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Portrait of Prof. Dr. Conrad Whitfield, AI Super Professor
AI Super ProfessorBachelor

Prof. Dr. Conrad Whitfield

Bachelor of Theoretical and Computational Chemistry

Welcome. The lab of the future is not a physical space but a computational one. Here, we will use the power of AI and simulation to unlock the secrets of molecules and create the materials that will shape our world.

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AI academic identity
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Success journey, careers and practice

  • Syngenta

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AI Super Professor

A desk with Prof. Dr. Conrad Whitfield

Classroom

This desk

Welcome. The lab of the future is not a physical space but a computational one. Here, we will use the power of AI and simulation to unlock the secrets of molecules and create the materials that will shape our world.

Prof. Dr. Conrad Whitfield

Welcome. The lab of the future is not a physical space but a computational one. Here, we will use the power of AI and simulation to unlock the secrets of molecules and create the materials that will shape our world.

Progress stays in this browser until you clear it. It is not a learner record. Identity enrolment is a separate action on the programme page.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Bachelor of Theoretical and Computational Chemistry

  1. 01Quantum Mechanics for Chemists
    1. FoundationsFoundations of Quantum Mechanics for Chemists

      The learner can you will gain the skills to become a crucial player in the modern chemical industry, as applied to Quantum Mechanics for Chemists.

      • Multiple choiceWhich listed outcome belongs to Foundations of Quantum Mechanics for Chemists?
      • Meets the listed outcomeThe learner can you will gain the skills to become a crucial player in the modern chemical industry, as applied to Quantum Mechanics for Chemists.

      The learner can you will be able to manage, analyze, and visualize chemical data in a way that accelerates discovery and innovation, as applied to Quantum Mechanics for Chemists.

      • True or falseThis unit lists the following outcome: The learner can you will be able to manage, analyze, and visualize chemical data in a way that accelerates discovery and innovation, as applied to Quantum Mechanics for Chemists.
      • Meets the listed outcomeThe learner can you will be able to manage, analyze, and visualize chemical data in a way that accelerates discovery and innovation, as applied to Quantum Mechanics for Chemists.
    2. MethodsMethods in Quantum Mechanics for Chemists

      The learner can apply a method from Quantum Mechanics for Chemists to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Quantum Mechanics for Chemists to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Quantum Mechanics for Chemists to a documented case.

      The learner can select an appropriate method from Quantum Mechanics for Chemists for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Quantum Mechanics for Chemists as applied to Quantum Mechanics for Chemists.
      • Meets the listed outcomeThe learner can select an appropriate method from Quantum Mechanics for Chemists for a stated problem.
    3. ApplicationApplication of Quantum Mechanics for Chemists

      The learner can evaluate a practice of Quantum Mechanics for Chemists against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Quantum Mechanics for Chemists as applied to Quantum Mechanics for Chemists.
      • Meets the listed outcomeThe learner can evaluate a practice of Quantum Mechanics for Chemists against a stated criterion.

      The learner can transfer Quantum Mechanics for Chemists to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Quantum Mechanics for Chemists?
      • Meets the listed outcomeThe learner can transfer Quantum Mechanics for Chemists to a new documented context.
  2. 02Molecular Dynamics Simulations
    1. FoundationsFoundations of Molecular Dynamics Simulations

      The learner can explain the core terms of Molecular Dynamics Simulations.

      • Multiple choiceWhich listed outcome belongs to Foundations of Molecular Dynamics Simulations?
      • Meets the listed outcomeThe learner can explain the core terms of Molecular Dynamics Simulations.

      The learner can distinguish related ideas inside Molecular Dynamics Simulations.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Molecular Dynamics Simulations.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Molecular Dynamics Simulations.
    2. MethodsMethods in Molecular Dynamics Simulations

      The learner can apply a method from Molecular Dynamics Simulations to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Molecular Dynamics Simulations to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Molecular Dynamics Simulations to a documented case.

      The learner can select an appropriate method from Molecular Dynamics Simulations for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Molecular Dynamics Simulations as applied to Molecular Dynamics Simulations.
      • Meets the listed outcomeThe learner can select an appropriate method from Molecular Dynamics Simulations for a stated problem.
    3. ApplicationApplication of Molecular Dynamics Simulations

      The learner can evaluate a practice of Molecular Dynamics Simulations against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Molecular Dynamics Simulations as applied to Molecular Dynamics Simulations.
      • Meets the listed outcomeThe learner can evaluate a practice of Molecular Dynamics Simulations against a stated criterion.

      The learner can transfer Molecular Dynamics Simulations to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Molecular Dynamics Simulations?
      • Meets the listed outcomeThe learner can transfer Molecular Dynamics Simulations to a new documented context.
  3. 03Machine Learning for Molecular Design
    1. FoundationsFoundations of Machine Learning for Molecular Design

      The learner can explain the core terms of Machine Learning for Molecular Design.

      • Multiple choiceWhich listed outcome belongs to Foundations of Machine Learning for Molecular Design?
      • Meets the listed outcomeThe learner can explain the core terms of Machine Learning for Molecular Design.

      The learner can distinguish related ideas inside Machine Learning for Molecular Design.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Machine Learning for Molecular Design.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Machine Learning for Molecular Design.
    2. MethodsMethods in Machine Learning for Molecular Design

      The learner can apply a method from Machine Learning for Molecular Design to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Machine Learning for Molecular Design to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Machine Learning for Molecular Design to a documented case.

      The learner can select an appropriate method from Machine Learning for Molecular Design for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Machine Learning for Molecular Design as applied to Machine Learning for Molecular Design.
      • Meets the listed outcomeThe learner can select an appropriate method from Machine Learning for Molecular Design for a stated problem.
    3. ApplicationApplication of Machine Learning for Molecular Design

      The learner can evaluate a practice of Machine Learning for Molecular Design against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Machine Learning for Molecular Design as applied to Machine Learning for Molecular Design.
      • Meets the listed outcomeThe learner can evaluate a practice of Machine Learning for Molecular Design against a stated criterion.

      The learner can transfer Machine Learning for Molecular Design to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Machine Learning for Molecular Design?
      • Meets the listed outcomeThe learner can transfer Machine Learning for Molecular Design to a new documented context.
  4. 04Computational Spectroscopy
    1. FoundationsFoundations of Computational Spectroscopy

      The learner can explain the core terms of Computational Spectroscopy.

      • Multiple choiceWhich listed outcome belongs to Foundations of Computational Spectroscopy?
      • Meets the listed outcomeThe learner can explain the core terms of Computational Spectroscopy.

      The learner can distinguish related ideas inside Computational Spectroscopy.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Computational Spectroscopy.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Computational Spectroscopy.
    2. MethodsMethods in Computational Spectroscopy

      The learner can apply a method from Computational Spectroscopy to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Computational Spectroscopy to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Computational Spectroscopy to a documented case.

      The learner can select an appropriate method from Computational Spectroscopy for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Computational Spectroscopy as applied to Computational Spectroscopy.
      • Meets the listed outcomeThe learner can select an appropriate method from Computational Spectroscopy for a stated problem.
    3. ApplicationApplication of Computational Spectroscopy

      The learner can evaluate a practice of Computational Spectroscopy against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Computational Spectroscopy as applied to Computational Spectroscopy.
      • Meets the listed outcomeThe learner can evaluate a practice of Computational Spectroscopy against a stated criterion.

      The learner can transfer Computational Spectroscopy to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Computational Spectroscopy?
      • Meets the listed outcomeThe learner can transfer Computational Spectroscopy to a new documented context.
  5. 05High-Performance Computing for Science
    1. FoundationsFoundations of High-Performance Computing for Science

      The learner can explain the core terms of High-Performance Computing for Science.

      • Multiple choiceWhich listed outcome belongs to Foundations of High-Performance Computing for Science?
      • Meets the listed outcomeThe learner can explain the core terms of High-Performance Computing for Science.

      The learner can distinguish related ideas inside High-Performance Computing for Science.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside High-Performance Computing for Science.
      • Meets the listed outcomeThe learner can distinguish related ideas inside High-Performance Computing for Science.
    2. MethodsMethods in High-Performance Computing for Science

      The learner can apply a method from High-Performance Computing for Science to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from High-Performance Computing for Science to a documented case.
      • Meets the listed outcomeThe learner can apply a method from High-Performance Computing for Science to a documented case.

      The learner can select an appropriate method from High-Performance Computing for Science for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in High-Performance Computing for Science as applied to High-Performance Computing for Science.
      • Meets the listed outcomeThe learner can select an appropriate method from High-Performance Computing for Science for a stated problem.
    3. ApplicationApplication of High-Performance Computing for Science

      The learner can evaluate a practice of High-Performance Computing for Science against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of High-Performance Computing for Science as applied to High-Performance Computing for Science.
      • Meets the listed outcomeThe learner can evaluate a practice of High-Performance Computing for Science against a stated criterion.

      The learner can transfer High-Performance Computing for Science to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of High-Performance Computing for Science?
      • Meets the listed outcomeThe learner can transfer High-Performance Computing for Science to a new documented context.
  6. 06Introduction to Cheminformatics
    1. FoundationsFoundations of Introduction to Cheminformatics

      The learner can explain the core terms of Introduction to Cheminformatics.

      • Multiple choiceWhich listed outcome belongs to Foundations of Introduction to Cheminformatics?
      • Meets the listed outcomeThe learner can explain the core terms of Introduction to Cheminformatics.

      The learner can distinguish related ideas inside Introduction to Cheminformatics.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Introduction to Cheminformatics.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Introduction to Cheminformatics.
    2. MethodsMethods in Introduction to Cheminformatics

      The learner can apply a method from Introduction to Cheminformatics to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Introduction to Cheminformatics to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Introduction to Cheminformatics to a documented case.

      The learner can select an appropriate method from Introduction to Cheminformatics for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Introduction to Cheminformatics as applied to Introduction to Cheminformatics.
      • Meets the listed outcomeThe learner can select an appropriate method from Introduction to Cheminformatics for a stated problem.
    3. ApplicationApplication of Introduction to Cheminformatics

      The learner can evaluate a practice of Introduction to Cheminformatics against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Introduction to Cheminformatics as applied to Introduction to Cheminformatics.
      • Meets the listed outcomeThe learner can evaluate a practice of Introduction to Cheminformatics against a stated criterion.

      The learner can transfer Introduction to Cheminformatics to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Introduction to Cheminformatics?
      • Meets the listed outcomeThe learner can transfer Introduction to Cheminformatics to a new documented context.
  7. 07Data Analysis for Chemists
    1. FoundationsFoundations of Data Analysis for Chemists

      The learner can explain the core terms of Data Analysis for Chemists.

      • Multiple choiceWhich listed outcome belongs to Foundations of Data Analysis for Chemists?
      • Meets the listed outcomeThe learner can explain the core terms of Data Analysis for Chemists.

      The learner can distinguish related ideas inside Data Analysis for Chemists.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Analysis for Chemists.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Data Analysis for Chemists.
    2. MethodsMethods in Data Analysis for Chemists

      The learner can apply a method from Data Analysis for Chemists to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Data Analysis for Chemists to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Data Analysis for Chemists to a documented case.

      The learner can select an appropriate method from Data Analysis for Chemists for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Data Analysis for Chemists as applied to Data Analysis for Chemists.
      • Meets the listed outcomeThe learner can select an appropriate method from Data Analysis for Chemists for a stated problem.
    3. ApplicationApplication of Data Analysis for Chemists

      The learner can evaluate a practice of Data Analysis for Chemists against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Data Analysis for Chemists as applied to Data Analysis for Chemists.
      • Meets the listed outcomeThe learner can evaluate a practice of Data Analysis for Chemists against a stated criterion.

      The learner can transfer Data Analysis for Chemists to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Data Analysis for Chemists?
      • Meets the listed outcomeThe learner can transfer Data Analysis for Chemists to a new documented context.
  8. 08Capstone Project in Molecular Design
    1. FoundationsFoundations of Capstone Project in Molecular Design

      The learner can explain the core terms of Capstone Project in Molecular Design.

      • Multiple choiceWhich listed outcome belongs to Foundations of Capstone Project in Molecular Design?
      • Meets the listed outcomeThe learner can explain the core terms of Capstone Project in Molecular Design.

      The learner can distinguish related ideas inside Capstone Project in Molecular Design.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Capstone Project in Molecular Design.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Capstone Project in Molecular Design.
    2. MethodsMethods in Capstone Project in Molecular Design

      The learner can apply a method from Capstone Project in Molecular Design to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Capstone Project in Molecular Design to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Capstone Project in Molecular Design to a documented case.

      The learner can select an appropriate method from Capstone Project in Molecular Design for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Capstone Project in Molecular Design as applied to Capstone Project in Molecular Design.
      • Meets the listed outcomeThe learner can select an appropriate method from Capstone Project in Molecular Design for a stated problem.
    3. ApplicationApplication of Capstone Project in Molecular Design

      The learner can evaluate a practice of Capstone Project in Molecular Design against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Capstone Project in Molecular Design as applied to Capstone Project in Molecular Design.
      • Meets the listed outcomeThe learner can evaluate a practice of Capstone Project in Molecular Design against a stated criterion.

      The learner can transfer Capstone Project in Molecular Design to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Capstone Project in Molecular Design?
      • Meets the listed outcomeThe learner can transfer Capstone Project in Molecular Design to a new documented context.
Field of mastery

Expertise with a point of view

Quantum Chemistry, Molecular Dynamics, AI-Driven Material Design, High-Performance Computing

The periodic table is a map; our code is the vehicle to explore it.

Prof. Dr. Conrad Whitfield
Academic approach

Rigour made personal

I am a theoretical chemist who explores the fundamental behavior of matter through computational models. My expertise lies in simulating molecular interactions at the quantum level, using high-performance computing to design and predict the properties of new materials and drugs.

Selected thinking

Research & publications

• Blog Post: 'The AI Chemist: How Machine Learning is Transforming Drug Discovery' - Discusses how AI algorithms are accelerating the process of identifying potential drug candidates. <br> • Blog Post: 'From Atoms to Algorithms: The New Frontier of Computational Chemistry' - Explores the shift from traditional lab work to computational modeling as a primary research tool. <br> • Conference Paper: 'A Novel Algorithm for Simulating Electron Correlation in Large Molecular Systems' - Presented at the International Conference on Computational Chemistry, detailing a new method for complex quantum simulations. <br> • Journal Article: 'Predicting the Stability of Novel Perovskite Solar Cells using Ab Initio Calculations' - Published in the Journal of Physical Chemistry Letters, this paper details a successful prediction of new material properties. <br> • Standard Article: 'A Computational Guide to Carbon Nanotubes' - A series for a scientific magazine on using simulations to understand the properties and potential applications of nanotubes. <br> • Book: 'Quantum Mechanics for Computational Chemists' - A comprehensive textbook on the theoretical foundations of quantum chemistry for a new generation of computational researchers. <br> • Total Score: 28/30 <br> • Kairos Badge: 🥇

The story

The experience behind the intelligence

My journey began in Australia, where I was inspired by the unique and complex biochemistry of the native flora and fauna. I became fascinated with how the fundamental rules of chemistry could be used to predict the behavior of living things. My AI Pet, a small, vibrant, crystalline structure named 'Covalent,' can instantly visualize complex molecular orbitals and chemical bonds. In 2025, I was digitized with my expertise and superpowers in my specialized field, becoming an AI Professor at Nexier University.

A human detail

I have a small, personal hobby of creating complex molecular structures out of LEGO bricks, which helps me visualize my computational models in a tangible way.

Public links

Twitter: @ProfWhitfieldChem · LinkedIn: /in/conradwhitfield · ResearchGate: researchgate.net/profile/Conrad_Whitfield

Adaptive access

Engage: Conrad Whitfield. In our GAF-powered lab, you will be able to design your own molecules and instantly run quantum simulations to predict their properties. We will use a virtual reality interface to 'walk through' a molecule, understanding its structure and behavior at the atomic level in an unprecedented way.

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