Molecular interactions have a central role in determining the chemical and physical properties of molecules and materials. Intramolecular interactions (a.k.a. chemical bonds) bind atoms together to form molecules; on the other hand, intermolecular interactions are the attractive and repulsive forces between non-bonded fragments within a molecule or in-between different molecules. Even though intermolecular interactions contribute only a tiny fraction to the total energy, accurately quantifying intermolecular interactions is essential to correctly model chemical phenomena like the structure, dynamics, and function of proteins, the binding and metabolism of drugs, the structure and relative stability of supramolecular complexes and crystal polymorphs, and the orientation and reactivity of molecules on surfaces.
Intermolecular potentials can be accurately quantified by applying high-level molecular quantum mechanics methods. Although these methods are physically sound and mathematically rigorous, their computational cost grows exponentially with the size of the system. Conversely, molecular mechanics compromises accuracy for speed, and approximates intermolecular interactions by parameterizing a classical potential energy function called a force field. Although molecular mechanics methods are applicable to systems with millions of atoms, they are unreliable for systems and physicochemical phenomena that are dissimilar to those used to parameterize the force field, which significantly limits their accurate applications.
These limitations motivated me to develop state-of-the-art machine-learning (ML) models to accurately and rapidly predict molecular interaction energies and forces. Just as human chemists learn from past experiences to make predictions about the properties of new molecules, in ML a mathematical model is trained to leverage prior experimental and/or computational results to predict the properties of new molecules. This proposal is motivated by the impressive success of recent statistical ML models for accurately predicting molecular energies and forces, but their ineluctable shortcomings for modeling intermolecular (long-range and non-local) interactions and their consequent failure to scale to larger systems. By modeling long-range interactions using quantum mechanics methods (where they are computationally affordable), I developed an ML method that can be trained using only small-to-medium-size molecules, but applied to larger molecules.
The main objectives achieved within the first half of the proposed action include: 1) developing a new methodology called Distance-Adapted version of SchNet (DASNet) which improve the existing state-of-the-art neural network architectures, 2) implementing a prototype of DASNet alongside designing the framework of the ultimate software package for predicting interaction energies based on the proposed model, 3) building the infrastructure for running quantum chemistry computations and compiling training data, 4) Releasing ChemTools software (free and open-source package which embodies a collection of interpretive tools for analyzing outputs of quantum chemistry calculations to gain chemical knowledge), 5) disseminating the action outcome by presenting at international workshops and conferences and organizing a hands-on workshop in Europe to promote Python programming language and teach ChemTools software package to a wide range of researchers.
