Daniel J. Kowalski

About Me

I am a PhD-qualified chemist with close to a decade of research and teaching experience and a strong interdisciplinary background. My synthetic work has ranged across the breadth of chemistry, from an internship in the pharmaceutical industry to work on academic projects in classical and organometallic coordination chemistry, nanochemistry, and abiogenesis. As a doctoral candidate, I focused on designing and validating digital tools to enhance chemical discovery, drawing from data science, machine learning, and robotics.

Alongside this, I have extensive teaching experience across all ages from elementary/primary school to undergraduate. Notably, I have spent two and a half years as a teacher of English as a foreign language across multiple schools in Japan.

Research Interests

My research interests cover the intersection of chemistry with data science, machine learning, and robotics. I aim to discover new molecules and material by developing new digital tools to enhance chemical discovery.

• Digital Discovery Strategies
  Developing machine learning workflows for the exploration and exploitation of chemical search spaces, with a focus on fostering serendipity.
• Earth-Abundant d-Block Chemistry
  Exploring the structures, reactivity, and properties of systems built around first row transition metals.
• Self-Driving Laboratories
  Assembling systems that integrate robotics and machine learning for closed-loop material discovery, and investigating the interactions between human and AI agents.
• Deconvolution of Chemical Mixtures
  Applying machine learning to interpret analytical data and visualise meaningful relationships within mixtures with high compositional diversity.
• Digitisation of Chemical Synthesis
  Creating interpretable and generalisable representations of synthetic data for machine learning.

Data Portfolio

This portfolio is intended to demonstrate my skills across a variety of data science and machine learning techniques. Each project is a toy problem, most drawn from chemistry. They are intended to be relevant to current research, but are limited by my personal resources. These projects are not intended to be taken as novel academic research. Code is written in python3 and no AI-generated code has been included.

Links are provided to: (i) a brief non-technical explanation of each project, and (ii) the full GitHub repo for each project.

1 | Optimising a Neural Network for Solubility Prediction

This project uses bayesian optimisation to tune the hyperparameters for a recurrent long short-term memory (LSTM) network.

• Read More
• GitHub

2 | Building a Bot to play Viking Chess using Reinforcement Learning

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• Read More
• GitHub

3 | Generating Bird Pictures using a Denoising Diffision Model

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• Read More
• GitHub

Technical Publications

Automated Library Generation and Serendipity Quantification Enables Diverse Discovery in Coordination Chemistry
Kowalski, MacGregor, Long, Bell, Cronin
J. Am. Chem. Soc. 2023, 145(4), pp.2332

• Journal
• GitHub

An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge
Porwol, Kowalski, Henson, Long, Bell, Cronin
Angew. Chem. Int. Ed. 2020, 59(28), pp.11256

• Journal

Wikipedia Projects

Contributing to Wikipedia enables me to help promote accurate, well-referenced scientific knowledge and make it freely accessible to all, while also reinforcing my own understanding. Below are a list of my contributions to Wikipedia, including links to each page.