PhD position: Developing Machine Learned Force Fields to Predict the Stability of Molecular Crystals 

Introduction: A method that can accurately and efficiently predict the stable phase of molecular crystals will greatly aid the development of new pharmaceutical drugs [1]. If a drug is manufactured in a metastable phase that later converts to the stable phase, it can render the drug insoluble and ineffective. For instance, ritovanir, an HIV treatment, had to be recalled from the market after it began to convert to a more stable phase. The mistake cost approximately $250 million. 

There are several reasons why predicting the stable phase of a molecular crystal is challenging. The thermodynamic stability of a structure depends on the minute balance between intramolecular and intermolecular forces, in particular van der Waals interactions, Pauli repulsion, and hydrogen bonding, all of which necessitate a quantum mechanical treatment. Additionally, the free energy of the molecular crystal includes nonnegligible anharmonic vibrational effects. A state-of-the-art density functional theory (DFT) method developed in our group is the leading method for accurately predicting the stability of molecular crystals [2]. However, it is quite computationally demanding. We want to create next-generation machine learned force fields that can produce equivalently accurate results at a fraction of the cost, a game changer for drug development. 

Research: The candidate will learn how crystal structure prediction is currently done in the industry and target where machine-learning will best speed up the crystal structure prediction process. They will then develop training data sets using advanced DFT methods, and design and test machine learned force field methodologies, including kernel ridge regression and graph neural networks, that can be integrated into current crystal structure prediction workflows. 

PhD Position:  The PhD position is in the Theoretical Chemical Physics (TCP) group, led by Prof. Alexandre Tkatchenko in the Physics and Materials Science Department (DPhyMS) at the University of Luxembourg. This PhD position belongs to the PHYMOL: A Marie Skłodowska–Curie Actions Doctoral Network (MSCA DN) on Intermolecular Interactions. As such, the PhD candidate will enjoy a broad collaboration with top-notch research groups. The candidate will also be co-supervised by Dr. Marcus Neumann, founder of Avant-garde Materials Simulation Deutschland GmbH, in Freiburg, Germany, the developer of the most accurate crystal structure prediction software in the pharmaceutical industry.  

Pre-requisites: Good mathematical and programming skills, a good understanding of basic quantum mechanics, thermodynamics, and physical and chemical intuition. Research experience in computational chemistry is desirable.  

Stipend: The yearly gross salary is EUR 38.028,96 (full time)  

Interested candidates should contact: Prof. Dr. Alexandre Tkatchenko ([email protected]) 

References: 

   [1] What is Crystal Structure Prediction? And why is it so difficult?​ ​- The Cambridge Crystallographic Data Centre (CCDC) 

    [2] Johannes Hoja  and Hsin-Yu Ko  and Marcus A. Neumann  and Roberto Car  and Robert A. DiStasio  and Alexandre Tkatchenko "Reliable and practical computational description of molecular crystal polymorphs" Sci. Adv., 5, eaau3338 (2019) provides an excellent overview of the general problem and the method, DFT+MBD, that the doctoral candidate will use.