedikt and Pavone, Pasquale and Peschel, Fabian and Popova-Gorelova, Daria and Qiao, Lu and Richter, Elias and Rigamonti, Santiago and Rodrigues Pel{\´a}, Ronaldo and Sinha, Kshitij and Speckhard, Daniel T. and Tillack, Sebastian and Tumakov, Dmitry and Hong, Seokhyun and Uzulis, Janis and Voiculescu, Mara and Vona, Cecilia and Yang, Mao and Draxl, Claudia}, title = {An exciting approach to theoretical spectroscopy}, arxiv = {http://arxiv.org/abs/2601.11388}, year = {2026}, language = {en} } @article{QiaoRodriguesPelaDraxl2026, author = {Qiao, Lu and Rodrigues Pel{\´a}, Ronaldo and Draxl, Claudia}, title = {First-principles approach to ultrafast pump-probe spectroscopy in solids}, arxiv = {http://arxiv.org/abs/2509.07612}, year = {2026}, language = {en} }