{"id":868,"date":"2026-05-14T13:23:35","date_gmt":"2026-05-14T13:23:35","guid":{"rendered":"https:\/\/sites.rutgers.edu\/sun-lab\/?page_id=868"},"modified":"2026-05-16T12:15:45","modified_gmt":"2026-05-16T12:15:45","slug":"teach-chem542","status":"publish","type":"page","link":"https:\/\/sites.rutgers.edu\/sun-lab\/teach-chem542\/","title":{"rendered":"CHEM 487\/542 – Chemical Data Science"},"content":{"rendered":"

This course explores advanced topics in data science and artificial intelligence (AI) with a focus on chemistry applications. It covers the fundamentals of machine learning and data science, along with advanced research areas such as ML-guided experimental design, high-throughput screening, and AI-driven chemical discovery. Through a combination of lectures and hands-on lab sessions, students will gain both the theoretical background and practical skills needed to pursue research in chemical data science.<\/p>\n

Evaluation:<\/strong><\/h5>\n

–<\/span> Class participation (20%): <\/span>Includes attending lectures and hands-on sessions and actively asking questions.<\/p>\n

–<\/span> Hands-on assignments (50%): <\/span>Hands-on assignments include coding and computational tasks, along with analysis of the results.<\/p>\n

–<\/span> Capstone project (30%): <\/span>A short research-related project that will be summarized as a project report and a 20-minute talk.<\/p>\n

Schedule and Course Materials<\/strong><\/h5>\n

Lecture notes and lab codes for the first 17 lectures were created by Dr. Chong Sun. The English was refined with assistance from ChatGPT. More lectures will be added, stay tuned!<\/p>\n

All rights reserved. If you use any part of this content, please provide proper acknowledgment of the source. If you enjoy the course, please give a star to the Course Github Repo<\/a>!<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
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#<\/strong> <\/span><\/p>\n<\/td>\n

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Date <\/span><\/strong><\/p>\n<\/td>\n

\n

Lecture\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/strong><\/span><\/p>\n<\/td>\n

\n

Lab<\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1.\u00a0<\/span><\/p>\n<\/td>\n

\n

Jan 20\u00a0 \u00a0 \u00a0<\/span><\/p>\n<\/td>\n

\n

Introduction: Data science for Chemistry <\/span><\/a><\/p>\n<\/td>\n

\n

1. Python Basics<\/a><\/span><\/p>\n

2. UV-Vis Spectrum<\/a><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

2 <\/span><\/p>\n<\/td>\n

\n

Jan 22 <\/span><\/p>\n<\/td>\n

\n

Chemical data: Representation<\/a> <\/span><\/p>\n<\/td>\n

\n

1. 1D string and 3D XYZ<\/a><\/span><\/p>\n

2. Graph representation.<\/a><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

3 <\/span><\/p>\n<\/td>\n

\n

Jan 27 <\/span><\/p>\n<\/td>\n

\n

Chemical data: Acquisition and Feature Engineering<\/span><\/a><\/p>\n<\/td>\n

\n

1. API Access<\/a><\/span><\/p>\n

2. Chemical Similarity<\/a><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

4 <\/span><\/p>\n<\/td>\n

\n

Jan 29 <\/span><\/p>\n<\/td>\n

\n

Fundamentals of Statistical Analysis<\/a> <\/span><\/p>\n<\/td>\n

\n

Maximum Likelihood Estimation<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

5 <\/span><\/p>\n<\/td>\n

\n

Feb 3 <\/span><\/p>\n<\/td>\n

\n

Statistical Methods for Chemical Prediction<\/a> <\/span><\/p>\n<\/td>\n

\n

1. Solubility prediction with linear<\/a><\/span><\/p>\n

models<\/span><\/a><\/p>\n

2. Boiling point prediction with<\/a><\/span><\/p>\n

Gaussian process<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

6 <\/span><\/p>\n<\/td>\n

\n

Feb 5 <\/span><\/p>\n<\/td>\n

\n

Dimension reduction and Preliminaries on<\/span><\/a><\/p>\n

Training ML Models<\/span><\/a><\/p>\n<\/td>\n

\n

Feature selection and PCA<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

7 <\/span><\/p>\n<\/td>\n

\n

Feb 10 <\/span><\/p>\n<\/td>\n

\n

Feedforward Neural Networks<\/a> <\/span><\/p>\n<\/td>\n

\n

1. Build an FNN with PyTorch<\/span><\/a><\/p>\n

2. Use GPU backend<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

8 <\/span><\/p>\n<\/td>\n

\n

Feb 12 <\/span><\/p>\n<\/td>\n

\n

Chemical Feature Engineering II<\/a> <\/span><\/p>\n<\/td>\n

\n

1. Ultrafast shape recognition<\/span><\/a><\/p>\n

2. Simple learned embedding for<\/span><\/a><\/p>\n

molecules<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

9 <\/span><\/p>\n<\/td>\n

\n

Feb 17 <\/span><\/p>\n<\/td>\n

\n

Case Study: Learning a potential energy surface (PES)<\/span><\/a><\/p>\n<\/td>\n

\n

1. MLP with ACSF and FNN<\/span><\/a><\/p>\n

2. MLP with SOAP and GPR<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

10 <\/span><\/p>\n<\/td>\n

\n

Feb 19 <\/span><\/p>\n<\/td>\n

\n

Chemical Prediction with Graph neural networks (GNN)<\/span><\/a><\/p>\n<\/td>\n

1. Molecular property prediction with<\/span><\/a><\/p>\n

GNN<\/span><\/a><\/p>\n

2. Create a Graph Dataset<\/a><\/td>\n<\/tr>\n

\n

11 <\/span><\/p>\n<\/td>\n

\n

Feb 24 <\/span><\/p>\n<\/td>\n

\n

Active Learning for Chemical Data Efficiency<\/a> <\/span><\/p>\n<\/td>\n

\n

1.<\/a> Improve GPR with active<\/a><\/span><\/p>\n

learning<\/span><\/a><\/p>\n

2. Im<\/a>prove FNN with MC dropout<\/a><\/span><\/p>\n

and max-min sampling<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

12 <\/span><\/p>\n<\/td>\n

\n

Feb 26 <\/span><\/p>\n<\/td>\n

\n

Chemical Design with Bayesian optimization<\/a> <\/span><\/p>\n<\/td>\n

\n

BH Reaction optimized by BO<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

13 <\/span><\/p>\n<\/td>\n

\n

Mar 3 <\/span><\/p>\n<\/td>\n

\n

Reaction kinetics modeling with recurrent neural networks (RNN)<\/span><\/a><\/p>\n

\n<\/td>\n

\n

1. Reaction Kinetics with RNN<\/span><\/a><\/p>\n

2. Molecular Generation with RNN<\/span><\/a><\/p>\n

(LSTM)<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

14 <\/span><\/p>\n<\/td>\n

\n

Mar 5 <\/span><\/p>\n<\/td>\n

\n

Retrosynthesis with Sequence-to-Sequence Modeling<\/span><\/a><\/p>\n<\/td>\n

\n

Retrosynthesis with Seq2Seq<\/span><\/a><\/p>\n

model<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

15 <\/span><\/p>\n<\/td>\n

\n

Mar 10 <\/span><\/p>\n<\/td>\n

\n

Generative Chemistry and Variational Autoencoder<\/span><\/a><\/p>\n<\/td>\n

\n

Learning the Chemical Latent<\/span><\/a><\/p>\n

Space with VAE<\/span><\/a><\/p>\n<\/td>\n<\/tr>\n

\n

16 <\/span><\/p>\n<\/td>\n

\n

Mar 12 <\/span><\/p>\n<\/td>\n

\n

Molecular Docking with Diffusion Models<\/a> <\/span><\/p>\n<\/td>\n

\n

1. Processing a PDB file<\/a><\/span><\/p>\n

2. A simple implementation of Diffusion Models<\/a><\/span><\/p>\n<\/td>\n<\/tr>\n

\n

17 <\/span><\/p>\n<\/td>\n

\n

Mar 24 <\/span><\/p>\n<\/td>\n

\n

Crystal Structure Design with Transformer<\/span><\/a><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

18 <\/span><\/p>\n<\/td>\n

\n

Mar 26 <\/span><\/p>\n<\/td>\n

\n

Equivariance Neural Networks<\/span><\/p>\n<\/td>\n

Laurence Giordano<\/td>\n<\/tr>\n
\n

19 <\/span><\/p>\n<\/td>\n

\n

Mar 31 <\/span><\/p>\n<\/td>\n

\n

Self-Driving Lab and Agentic AI<\/span><\/p>\n<\/td>\n

Self-reading<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
\n

21 <\/span><\/p>\n<\/td>\n

\n

Apr 7 <\/span><\/p>\n<\/td>\n

\n

Help session with terminal, WSL, VSCode,<\/span><\/p>\n

Git\/Github<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

22 <\/span><\/p>\n<\/td>\n

\n

Apr 9 <\/span><\/p>\n<\/td>\n

\n

Guest lecture (Prof. York)<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

23 <\/span><\/p>\n<\/td>\n

\n

Apr 16<\/span><\/p>\n<\/td>\n

\n

Guest lecture (Prof. Khare)<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

24\u00a0<\/span><\/p>\n<\/td>\n

\n

Apr 23<\/span><\/p>\n<\/td>\n

\n

Guest lecture (Prof. Remsing)<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

This course explores advanced topics in data science and artificial intelligence (AI) with a focus on chemistry applications. 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