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UID:pretalx-scipy-2026-BQDMZH@pretalx.com
DTSTART;TZID=CST:20260717T131500
DTEND;TZID=CST:20260717T134500
DESCRIPTION: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 t
 he electrolyte and the electrodes\, and observing how the system evolves a
 long a dynamic trajectory. Until a few years ago\, we would have approache
 d this problem by first oversimplifying it into its core components and th
 en using quantum mechanical simulations. However\, these calculations are 
 computationally very demanding: for a system like this\, it could take hou
 rs on a supercomputer just to analyze a single trajectory step!\n\nWith th
 e AI boom\, machine-learning interatomic potentials (MLIPs) have become on
 e of the most promising alternatives. Instead of running expensive quantum
 -mechanical calculations at every step\, we can now perform only a small n
 umber of them and use the results as a training set for neural networks. O
 nce trained\, the MLIP can look at the complex atomic configuration of a s
 ystem and immediately predict the energies and forces acting on each atom\
 , without solving the underlying physics equations. This allows the simula
 tion to evolve in milliseconds rather than hours\, opening the door to sim
 ulations that were previously impractical.\n\nWithout the Scientific Pytho
 n ecosystem\, the development of machine learning methods in quantum chemi
 stry would have a very hard time\, since libraries such as PyTorch\, Sciki
 t-learn\, and TensorFlow\, combined with atomistic workflow tools like the
  Atomistic Simulation Environment (ASE)\, form the backbone of these metho
 ds. Importantly\, most MLIPs are also open-source projects\, whether devel
 oped by universities (MACE\, CHGNet\, and M3GNet) or by research groups at
  large technology companies such as Google DeepMind and Meta FAIR (UMA).\n
 \nIn this talk\, we will explore how Scientific Python libraries power mod
 ern MLIP workflows\, from dataset generation and model training to large-s
 cale 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 ato
 ms move to learning the behavior of electrons\, which ultimately determine
  those motions as well as many other fundamental properties\, a much more 
 challenging task.
DTSTAMP:20260715T021114Z
LOCATION:Memorial Hall
SUMMARY:How Is Python Transforming Materials Modeling with Machine Learning
 ? - Filippo Balzaretti
URL:https://pretalx.com/scipy-2026/talk/BQDMZH/
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