Digital Synthesis Lab
(PI: Daniel Schwalbe-Koda)
We develop computational methods to enable predictive materials synthesis, thus accelerating their design beyond screening. Using a range of tools - from databases to machine learning - we propose solutions in energy, sustainability, and AI.
Recent Highlights
Atomistic information theory for thermodynamics, UQ, and machine learning
Machine learning interatomic potentials (MLIPs) can bypass limitations of density functional theory (DFT) approaches regarding computational cost and scaling, but MLIPs often require heuristics, from training set selection to uncertainty quantification (UQ). By proposing an atomistic information theory, we showed that the information entropy from a distribution of local descriptors can be used in a range of problems in atomistic simulations, such as explaining trends in MLIP errors, rationalizing dataset analysis/compression, providing a robust UQ estimate for ML-driven simulations, and detecting outliers in atomistic simulations. We also proposed parallels between thermodynamic and information entropy and connect our information-theoretical approach to nucleation and growth. Our approach was demonstrated in a number of applications, and offers a general perspective on how materials theory, computation, and machine learning can be used to solve a range of problems in materials science. [preprint] [code]
Predicting coverage effects in catalysis using fast data pipelines
Modeling realistic interfaces using simulations is a challenge in computational catalysis. Especially in cases involving adsorbate-adsorbate interactions and co-adsorption, the number of configurations to evaluate grows rapidly with number of adsorbates, facet symmetry, and more, making DFT approaches overly expensive. On the other hand, machine learning approaches can suffer under extrapolation and may require training pipelines relying on active learning. We proposed an efficient data generation strategy to control the extrapolation of neural network models and perform fast sampling in different coverage regimes. Our approach was demonstrated by sampling high-dimensional spaces of CO coverage on six copper facets, as well as the co-adsorption of CO and CHOH on Rh(111), showing good agreement with experimental results and revealing substantial differences in kinetic pathways depending on the coverage configuration. [preprint]
Predicting inorganic and organic synthesis conditions in zeolites
Modeling materials synthesis is a complex task, especially in high-dimensional composition spaces such as those from zeolites. While we were successful in predicting organic templates for zeolite synthesis in the past, predicting inorganic synthesis conditions remained elusive. We showed that crystallographic distance metrics and machine learning can be combined to create inorganic synthesis-structure relationships in zeolites, demonstrating excellent agreement with the literature. [paper] [code] [website]
Our main tools
Machine Learning
Simulation Workflows
