AI4ChemMat Hands-On Series

Contribution List

2. End-to-end AI Framework for Interpretable Prediction of Molecular and Crystal Properties

Hyun Park

8/16/23, 10:30 AM

Presentation

Abstract: In this talk, I will target audience who are interested in understanding the basics of how to train, perform hyperparameter search and infer an ML model for molecular structures such as small molecules or MOFs. Moreover, with a bit of Python programming knowledge, my AI framework can help users who want to learn how to visualize learned molecular representations and highlight atoms...

3. Emergent autonomous scientific research capabilities of large language models

Daniil Boiko (Carnegie Mellon University)

8/30/23, 10:30 AM

Presentation

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...

4. Deep learning of reaction properties via graph-convolutional neural nets

Dr Esther Heid ( Technical University of Vienna)

9/6/23, 10:30 AM

Presentation

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...

7. Machine Learning Force Fields for Heterogeneous Catalysis

Lars Leon Schaaf (University of Cambridge)

10/18/23, 10:30 AM

Presentation

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....

9. Structure determination of nanoscale materials using theory and experimental characterization data

Joshua Paul (Argonne National Laboratory), Venkata Surya Chaitanya Kolluru (ANL)

11/8/23, 10:30 AM

Presentation

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...

8. Extracting and utilizing multimodal datasets of images and text with large language models

Aikaterini Vriza (ANL)

11/15/23, 10:30 AM

Presentation

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...