Education and Training
B.S., Gordon College
Ph.D. University of Notre Dame
Postdoctoral Fellow, Johns Hopkins University and NSF CCI Center for Sustainable Nanotechnology
Our understanding of microscopic systems depends heavily on our ability to connect macroscopic observations to molecular pictures. Improving this connection will spur the advancement of a wide variety of fields of inquiry. Because of this, research in the Daly lab will be focused on using modern computational techniques to elucidate the structure and dynamics of complex molecular systems and connect these observations directly to experiment via fruitful collaboration. The lab currently has three major research aims.
Aim 1: Spectroscopic Mapping via Machine Learning. Techniques for relating complex vibrational observables with physical variables that are readily available in molecular simulations have been used extensively in the past two decades to connect to vibrational spectroscopy experiments with great success. However, the simplicity of maps based mainly on linear regression of electrostatic factors limits their maximum accuracy. Starting with vibrational modes for which successful maps have not been created, we will endeavor to deeply understand the character of the vibrations using quantum chemistry calculations. We will then combine these with molecular dynamics simulations, producing the large amounts of data required to build an accurate map. Finally, using advanced machine learning techniques, we build new maps and apply them to uncover powerful new insights about complex systems. In this aim, a current collaborative project involves work with Lou Charkoudian and Casey Londergan at Haverford to build a map for the Raman active alkyne stretching mode, which will be used to understand the dynamics of carrier proteins.
Aim 2: Molecular Dynamics of Complex Ionic Liquids. Ionic liquids been researched widely as possible green designer materials for a wide variety of applications. Recent effort has focused more directly on ionic liquids that are confined in some way, such as within polymer matricies. Additionally, researchers have started to explore the properties of ionic liquids with multiple types of anions or cations, such as mixed ionic liquids. In this project, we examine the effect of such complexity on the properties of ionic liquids, with a particular focus on applications such as carbon capture.
Aim 3: The Biological Interactions of Silica Nanoparticle. Silica (SiO2) nanoparticles have recently been explored for several medical applications. When used in these applications, the nanoparticles enter biological environments such as the human bloodstream. As a nanomaterial spends time in the bloodstream, it develops what is called a protein corona, a shell of proteins that affects its interactions with cells and other proteins in the vicinity - possibly inducing unexpected effects. In collaboration with Korin Wheeler at Santa Clara University, we will investigate the interaction of model bloodstream proteins at the molecular level, building an understanding of the effect of the nanomaterial on its environment and vice versa.
Computational Resources: The Daly group has 3 in house workstations named for the starter Pokémon of the first generation of games (Charmander, Squirtle, and Bulbasaur), all of which contain Nvidia Quadro RTX 6000 GPUs. Data in our lab is stored on the 48 TB server Pikachu which is backed up both to our server Pichu and the cloud. We also make use of several high performance computing clusters, including those from the Mercury consortum (Skylight and Marcy) and Haverford's local Fock cluster, named after the quantum chemistry method Hartree-Fock. Our lab primarily uses python based programs for molecular dynamics simulations, quantum chemistry calculations, machine learning, and general data processing including Psi4, OpenMM, MDTraj, Tensorflow, Scikit-learn, and the scientific python suite.
If you would like to join the lab, please send me an email at cdaly2 [at] haverford.edu">cdaly2 [at] haverford.edu!