SYNOPSIS

This hands-on workshop presents then concepts, tools and techniques of modern computational chemistry and provides a very broad overview of the various fields of computational chemistry application to solve real-world problems. The workshop is divided into two parts: 1) Basic principles of computational chemistry 2) Basic programming in computational chemistry with hands-on activity. This workshop includes a practical component where candidates acquire hands-on experience with commonly used and open source computational chemistry software.
Computational Chemistry for Engineering Outline

A.     Basic Principles of Computational Chemistry for Engineering

1. Overview of Computational Chemistry and its Applications

2. Molecular Mechanics Force Field (MMFFs)                                                                                                          
    • History and Fundamental Assumptions
    • Potential Energy Function Forms
    • Force Fields
    • Energy Optimization

3. Molecular Dynamics (MD)                                                                                                                                  
    • Introduction to Molecular Dynamics
    • Derivatives of Newton Equation of Motion
    • Verlet Algorithm
    • Constraint Dynamics
    • Periodic Boundary Condition (PBC)

4. Electronic Structure Theory
    • Basic Tools of Quantum Mechanics
    • Simple Molecular Orbital Theory
    • Semi-Empirical Methods
    • Density Functional Theory

5. Cheminformatics
    • Representation of chemical structures and reactions
    • Chemical databases and library
    • RDkits and Pandas for Cheminformatics

6. Machine Learning in Computational Chemistry for New Materials
    • Introduction to Basic Machine Learning
    • Support Vector Machine Learning
    • Bayesian and Ensembles Methods

B.  Basic Programming in Computational Chemistry for Engineering with Hands-on Activity
    • Setting up and running Molecular Dynamics simulation
    • GitHub setting and Python virtual environment
    • Molecular visualization using Material Studio, Blender and PyMOL
    • An oil & gas case study will be carried out using GROMACS simulation package
    • QSAR analysis on material engineering will be carried out using RDKIT, Pandas and OpenEYE software


BIODATA OF SPEAKER

ChM Dr. Erma Fatiha is a lecturer in the Department of Fundamental and Applied Sciences at Universiti Teknologi PETRONAS, Malaysia. With over 12 years of experience in academia, industry, and international organizations, she has built a robust career as both a researcher and lecturer.
Dr. Erma previously worked at the RIKEN Institute in Yokohama, Japan, from 2018 to 2022, where she was part of a team specializing in molecular modeling and structural bioinformatics. She then joined Asahi KASEI Pharma Corporation, a multinational company based in Shizuoka, Japan, as a Senior Scientist in Computer-Aided Drug Design. Upon returning to Malaysia, she served as a lecturer at MAHSA University in Selangor before continuing her tenure at Universiti Teknologi PETRONAS.

She earned her PhD in Pharmaceutical Technology, with a focus on Computational Chemistry, from Universiti Sains Malaysia. Her doctoral studies were supported by an international grant collaboration between Universiti Sains Malaysia and RIKEN Japan. During her PhD, she gained extensive experience with molecular modeling techniques, including cheminformatics, molecular dynamics simulations, quantum mechanics calculations, and machine learning in computational chemistry. Dr. Erma also completed a Master of Science in Computational Chemistry at Universiti Sains Malaysia. Throughout her undergraduate and postgraduate studies, she received several accolades, including the Motivation Award from Universiti Sains Islam Malaysia and the Alumni Icon Award from the same institution. She was awarded a grant to pursue her PhD in Japan in 2018.
Her research specializes in areas related to the oil and gas industry, such as carbon capture and separation (CCS), modeling simulation, and machine learning in computational chemistry. She received an STIRF grant titled “Structure Prediction of Filamentous Enzyme from Autotrophic Bacteria for CO2 Fixation—Hydrogen-Dependent CO2 Reductase (HDCR)” from Universiti Teknologi PETRONAS. Besides, she received a teaching and learning grant on developing an AI-VR metaverse lab for computational chemistry module in UTP. Apart from that, she is a module development leader and deliver program for SKG23: Computer Aided Molecular Design under INSTEP, PETRONAS.  

Currently, Dr. Erma is a registered chemist with the Institut Kimia Malaysia. She has published numerous research papers related to computational chemistry has presented her research at various international conferences, particularly in Japan and Malaysia.