Projects

(1) Quantifying rupture risk of brain aneurysms by combining morphological descriptors and blood flow data from large-scale lattice Boltzmann simulations

(2) Synthesis of Oxide Heterostructures by Design

(3) High Fidelity Universal Prediction of Bandgaps in Inorganic Materials

(4) Machine Learning for Identification and Automated Analysis of 2D Supramolecular Self-Assemblies at Surfaces from Scanning Tunneling Microscopy Data

(5) Image-based microstructural meshing in OOF

(6) Data-driven Analysis of Correlations between Chemical Structure and Electrical Function at the Nanoscale

(7) Finding Predictive Descriptors for Singlet Fission: Revealing Fundamental Physics in Data

(8) Mitigating Hazards Posed by Stretchable Electronic Circuits: Liquid Metal Embrittlement by Exposure of Engineering Alloys to Eutectic Gallium Indium

(9) Computational discovery of novel organic metals and narrow-gap semiconductors with generative models

(10) A scalable deep representation learning framework to accelerate ab initio molecular dynamics simulations

(11) Machine-learned model for accelerating discovery of photocatalysts

(12) Development of a data-driven method to predict ReaxFF force field parameters

(13) Dilute Solute Diffusion

(14) Unsupervised Classification of Domains in Nanostructured Thin-films

(15) Using machine learning to enhance the efficiency of rare event sampling methods

(16) A scalable deep representation learning framework to accelerate ab initio molecular dynamics simulations

(17) Rapid identification and characterization of defects in perovskite oxides

(18) Characterizing Protein Hydrophobicity Using High-Dimensional Descriptors

(19) Materials design based on machine learning of optical response

(20) Data driven discovery of structurally defined 2D organic materials

(21) Machine Learning for Structure-Performance Relationships in Organic Semiconducting Devices