ProgramMaster Logo
Conference Tools for MS&T22: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T22: Materials Science & Technology
Symposium Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title Computational and Machine Learning Studies of DNA-templated Dye Aggregate Design
Author(s) Lan Li
On-Site Speaker (Planned) Lan Li
Abstract Scope Organic molecules, known as dyes, which can absorb and emit light, have various potential applications, such as biomedical imaging, organic photovoltaics, non-linear optics, and quantum information systems. These applications are controlled by dye properties, including extinction coefficient, transition dipole moment, and aggregation ability. Dye aggregate networks via deoxyribonucleic acid (DNA) templating exhibit exciton delocalization, energy transport, and fluorescence emission. DNA nanotechnology provides scaffolding upon which dyes attach in an aqueous environment. In order to control the process and optimize the properties, we have combined machine learning and multiscale modeling to identify ideal dye candidates and reveal their dye aggregate-DNA interactions and the dye orientations. We found that those structural features have a strong impact on the resultant performance of the DNA-templated dye aggregates. The computational results were also validated with experimental observations.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Addressing Data Needs for High Temperature Material Processing with Natural Language Processing
AI Driven Microscopic Analysis to Predict the Local Structure in Zirconia Ceramics
AI/ML-Driven Multi-Scale Modeling and Design of Structural Materials
B-5: Using Computer Vision and Machine Learning to Characterize Melt Pool Geometry in Additive Manufacturing
Comparison of Data Driven and Physics-informed Machine Learning Models for Temperature Prediction of Shear Assisted Processing and Extrusion
Composition and Property Prediction of Polymer-derived Silicon Oxycarbides
Computational and Machine Learning Studies of DNA-templated Dye Aggregate Design
Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys
Graph Neural Network Modeling of Deforming Polycrystals
High-throughput Machine Learning Experiments with Graph Neural Networks for Predicting Abnormal Grain Growth in Polycrystalline Materials
Large Scale Atomistic Simulation of the B1-B4 Phase Transition of GaN with the Machine Learning Potential
Machine Learning Based Prediction of Cation Distribution in Complex Spinel Oxides as a Function of Processing Temperature
Machine Learning for Joint Quality Performance-determining Relationship between Intermetallic Properties and weld Microstructure of Al/steel Resistance Spot Welds
Microstructure Characterization and Reconstruction by Deep Learning Methodology
Unraveling the Process Fundamentals of Additive Friction Stir Deposition by Integrating Physics Simulation with Data-driven Approaches

Questions about ProgramMaster? Contact programming@programmaster.org