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Atomic layer deposition (ALD) is a key technique for the continued scaling of semiconductor devices, which increasingly relies on reproducible and scalable processes for interface manipulation of 3D structured surfaces on the atomic scale.…

Building on our earlier study, we examine the kinetic barriers to decomposition of alane, AlH$_3$, on the Si(001) surface, using the nudged elastic band (NEB) approach within DFT. We find that the initial decomposition to AlH with two H…

Materials Science · Physics 2018-03-14 Richard Smith , David R. Bowler

Surface termination and interfacial interactions are critical for advanced solid-state quantum applications. In this paper, we demonstrate that atomic layer deposition (ALD) can both provide valuable insight on the chemical environment of…

Materials Science · Physics 2023-07-20 Jessica C. Jones , Nazar Delegan , F. Joseph Heremans , Alex B. F. Martinson

Computing accurate rate constants for catalytic events occurring at the surface of a given material represents a challenging task with multiple potential applications in chemistry. To address this question, we propose an approach based on a…

We present an asymptotic analysis of shell lattice metamaterials based on Ciarlet's shell theory, introducing a new metric--asymptotic directional stiffness (ADS)--to quantify how the geometry of the middle surface governs the effective…

Analysis of PDEs · Mathematics 2025-06-25 Di Zhang , Ligang Liu

X-ray absorption spectroscopy yields direct access to the electronic and geometric structure of hybrid inorganic-organic interfaces formed upon adsorption of complex molecules at metal surfaces. The unambiguous interpretation of…

Chemical Physics · Physics 2017-06-19 Katharina Diller , Reinhard J. Maurer , Moritz Müller , Karsten Reuter

Adsorption properties of chain fluids are of interest from both fundamental and industrial points of view. Density Functional Theory (DFT) based models are among the most appropriate techniques allowing to describe surface phenomena. At the…

Statistical Mechanics · Physics 2019-02-20 Timur Aslyamov , Vera Pletneva , Aleksey Khlyupin

High-entropy alloys (HEAs) exhibit exceptional catalytic performance due to their complex surface structures. However, the vast number of active binding sites in HEAs, as opposed to conventional alloys, presents a significant computational…

Materials Science · Physics 2024-08-22 Mohamed Hendy , Okan K. Orhan , Homin Shin , Ali Malek , Mauricio Ponga

Reactive dopant atoms embedded in inert host metal surfaces define the active sites in single-atom alloys (SAAs), yet SAA synthesis remains challenging. To address this, we elucidate how dopant adatoms deposited on Cu and Ag surfaces become…

Materials Science · Physics 2025-12-01 Ioannis Karageorgiou , Angelos Michaelides , Fabian Berger

We present a dry surface treatment combining atomic layer etching and deposition (ALE and ALD) to mitigate dielectric loss in fully fabricated superconducting quantum devices formed from aluminum thin films on silicon. The treatment,…

Static electric response properties of atoms and molecules are reported within the real-space Cartesian grid implementation of pseudopotential Kohn-Sham (KS) density functional theory (DFT). A detailed systematic investigation is made for a…

Chemical Physics · Physics 2019-04-26 Tanmay Mandal , Abhisek Ghosal , Amlan K. Roy

Dense random sampling and surfacing of shapes encoded via implicit occupancy functions (OFs) are critical elements of many applications. Existing methods largely provide either one or the other of random sampling or mesh surfaces: ray…

Graphics · Computer Science 2026-05-06 Suzuran Takikawa , Leo Foord-Kelcey , Oliver Oxford , Nicholas Vining , Alla Sheffer

Conventional methods for modeling thermocatalytic systems are typically based on the Kohn-Sham density functional theory (KS-DFT), neglecting the inhomogeneous distributions of gas molecules in the reactive environment. However, industrial…

Chemical Physics · Physics 2025-08-14 Jikai Sun , Jianzhong Wu

Nonadiabatic effects in chemical reaction at metal surfaces, due to excitation of electron-hole pairs, stand at the frontier of the studies of gas-surface reaction dynamics. However, the first principles description of electronic excitation…

Chemical Physics · Physics 2019-12-20 Yaolong Zhang , Reinhard J. Maurer , Bin Jiang

We used DFT to study the energetics of the decomposition of alane, AlH3, on the Si(001) surface, as the acceptor complement to PH3. Alane forms a dative bond with the raised atoms of silicon surface dimers, via the Si atom lone pair. We…

Materials Science · Physics 2017-09-13 Richard L. Smith , David R. Bowler

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of…

Materials Science · Physics 2021-07-07 Yilin Yang , Omar A. Jimenez-Negron , John R. Kitchin

We report differences in adsorption and reaction energetics for ethylene on Si(001) with respect to different dangling bond configurations induced by hydrogen precoverage as obtained via density functional theory calculations. This can help…

Chemical Physics · Physics 2016-12-06 Josua Pecher , Gerson Mette , Michael Dürr , Ralf Tonner

The purpose of this work is to identify the field evaporation mechanism associated with charge density distribution under extreme fields, linking atom probe tomography (APT) experiments with density functional theory (DFT) modeling. DFT is…

Materials Science · Physics 2016-12-21 Claudia Loyola , Joaquin Peralta , Scott R. Broderick , Krishna Rajan

Machine-learned interatomic potentials (MLPs) provide near density functional theory (DFT) accuracy at reduced computational cost, but their reliability depends on representative training data and often deteriorates in transition-state…

Chemical Physics · Physics 2026-05-06 Ashique Lal , Rik S. Breebaart , Peter G. Bolhuis , Evert Jan Meijer

Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their…

Materials Science · Physics 2026-02-23 Nico Unglert , Michael Ketter , Georg K. H. Madsen
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