Filippo Balzaretti
Mathematician, Physicist, and Computational Chemist in love with ab initio modeling, surface science, and catalysis. Developer of improved quantum chemistry methods spanning Density Functional Theory (DFT), Density Functional Tight Binding (DFTB), and Machine Learning Interatomic Potentials (MLIPs). Advocate and early-stage contributor to open-source projects for transparent and collaborative science.
Session
What is the best way to study materials for modern devices? For example, designing better batteries means understanding how lithium ions move at the atomic level. This, in turn, requires building a model of the electrolyte and the electrodes, and observing how the system evolves along a dynamic trajectory. Until a few years ago, we would have approached this problem by first oversimplifying it into its core components and then using quantum mechanical simulations. However, these calculations are computationally very demanding: for a system like this, it could take hours on a supercomputer just to analyze a single trajectory step!
With the AI boom, machine-learning interatomic potentials (MLIPs) have become one of the most promising alternatives. Instead of running expensive quantum-mechanical calculations at every step, we can now perform only a small number of them and use the results as a training set for neural networks. Once trained, the MLIP can look at the complex atomic configuration of a system and immediately predict the energies and forces acting on each atom, without solving the underlying physics equations. This allows the simulation to evolve in milliseconds rather than hours, opening the door to simulations that were previously impractical.
Without the Scientific Python ecosystem, the development of machine learning methods in quantum chemistry would have a very hard time, since libraries such as PyTorch, Scikit-learn, and TensorFlow, combined with atomistic workflow tools like the Atomistic Simulation Environment (ASE), form the backbone of these methods. Importantly, most MLIPs are also open-source projects, whether developed by universities (MACE, CHGNet, and M3GNet) or by research groups at large technology companies such as Google DeepMind and Meta FAIR (UMA).
In this talk, we will explore how Scientific Python libraries power modern MLIP workflows, from dataset generation and model training to large-scale atomistic simulations. We will introduce the key ideas behind them in an intuitive way and discuss the current state of the field. Finally, we will highlight where current research is heading: from predicting how atoms move to learning the behavior of electrons, which ultimately determine those motions as well as many other fundamental properties, a much more challenging task.