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Person# Giulio Imbalzano

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Related research domains (3)

Related publications (9)

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Molecular dynamics

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanical force fields.

Computer simulation

Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering.

Molecular simulations allow to investigate the behaviour of materials at the atomistic level, shedding light on phenomena that cannot be directly observed in experiments. Accurate results can be obtained with ab initio methods, while simulations of large-scale systems are usually possible only with coarse approximations of the molecular interactions. Machine learning interatomic potentials (MLIP) combine the strengths of the two methods in a framework that allows iterative refinement, opening the doors to the investigation of complex systems.Currently, the training of a MLIP is still human-centered. The success or failure is often dictated by the complexity of the system and by the experience of the user with the software. In this thesis, we want to provide some methods that would make the training and validation of the potentials easier and more general, even for complex, heterogeneous systems.We begin by comparing the learning ability of three widely adopted frameworks that have been developed by the community, proving that a well-constructed set of input features allow to learn at similar accuracy datasets of water dimers and trimers. Then, we compare heuristic methods based on the intrinsic correlations of the dataset to automatically identify the "best" inputs out of a larger set of candidates, which results in an accurate description of the system at a low computational cost. This allows to simplify the construction of potentials that use symmetry functions or smooth overlap of atomic positions as inputs.Finally, we introduce and implement a method to cheaply compute the uncertainty of the thermodynamic properties obtained through MD simulations with MLIPs. This method can be used either to assess the confidence of a given result obtained with a MLIP -necessary when we make quantitative predictions of properties- or to safely explore the phase space of interest, with the aid of a fall-back potential that takes over when the MLIP cannot be trusted.We showcase these methods with a real example, in which we train a potential for the complex GaAs system. The MLIP that we have developed is able to accurately predict the behavior across the whole phase diagram, spanning liquid and solid, metallic and semiconducting phases. In this endeavour we investigate a variety of methods to obtain a comprehensive dataset of structures that are fed into the MLIP.To demonstrate the transferability of the potential, we compute multiple properties, some of which (e.g. the liquid surface tension) are well beyond the limits of ab initio methods. We compare these results to our reference calculations and to experiments, finding a good agreement, within the limits of the selected level of theory (DFT at the GGA level). Finally, we use our GaAs MLIP to investigate the behaviour of liquid gallium in contact with the polar [111] surface of solid GaAs. Recent experimental findings assign an important role to the pre-ordering of the liquid at the interface during the growth of GaAs nanowires, pointing to the polarity as one of the main drivers for the correct growth. Our simulations allow to investigate this pre-ordering with increased detail, supporting and complementing the experimental observations. Furthermore, we explore the free energy of As atoms in the liquid Ga, to understand the behaviour of As atoms during the growth to help identifying the ideal growth conditions.

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Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to achieve transferable empirical models of interactions between the atoms that can reliably predict their behavior across the temperature and composition range that is relevant to the study of the synthesis and properties of III/V nanostructures and devices. We present a machine-learning potential trained on density functional theory reference data that provides a general-purpose model for the GaxAs1-x system. We provide a series of stringent tests that showcase the accuracy of the potential, and its applicability across the whole binary phase space, computing with ab initio accuracy a large number of finite-temperature properties as well as the location of phase boundaries. We also show how a committee model can be used to reliably determine the uncertainty induced by the limitations of the machine-learning model on its predictions, to identify regions of phase space that are predicted with insufficient accuracy, and to iteratively refine the training set to achieve consistent, reliable modeling.

Michele Ceriotti, Federico Grasselli, Yongbin Zhuang, Venkat Kapil, Kevin Rossi, Edgar Albert Engel, Giulio Imbalzano

Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature of these models, the reliability of predictions depends on the position in phase space, and it is crucial to obtain an estimate of the error that derives from the finite number of reference structures included during model training. When using a machine-learning potential to sample a finite-temperature ensemble, the uncertainty on individual configurations translates into an error on thermodynamic averages and leads to a loss of accuracy when the simulation enters a previously unexplored region. Here, we discuss how uncertainty quantification can be used, together with a baseline energy model, or a more robust but less accurate interatomic potential, to obtain more resilient simulations and to support active-learning strategies. Furthermore, we introduce an on-the-fly reweighing scheme that makes it possible to estimate the uncertainty in thermodynamic averages extracted from long trajectories. We present examples covering different types of structural and thermodynamic properties and systems as diverse as water and liquid gallium.

2021