Zobrazeno 1 - 10
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pro vyhledávání: '"Schwalbe-Koda, Daniel"'
An accurate description of information is relevant for a range of problems in atomistic modeling, such as sampling methods, detecting rare events, analyzing datasets, or performing uncertainty quantification (UQ) in machine learning (ML)-driven simul
Externí odkaz:
http://arxiv.org/abs/2404.12367
Publikováno v:
Digital Discovery 2, 1911 (2023)
Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic and organic synthesis conditions, computational studies of zeolite sy
Externí odkaz:
http://arxiv.org/abs/2307.10935
Autor:
Vita, Joshua A., Schwalbe-Koda, Daniel
Publikováno v:
Machine Learning: Science and Technology 4, 035031 (2023)
Over the last few years, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit
Externí odkaz:
http://arxiv.org/abs/2302.05823
Autor:
Schwalbe-Koda, Daniel
Publikováno v:
Computational Materials Science 230, 112439 (2023)
Advances in high-throughput simulation (HTS) software enabled computational databases and big data to become common resources in materials science. However, while computational power is increasingly larger, software packages orchestrating complex wor
Externí odkaz:
http://arxiv.org/abs/2301.08841
Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependen
Externí odkaz:
http://arxiv.org/abs/2107.05109
Publikováno v:
Nat. Commun. 12, 5104 (2021)
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within wel
Externí odkaz:
http://arxiv.org/abs/2101.11588
Autor:
Ruza, Jurgis, Wang, Wujie, Schwalbe-Koda, Daniel, Axelrod, Simon, Harris, William H., Gomez-Bombarelli, Rafael
Publikováno v:
The Journal of Chemical Physics 153.16 (2020): 164501
Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemb
Externí odkaz:
http://arxiv.org/abs/2007.14144
Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize p
Externí odkaz:
http://arxiv.org/abs/1907.01632
Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis. Although interzeolite transformations enable selective crystallization, their design lacks predictions to connect framework similarity and experimental
Externí odkaz:
http://arxiv.org/abs/1812.02685
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