UCLA

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

Predicting coverage effects in catalysis using fast data pipelines logo

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 logo

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]

Automating materials simulations in distributed computing logo

Automating materials simulations in distributed computing

We released a new software package to perform high-throughput simulations in distributed computing environments. The package, mkite, is expected to accelerate complex materials workflows and facilitate their deployment in heterogeneous hardware infrastructure. [paper] [code] [docs]

Our main tools

Machine Learning logo

Machine Learning

We develop new ML methods to accelerate materials design
Simulation Workflows logo

Simulation Workflows

We integrate complex physics simulations with high-performance computing
Theory & Data Science logo

Theory & Data Science

We propose data-driven models to elucidate materials synthesis