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Ion generation by photoionization and photofield tunneling of electrons during atom probe tomography of thermally grown chromia with deep UV laser light
Authors:
Severin Jakob,
David Mayweg,
Mattias Thuvander
Abstract:
The evaporation mechanisms during laser-assisted field evaporation of thermally grown chromia is investigated with the newest generation of commercial atom probe tomography instrument, equipped with a deep-UV laser (257.5 nm, 4.8 eV photon energy). By holding the voltage constant, the electrostatic field is kept constant, and the evolution of detection rate is recorded. The detection rate is measu…
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The evaporation mechanisms during laser-assisted field evaporation of thermally grown chromia is investigated with the newest generation of commercial atom probe tomography instrument, equipped with a deep-UV laser (257.5 nm, 4.8 eV photon energy). By holding the voltage constant, the electrostatic field is kept constant, and the evolution of detection rate is recorded. The detection rate is measured as a function of laser pulse energy for chromia and the metallic alloy Ni-20Cr, where the expected thermal evaporation is observed. Furthermore, the detection rate as a function of electrostatic field is measured for chromia and follows the well-known Fowler-Nordheim type equation. The observation suggests that photoionization and photofield electron tunneling are the rate-limiting steps during evaporation of chromia with deep-UV laser light. The work function and bandgap of the material are discussed, and the evaporation behavior is put into context to existing observations.
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Submitted 4 November, 2024;
originally announced November 2024.
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Towards establishing best practice in the analysis of hydrogen and deuterium by atom probe tomography
Authors:
Baptiste Gault,
Aparna Saksena,
Xavier Sauvage,
Paul Bagot,
Leonardo S. Aota,
Jonas Arlt,
Lisa T. Belkacemi,
Torben Boll,
Yi-Sheng Chen,
Luke Daly,
Milos B. Djukic,
James O. Douglas,
Maria J. Duarte,
Peter J. Felfer,
Richard G. Forbes,
Jing Fu,
Hazel M. Gardner,
Ryota Gemma,
Stephan S. A. Gerstl,
Yilun Gong,
Guillaume Hachet,
Severin Jakob,
Benjamin M. Jenkins,
Megan E. Jones,
Heena Khanchandani
, et al. (20 additional authors not shown)
Abstract:
As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical properties (strength, ductility, toughness) of most materials that can hinder their use as part of the infrastructure of a hydrogen-based economy. Enab…
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As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical properties (strength, ductility, toughness) of most materials that can hinder their use as part of the infrastructure of a hydrogen-based economy. Enabling H mapping, including local hydrogen concentration analyses at specific microstructural features, is essential for understanding the multiple ways that H affect the properties of materials, including for instance embrittlement mechanisms and their synergies, but also spatial mapping and quantification of hydrogen isotopes is essential to accurately predict tritium inventory of future fusion power plants, ensuring their safe and efficient operation for example. Atom probe tomography (APT) has the intrinsic capabilities for detecting hydrogen (H), and deuterium (D), and in principle the capacity for performing quantitative mapping of H within a material's microstructure. Yet the accuracy and precision of H analysis by APT remain affected by the influence of residual hydrogen from the ultra-high vacuum chamber that can obscure the signal of H from within the material, along with a complex field evaporation behavior. The present article reports the essence of discussions at a focused workshop held at the Max-Planck Institute for Sustainable Materials in April 2024. The workshop was organized to pave the way to establishing best practices in reporting APT data for the analysis of H. We first summarize the key aspects of the intricacies of H analysis by APT and propose a path for better reporting of the relevant data to support interpretation of APT-based H analysis in materials.
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Submitted 21 May, 2024;
originally announced May 2024.
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A foundation model for atomistic materials chemistry
Authors:
Ilyes Batatia,
Philipp Benner,
Yuan Chiang,
Alin M. Elena,
Dávid P. Kovács,
Janosh Riebesell,
Xavier R. Advincula,
Mark Asta,
Matthew Avaylon,
William J. Baldwin,
Fabian Berger,
Noam Bernstein,
Arghya Bhowmik,
Filippo Bigi,
Samuel M. Blau,
Vlad Cărare,
Michele Ceriotti,
Sanggyu Chong,
James P. Darby,
Sandip De,
Flaviano Della Pia,
Volker L. Deringer,
Rokas Elijošius,
Zakariya El-Machachi,
Fabio Falcioni
, et al. (63 additional authors not shown)
Abstract:
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time…
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Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early ML force fields have largely been limited by: (i) the substantial computational and human effort of developing and validating potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model - and its qualitative and at times quantitative accuracy - on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users get reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step towards democratising the revolution in atomic-scale modeling that has been brought about by ML force fields.
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Submitted 4 September, 2025; v1 submitted 29 December, 2023;
originally announced January 2024.