Abstract: In this talk, we will discuss an intelligent agent system that integrates multiple large language models for autonomous design, planning, and execution of scientific experiments. We will demonstrate the Agent's scientific research abilities using several examples, with the most complex one involving the successful execution of catalyzed cross-coupling reactions. Lastly, we address...
Abstract: Machine learning models are very successful in predicting various chemical properties. Graph-convolutional neural networks (GCNNs) are routinely used for the prediction of molecular properties, but their application to chemical reactions is largely unexplored. GCNNs allow for a learned extraction of important characteristics of a molecule and enable end-to-end learning, instead of...
Abstract:
Machine learning force fields (MLFFs) are set to become an indispensable tool in computational catalysis. In this talk, we provide a detailed walkthrough on how to train an MLFF to accurately predict energy barriers for catalytic reaction pathways. We demonstrate the capabilities of the resulting interatomic potential that offers near ab-initio accuracy at a fraction of the cost....
Abstract:
The atomistic structure determines the stability and properties of a material and its potential use in applications. We develop software tools such as Ingrained and FANTASTX (Fully Automated Nanoscale To Atomistic Structure from Theory and eXperiments) to find the atomistic structure from experimental data. Ingrained software can construct a grain boundary structure or a surface...
Abstract:
With the recent exponential growth in publication rates, it has become impossible for a scientist to keep up with all publications related to a specific topic. Although there are notable efforts to automate text parsing from literature, there are many instances where important information is communicated through images or tables in papers.1 In this talk, I will present the latest...
