About

David B. Graves is a Professor of Chemical and Biological Engineering at Princeton University. His research centers around the science and applications of non-equilibrium, or 'low temperature,' ionized gas plasma. He investigates the properties of non-equilibrium plasma (NEP), a weakly to partially ionized gas where electrons are generally much hotter than neutral or ionic species, enabling a wide range of technological applications. His work includes plasma applications in semiconductor and quantum device nanofabrication, where plasma is used in nearly half of the steps involved in manufacturing semiconductor integrated circuits, primarily through thin film etching or deposition. He focuses on developing advanced plasma models, large-scale computing, plasma and surface diagnostics, machine learning, and process control to address challenges such as controlling device critical dimensions at nanometer scales and minimizing contamination and damage, especially in quantum device fabrication. Additionally, Graves explores plasma's biomedical applications, including disinfection, wound healing, cancer treatment, and dental procedures, leveraging plasma-generated reactive species and their interactions with biological tissues. His research also extends to chemical processing, where plasma is used to induce chemical transformations and promote electrification of industrial processes, with an emphasis on improving chemical selectivity through coupling plasma with catalysis. Graves has received numerous awards for his contributions to plasma chemistry, including the Plasma Chemistry Award from the International Plasma Chemistry Society, the ISPlasma Prize, and fellowships in professional societies. His work involves collaboration across fields such as materials science, surface science, device physics, biochemistry, medicine, and agriculture.

Research topics

• Sociology
• Materials science
• Physics
• Artificial Intelligence
• Computer Science
• Engineering
• Nanotechnology
• Machine Learning
• Biochemical engineering
• Nuclear physics
• Engineering physics

Selected publications

• Diffusion-reaction modeling of atomic layer etching

  Journal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2026-01-09

  articleSenior author

  We present a diffusion-reaction model of plasma-assisted atomic layer etching (ALE) of silicon (Si) with alternating exposure to chlorine gas (Cl2) and argon ions (Ar+). The model is an extension of a previous transient site-balance model [Vella et al., Plasma Sources Sci. Technol. 33, 075009 (2024)], which was parameterized using molecular dynamics (MD) simulations but assumed perfect mixing within the near-surface “mixed layer.” In contrast, the diffusion-reaction model, also parameterized using MD data, provides a continuum description that resolves transient spatial gradients of Cl concentration within the near-surface region during Ar+ ion bombardment. The model conceptualizes the near-surface region as liquidlike, where Cl atoms mix and diffuse within the region during ion bombardment. The model is shown to accurately capture the evolution of the Cl concentration profiles during the ALE ion bombardment step. It also correctly reproduces the etch product distributions. This approach can be generalized to more complex chemistries and materials and could serve as a powerful foundation for future reduced-order models (ROMs) of surface modification during plasma-assisted processing.

  PublisherDOI

• A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

  npj Computational Materials · 2025-07-15 · 2 citations

  articleOpen access

  Abstract Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

  PublisherOA PDFDOI

• The importance of accounting for the Tolman correction to surface tension for nucleation and growth modeling of Fe clusters

  Aerosol Science and Technology · 2025-02-05 · 1 citations

  articleOpen accessSenior author

  Gibbs free energies of clusters are required for predictive modeling of cluster growth during condensation of a cooling vapor. Here, we present a straightforward method of calculating free energies of cluster formation using the data from molecular dynamics (MD) simulations. We apply this method to iron clusters having from 2 to 100 atoms. The energies obtained are verified by comparing to an MD-simulated equilibrium cluster size distribution in a sub-saturated vapor. We show that these free energies differ significantly from those obtained with a commonly used spherical cluster approximation, which relies on a surface tension coefficient of a flat surface, as it is used in the classical nucleation theory (CNT). We show that the spherical cluster approximation in CNT can be improved by using a cluster-size-dependent Tolman correction for the surface tension. The Tolman length and effective surface tension values were derived for iron clusters, and they significantly differ from the commonly used experimentally measured values. This improved approximation does not account for geometric magic number effects responsible for spikes and troughs in densities of neighbor cluster sizes. Nonetheless, it allows to more accurately model cluster formation from a cooling vapor. It better reproduces the condensation timeline, overall shape of the cluster size distribution, average cluster size, and the distribution width. In contrast, using a constant surface tension coefficient (as done in CNT) resulted in incorrect condensation dynamics and cluster size distributions. The analytical expression for cluster nucleation rate from CNT was updated to account for the size-dependence of cluster surface tension.

  PublisherOA PDFDOI

• Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions

  Journal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2025-01-01 · 3 citations

  articleOpen accessSenior author

  Machine learning approaches to potential generation for molecular dynamics (MD) simulations of low-temperature plasma-surface interactions could greatly extend the range of chemical systems that can be modeled. Empirical potentials are difficult to generalize to complex combinations of multiple elements with interactions that might include covalent, ionic, and metallic bonds. This work demonstrates that a specific machine learning approach, Deep Potential Molecular Dynamics (DeepMD), can generate potentials that provide a good model of plasma etching in the Si-Cl-Ar system. Comparisons are made between MD results using DeepMD models and empirical potentials, as well as experimental measurements. Pure Si properties predicted by the DeepMD model are in reasonable agreement with experimental results. Simulations of Si bombardment by Ar+ ions demonstrate the ability of the DeepMD method to predict sputtering yields as well as the depth of the amorphous-crystalline interface. Etch yields as a function of flux ratio and ion energy for simultaneous Cl2 and Ar+ impacts are in good agreement with previous simulation results and experiment. Predictions of etch yields and etch products during plasma-assisted atomic layer etching of Si-Cl2-Ar are shown to be in good agreement with MD predictions using empirical potentials and with experiment. Finally, good agreement was also seen with measurements for the spontaneous etching of Si by Cl atoms at 300 K. The demonstration that DeepMD can reproduce results from MD simulations using empirical potentials is a necessary condition to future efforts to extend the method to a much wider range of systems for which empirical potentials may be difficult or impossible to obtain.

  PublisherOA PDFDOI

• Si–Cl₂–Ar⁺ Atomic Layer Etching Window: A Fundamental Study Using Molecular Dynamics Simulations and a Reduced Order Model

  The Journal of Physical Chemistry B · 2025-06-02 · 5 citations

  articleSenior author

  Silicon (Si) atomic layer etching (ALE) by alternating exposure to chlorine gas (Cl2) and argon ions (Ar+) is studied by using molecular dynamics (MD) simulations and a reduced order model (ROM). The purpose of this study is to elucidate the properties of the ALE window, a range of ion energies where the amount of Si etched over a series of cycles is nonzero and nearly independent of ion energy. Experimental studies of the Si–Cl2–Ar+ ALE system report contradictory results related to the ALE window’s ion energy range. Both MD simulations and the ROM show that there is an ALE window present from approximately 15 to 20 eV for normal incidence argon ions. The Si–Cl2–Ar+ system, therefore, exhibits a narrow ALE window. The amount of Si etched per cycle is less than one atomic layer because of the higher etch yield of Cl atoms relative to atomic Si and silicon chlorides. A modified version of the ROM with an artificially increased Si physical sputtering threshold energy expands the ALE window, illustrating the importance of the difference in chemical and physical sputtering threshold energies in the ALE window energy range. The ROM is also used to examine the dependence of the EPC on the Ar+ ion fluence.

  PublisherDOI

• Size-dependent second-order-like phase transitions in Fe nanocluster melting from low-temperature structural isomerization

  The Journal of Chemical Physics · 2025-04-07

  articleOpen accessSenior author

  In this work, the melting phase transitions of Fen nanoclusters with 10 ≤ n ≤ 100 atoms are investigated using classical many-body molecular dynamics simulations. For many cluster sizes, surface melting occurs at much lower temperatures than core melting. Surface and core melting points and energetic melting points (temperatures of maximum heat capacity, Cv) are calculated for all cluster sizes. Melting properties are found to be strong functions of cluster structure. Cluster sizes with closed-shell structures always have first-order-like phase transitions. Almost one-third of cluster sizes in the analyzed range exhibit second-order-like phase transitions due to the presence of multiple structural configurations close in energy. 1-shell clusters with one to a few more atoms than a neighboring closed-shell structure have very low surface melting points and very high energetic melting points compared to their closed-shell counterparts. In clusters above 50 atoms with certain core structures, melting of the surface before the core was observed.

  PublisherDOI

• Development and transferability of neural-network models for plasma-surface interactions

  Journal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2025-10-07 · 1 citations

  articleSenior author

  Plasma-surface interactions are increasingly critical to modern technologies; yet, accurate molecular dynamics simulations remain limited by the capabilities of interatomic potentials. Deep Potentials (DPs) promise to revolutionize the field by providing a systematic method for producing accurate interatomic potentials. The primary challenge of DP development is selecting a dataset, which efficiently spans the set of atomic environments one expects to encounter in the subsequent molecular dynamics simulations. The computational cost of density functional theory calculations, which are the typical basis for DP development, makes it impossible to directly verify the quality of a given DP. To address this challenge, we explore the development of a deep-learned interatomic potential, “DeepREBO,” trained to reproduce the behavior of the REBO2 empirical potential, enabling direct validation of training methodology and transferability. Using an active learning framework, we begin with a minimal dataset and iteratively expand it to train a Deep Potential-Smooth Edition model that faithfully reproduces REBO2 results for 25 eV hydrogen bombardment of diamond (001), a particularly challenging case. We show that small, carefully curated datasets can outperform large, unguided ones, with effective models requiring fewer than 15 000 snapshots. Subsequent transferability tests demonstrate that while DeepREBO generalizes well to diamond (111) surfaces, performance degrades for amorphous carbon or higher-energy impacts, highlighting the need for use-case-specific training data. We also evaluate methods to improve short-range repulsion. This study outlines best practices for training robust deep potentials and underscores the importance of dataset design for predictive plasma simulations.

  PublisherDOI

• Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine

  Journal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2025-10-16

  articleSenior author

  The continued development of plasma-assisted processing techniques requires a fundamental understanding of plasma-surface interactions. Molecular dynamics (MD) simulations have been employed to complement experimental studies and better understand the properties of such systems. Recently, machine learning (ML) methods have enabled the development of ab initio-based interatomic potentials, which can be generalized to complex combinations of multiple atom types. In this work, we use ML potentials developed using the Deep Potential Molecular Dynamics (DeepMD) framework to provide a model of ion-enhanced etching of Si by Cl atoms. We demonstrate the importance of proper selection of the training data set to the accuracy of the DeepMD model and compare our results to MD results using empirical potentials, as well as to experimental measurements. Exposure of undoped Si at 300 K to thermal Cl atoms yields a steady-state Cl coverage of 1.25 monolayers, which is slightly lower than the value obtained in previous experimental studies. Predictions of Si etch yields by simultaneous Cl atom and Ar+ ion impacts as a function of ion energy, neutral to ion flux ratio, and angle of incidence of the ions are in reasonably good agreement with classical MD results and experimental measurements. Finally, etch yields and SiClx mixed layer thicknesses during simultaneous bombardment of the Si(100) surface by Cl atoms and Cl+ ions are in good agreement with experimental data. The present work is a necessary condition for the extension of the DeepMD procedure to more complex systems of interest in plasma-surface interactions.

  PublisherDOI

• Gibbs free energies of Fe clusters can be approximated by Tolman correction to accurately model cluster nucleation and growth

  arXiv (Cornell University) · 2024-08-29

  preprintOpen accessSenior author

  Accurate Gibbs free energies of Fe clusters are required for predictive modeling of Fe cluster growth during condensation of a cooling vapor. We present a straightforward method of calculating free energies of cluster formation using the data provided by molecular dynamics (MD) simulations. We apply this method to calculate free energies of Fe clusters having from 2 to 100 atoms. The free energies are verified by comparing to an MD-simulated equilibrium cluster size distribution in a sub-saturated vapor. We show that these free energies differ significantly from those obtained with a commonly used spherical cluster approximation - which relies on a surface tension coefficient of a flat surface. The spherical cluster approximation can be improved by using a cluster size-dependent Tolman correction for the surface tension. The values for the Tolman length and effective surface tension were derived, which differ from the commonly used experimentally measured surface tension based on the potential energy. This improved approximation does not account for geometric magic number effects responsible for spikes and troughs in densities of neighbor cluster sizes. Nonetheless, it allows to model cluster formation from a cooling vapor and accurately reproduce the condensation timeline, overall shape of the cluster size distribution, average cluster size, and the distribution width. Using a constant surface tension coefficient resulted in distorted condensation dynamics and inaccurate cluster size distributions. The analytical expression for cluster nucleation rate from classical nucleation theory (CNT) was updated to account for the size-dependence of cluster surface tension.

  PublisherOA PDFDOI

• Atomic scale etching of diamond: insights from molecular dynamics simulations

  Journal of Physics D Applied Physics · 2024-09-10 · 9 citations

  articleOpen accessSenior author

  Abstract Diamond is a promising material for multiple applications in quantum information processing and sensing as well as applications in microelectronics. However, diamond devices can be limited by surface defects that compromise charge stability and spin coherence, among others. Improved strategies in plasma etching of diamond could play an important role in minimizing or eliminating these defects. In this work, we explore plasma-assisted atomic scale etching of diamond using argon ions (Ar + ), hydrogen ions (H + ) and hydrogen atoms (H). We employ classical molecular dynamics (MD) simulations and test several interatomic potentials based on the Reactive Empirical Bond Order (REBO) form with comparisons to a variety of published experimental results. We performed MD simulations of low-energy hydrogen ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mtext>⩽</mml:mtext> </mml:mrow> </mml:math> 50 eV) and argon ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mtext>⩽</mml:mtext> </mml:mrow> </mml:math> 200 eV) ion bombardment of diamond surfaces. Ar + bombardment can be used to locally smooth initially rough diamond surfaces via the formation of an amorphous C layer, the thickness of which increases with argon ion energy. Subsequent exposure with hydrogen ions (or fast neutrals) will selectively etch this amorphous C layer, leaving the underlying diamond layer mostly intact if the H energy is maintained below about 10 eV. The simulations suggest that combining Ar + smoothing with selective, near threshold energy H removal of amorphous C can be an effective strategy for diamond surface engineering, leading to more reliable and sensitive diamond color center devices.

  PublisherDOI

Recent grants

• INFEWS N/P/H2O: Fundamentals of N2/O2 plasma and heterogeneous catalysis

  NSF · $300k · 2016–2020

• Collaborative Research: Atmospheric Pressure Plasma-Biomaterial Interactions - Bridging Understanding Of APP Sources To Rational Modification Of Biomolecules

  NSF · $15k · 2014–2017

Frequent coauthors

• Robert D. Short

  University of Sheffield

  86 shared

• Nishtha Gaur

  Lancaster University

  84 shared

• Akimitsu Hatta

  Photonic Systems (United States)

  84 shared

• Sung‐Ha Hong

  University of South Australia

  84 shared

• Endre J. Szili

  University of South Australia

  84 shared

• Allison J. Cowin

  University of South Australia

  83 shared

• Christine Charles

  Australian National University

  83 shared

• Masafumi Ito

  Japanese Red Cross Nagoya Daiichi Hospital

  83 shared

Labs

• Graves GroupPI

Education

• PhD, Chemical Engineering

  University of Minnesota

  1986

• Masters of Science, Chemical Engineering

  University of Arizona

  1981

• Bachelor of Science, Chemical Engineering

  University of Arizona

  1978

Awards & honors

• Plasma Chemistry Award, International Plasma Chemistry Socie…
• ISPlasma Prize, 2024
• Fellow of the International Plasma Chemistry Society, 2023
• Plasma Material Science Hall of Fame Prize, 2022
• Huazhong University of Science and Technology, Foreign Exper…