6 Result(s)
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Daniel Schwalbe-Koda
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Article
Graph similarity drives zeolite diffusionless transformations and intergrowth
Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis1–3. Interzeolite transformations enable selective crystallization4–7, but are often too complex to be designed by ...
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Chapter
Generative Models for Automatic Chemical Design
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 expensiv...
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Article
Open AccessDifferentiable sampling of molecular geometries with uncertainty-based adversarial attacks
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...
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Article
Open AccessSampling lattices in semi-grand canonical ensemble with autoregressive machine learning
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 w...
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Article
Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate i...
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Article
Open AccessApproaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others
Approaching the level of molecular recognition of enzymes with solid catalysts is a challenging goal, achieved in this work for the competing transalkylation and disproportionation of diethylbenzene catalyzed ...
