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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

3D nanolithography with metalens arrays and spatially adaptive illumination

The growing demand for advanced materials, miniaturized devices and integrated microsystems calls for the reliable fabrication of complex, multiscale, three-dimensional (3D) architectures, a need increasingly addressed through light-based and laser-based processes. However, owing to the field-of-view (FOV) limitations of conventional imaging optics, existing 3D laser nanofabrication techniques face fundamental challenges in throughput, proximity error and stitching defects on the path to scaling. Here, in this study, we present a scalable 3D nanofabrication platform that uses a metalens-generated focal spot array to parallelize two-photon lithography (TPL) beyond centimetre-scale write field areas. Metalenses are ideally suited for producing submicron-scale focal spots for high-throughput nanolithography, as they uniquely feature large numerical apertures (NAs), immersion media compatibility and large-scale manufacturability. We experimentally demonstrate a printing system that uses a 12-cm 2 metalens array to produce more than 120,000 cooperative focal spots, corresponding to a throughput exceeding 10 8 voxels s −1 . By programmatically patterning the focal spot array using a spatial light modulator (SLM), an adaptive parallel printing strategy is developed for precise greyscale linewidth modulation and choreographed printing of semiperiodic and fully aperiodic 3D geometries. We demonstrate parallel printing of replicated microstructures (>50 M microparticles per day), centimetre-scale 3D architectures with feature sizes down to 113 nm, and photonic and mechanical metamaterials. This work demonstrates the potential of 3D nanolithography towards wafer-scale production, showing how TPL could be used at scale for applications in microelectronics, biomedicine, quantum technology and high-energy laser targets.

Materials science↗
DOE OSTI2020

Standoff Detection of Chemical Plumes Using Swept-ECQCL Remote Detection Technology: Results from EMRTC Complex Terrain Dual Tracer Experiment

As part of the Complex Terrain Dual Tracer Experiments performed in support of the LYNM program, a swept-wavelength external cavity quantum cascade laser (swept-ECQCL) remote detection system was deployed to measure chemical plume propagation with high temporal resolution. The swept-ECQCL remote detection technology was initially developed at PNNL, and is now under further development at Opticslah, LLC under SBIR funding from DOE (DOE SBIR Phase I Project DE-SC0019855, Remote Detection Technologies, Program Manager Chris Ramos). The swept-ECQCL technology provides a single-frequency infrared laser source which is rapidly scanned over a large wavelength range to measure the infrared absorption of gases, liquids, or solids. Analysis of the measured infrared absorption spectrum is used to detect, identify, and quantify the chemical species which are present. While most previous laser-based techniques used small wavelength tuning ranges to measure only a few isolated spectral lines of a few species, the swept-ECQCL provides the large tuning range needed to measure multiple species simultaneously, and to detect broad absorption features from large molecules or condensed phase materials. In addition, the swept-ECQCL systems have high enough spectral resolution and sensitivity to measure narrow lines of small molecules and determine isotope ratios. The remote detection system for gas plumes is based on the swept-ECQCL technology, and consists of a swept-ECQCL source, infrared detector, control electronics, and beam transmitting and alignment optics. The remote detection system directs the infrared output of the swept-ECQCL to a target retro reflector typically located at ~10-10,000 m distance. The swept-ECQCL wavelength is varied continuously over a large wavelength range (>1000 nm) at rates up to 1000 Hz. The swept ECQCL light reflected from the remote target is collected and focused onto an infrared photodetector. By measuring the detector signal continuously as the swept-ECQCL wavelength is varied, a time-series of absorption spectra are measured for the gases along the beam line of-sight. Based on analysis and fitting of the measured absorption spectra, the time-dependent chemical concentrations of species are determined. Multiple species and mixtures can be detected simultaneously, for all species with absorption features above the sensor noise floor. The sensor output is thus a continuous, high-speed record of identified chemicals along with their concentrations and isotope ratios of interest. The high-speed operation enables low-noise operation in conditions of atmospheric turbulence and allows tracking of fluctuating concentrations in transient chemical plumes as needed for reliable remote detection applications. All components operate with minimal cooling requirements and no cryogens are needed. In addition, the output infrared light is invisible with an intensity below the maximum permissible exposure (MPE) threshold of 100 mW/cm2 for these infrared wavelengths and thus eye safe. The measurement concept for plume detection using the swept-ECQCL remote detection system is as follows. The ECQCL beam is directed toward a remote retro-reflector, which reflects the beam back to the ECQCL system where it is focused onto an infrared photodetector. The line-of-sight from the ECQCL system to the retro-reflector defines the measurement path. The detected infrared signal is recorded continuously and detects the absorption of molecular species along the measurement path. Before a plume release, the system measures absorption from atmospheric constituents, which in this LWIR band consist primarily of lines from H 2 O and CO 2 , with additional weaker absorption from N 2 O, NH 3 , and O 3 possibly contributing. The system then measures any changes in absorption due to the tracer compounds when they enter the measurement path. The shape of absorption features is used to distinguish and identify the different tracer compounds, and the strength of the absorption is used to determine the path-integrated concentration – also called the column density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Neural-network model for force prediction in multi-principal-element alloys

Atomistic simulations can provide useful insights into the physical properties of multi-principal-element alloys. However, classical potentials mostly fail to capture key quantum (electronic-structure) effects. Here we present a deep 3D convolutional neural network (3D CNN) based framework combined with a voxelization technique to design interatomic potentials for chemically complex alloys. We highlight the performance of the 3D CNN model and its efficacy in computing potentials using the medium-entropy alloy TaNbMo. In order to provide insights into the effect of voxel resolution, we implemented two approaches based on the inner and outer bounding boxes. An efficient 3D CNN model, which is as accurate as the density-functional theory (DFT) approach, for calculating potentials will provide a promising schema for accurate atomistic simulations of structure and dynamics of general multi-principle element alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Emergent ferromagnetism and insulator-metal transition in δ-doped ultrathin ruthenates

Abstract Heterostructures of complex transition metal oxides are known to induce extraordinary emergent quantum states that arise from broken symmetry and other discontinuities at interfaces. Here we report the emergence of unusual, thickness-dependent properties in ultrathin CaRuO 3 films by insertion of a single isovalent SrO layer (referred to as δ-doping). While bulk CaRuO 3 is metallic and nonmagnetic, films thinner than or equal to ~15-unit cells (u.c.) are insulating though still nonmagnetic. However, δ-doping to middle of such CaRuO 3 films induces an insulator-to-metal transition and unusual ferromagnetism with strong magnetoresistive behavior. Atomically resolved imaging and density-functional-theory calculations reveal that the whole δ-doped film preserves the bulk-CaRuO 3 orthorhombic structure, while appreciable structural and electronic changes are highly localized near the SrO layer. The results highlight delicate nature of magnetic instability in CaRuO 3 and subtle effects that can alter it, especially the role of A-site cation in electronic and magnetic structure additional to lattice distortion in ruthenates. It also provides a practical approach to engineer material systems via highly localized modifications in their structure and composition that may offer new routes to the design of oxide electronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Extreme Transverse Magnetoresistance in TiZn 16

Extreme magnetoresistance (XMR) is a phenomenon characterized by an increase in resistance by factors of 10 4 –10 7 % when a magnetic field is applied. This phenomenon is found in a number of semimetals such as WTe 2 , PtSn 4 , Cd 3 As 2 , and LaSb. The origin of XMR is still hotly debated, possibly with different materials having different (or multiple) explanations. Extreme transverse magnetoresistance of up to 8000% at 14 T and 1.8 K is measured in TiZn 16 , a semimetal with a multitude of bands crossing the Fermi energy, akin to PtSn 4 . The magnetoresistance is suppressed when the magnetic field is rotated to be parallel to the applied current, similar to PtSn 4 and PdSn 4 . The resistance of TiZn 16 follows Kohler's rule, but displays different behavior under an applied transverse field and under a longitudinal magnetic field, suggesting distinct electrical phases. Also present are Shubnikov-de Haas and de Haas-van Alphen oscillations with a transverse magnetic field up to 43 T, showing that despite an insulator-like temperature-resistance curve, charge carriers are still present. This positions TiZn 16 as an interesting addition to the investigation of XMR materials as a multi-band metal with complex Fermi surface geometries.

extreme magnetoresistance↗
DOE OSTI2020

Many-Body Level Statistics of Single-Particle Quantum Chaos

We consider a noninteracting many-fermion system populating levels of a unitary random matrix ensemble (equivalent to the q = 2 complex Sachdev-Ye-Kitaev model)—a generic model of single-particle quantum chaos. We study the corresponding many-particle level statistics by calculating the spectral form factor analytically using algebraic methods of random matrix theory, and match it with an exact numerical simulation. Despite the integrability of the theory, the many-body spectral rigidity is found to have a surprisingly rich landscape. In particular, we find a residual repulsion of distant many-body levels stemming from single-particle chaos, together with islands of level attraction. These results are encoded in an exponential ramp in the spectral form factor, which we show to be a universal feature of nonergodic many-fermion systems embedded in a chaotic medium.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Hexagonal Boron Nitride: Physical Properties, Hydride Vapor‐Phase Epitaxy Growth of Large‐Diameter Quasi‐Bulk Wafers and Applications

Hexagonal boron nitride ( h ‐BN), with its ultrawide bandgap and 2D structure, holds an immense promise for advanced semiconductor applications. Scaling bulk crystals to large‐diameter wafers, crucial for complex device fabrication, remains a challenge with high temperature, high pressure, and metal flux solution methods. To address this, recent efforts have focused on hydride vapor‐phase epitaxy (HVPE) for producing large diameter thick h ‐BN quasi‐bulk wafers, reaching hundreds of micrometers. These HVPE‐grown quasi‐bulk crystals exhibit excellent c ‐axis long‐range order. Notably, the in‐plane mobility‐lifetime products for both electrons and holes surpass 10 −4 cm 2 V −1 , which are two orders of magnitude greater than the out‐of‐plane (vertical) values, highlighting the potential for high‐performance devices leveraging superior lateral transport. Lateral detectors fabricated from 100 μm thick B‐10 enriched h‐ BN wafers have achieved a record 60% thermal neutron detection efficiency. Based on its physical properties, h‐ BN appears to be an outstanding material of choice for light‐triggered electronic power switches capable of supporting high ‐ voltage and high ‐ power operations. These recent advancements in large‐diameter h ‐BN quasi‐bulk crystal growth, enabled by HVPE, pave the way for applications spanning deep UV photonics, high‐power electronics, high‐efficiency neutron detection, and quantum information technologies, establishing h ‐BN as both a versatile active semiconductor and an ideal substrate.

Jiang, Hongxing [Department of Electrical and Comp↗
DOE OSTI2021

The simulation of 3D hypervelocity spallation using a hydrocode PAGOSA with FLIP+MPM

n this work, a hydrocode PAGOSA with FLIP+MPM is presented and exercised to investigate the fracture in ductile material. The merit of PAGOSA with FLIP+MPM to solve the advection problem is first illustrated by a solid piston periodically moving in a sealed tube with air. Furthermore, the ability of PAGOSA with FLIP+MPM to capture the fracture in material is shown by a simple stretching fracture in ductile material. In both of two benchmark problems, the PAGOSA results and analytical solutions are also presented for comparison. Then PAGOSA with FLIP+MPM is used to model complex spall in ductile material, which is a challenging problem in engineering applications. The convergences of PAGOSA with FLIP+MPM—based on both the mesh size and the marker density—are investigated by monitoring free surface velocity. To further show the ability of PAGOSA with FLIP+MPM to predict fracture in ductile material, the numerical results are compared with the experimental results and other published numerical results. Moreover, the effect of the spall parameter in PAGOSA with FLIP+MPM on the numerical simulation is also analyzed by investigating the free surface velocity. Finally, the effects of the peak compressive stress, the tensile strain rate, and the loading rate on the spallation are further investigated. The numerical results show that PAGOSA with FLIP+MPM can improve PAGOSA's performance when applied to predicting fracture in ductile materials, and is robust enough to accurately predict spall fracture in ductile materials.

42 ENGINEERING↗
DOE OSTI2025

Low-Loss Charge Transfer Plasmons in Graphene/α-RuCl 3 Heterostructures Below 40 K

Charge transfer at material interfaces governs a wide range of physical properties, from electronic band structures to emergent collective excitations. In two-dimensional (2D) material heterostructures, charge transfer phenomena play important roles in enabling novel quantum phases, proximity effects, and tunable plasmonic responses. One representative charge transfer interface is formed between α-RuCl 3 , a van der Waals material with high electron affinity, and graphene. Significant charge transfer across this interface induces the formation of charge-transfer plasmon polaritons (CPPs), hybrid excitations between light and charge oscillations. However, previous studies found that as the charge transfer process takes place, α-RuCl 3 becomes lossy, which limits the quality factor of CPPs. Here, we investigate CPPs down to 10 K using a home-built scattering-type scanning near-field optical microscope (s-SNOM) optimized for low-temperature measurements. Our study reveals a dramatic suppression of plasmon loss channels below 40 K, contributing to a significant enhancement in the plasmonic quality factor. This reduction in loss is likely attributed to the blue shift of the correlation-induced Mott gap in α-RuCl 3 with decreasing temperature, along with the reduction of phonon scattering at low temperature. In conclusion, our results highlight the potential of using s-SNOM and CPPs to study complex 2D interfaces and reveal correlated electron dynamics in the underlying material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Proximity-induced chiral quantum light generation in strain-engineered WSe 2 /NiPS 3 heterostructures

Quantum light emitters capable of generating single photons with circular polarization and non-classical statistics could enable non-reciprocal single-photon devices and deterministic spin–photon interfaces for quantum networks. To date, the emission of such chiral quantum light relies on the application of intense external magnetic fields, electrical/optical injection of spin-polarized carriers/excitons or coupling with complex photonic metastructures. Here we report the creation of free-space chiral quantum light emitters via the nanoindentation of monolayer WSe 2 /NiPS 3 heterostructures at zero external magnetic field. These quantum light emitters emit with a high degree of circular polarization (0.89) and single-photon purity (95%), independent of pump laser polarization. In this study, scanning diamond nitrogen-vacancy microscopy and temperature-dependent magneto-photoluminescence studies reveal that the chiral quantum light emission arises from magnetic proximity interactions between localized excitons in the WSe 2 monolayer and the out-of-plane magnetization of defects in the antiferromagnetic order of NiPS 3 , both of which are co-localized by strain fields associated with the nanoscale indentations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Record-quality GaAs two-dimensional hole systems

The complex band structure, large spin-orbit induced band splitting, and heavy effective mass of two-dimensional (2D) hole systems hosted in GaAs quantum wells render them rich platforms to study many-body physics and ballistic transport phenomena. Here we report ultra-high-quality (001) GaAs 2D hole systems, fabricated using molecular beam epitaxy and modulation doping, with mobility values as high as 5.8 × 10 6 cm 2 /(V s) at a hole density of p = 1.3 × 10 11 /cm 2 , implying a mean free path of ≃ 27 μm. In the low-temperature magnetoresistance trace of this sample, we observe high-order fractional quantum Hall states up to the Landau level filling ν = 12/25 near ν = 1/2. Furthermore, we see a deep minimum develop at ν = 1/5 in the magnetoresistance of a sample with a much lower hole density of p = 4.0 × 10 10 /cm 2 where we measure a mobility of 3.6 × 10 6 cm2/(V s). Finally, these improvements in sample quality were achieved by the reduction of residual impurities both in the GaAs channel and in the AlGaAs barrier material, as well as optimization in the design of the sample structure.

2-dimensional systems↗
DOE OSTI2021

Structural and physical properties of 99 complex bulk chalcogenides crystals using first-principles calculations

Chalcogenide semiconductors and glasses have many applications in the civil and military fields, especially in relation to their electronic, optical and mechanical properties for energy conversion and in enviormental materials. However, they are much less systemically studied and their fundamental physical properties for a large class chalcogenide semiconductors are rather scattered and incomplete. Here, we present a detailed study using well defined first-principles calculations on the electronic structure, interatomic bonding, optical, and mechanical properties for 99 bulk chalcogenides including thirteen of these crytals which have never been calculated. Due to their unique composition and structures, these 99 bulk chalcogenides are divided into two main groups. The first group contains 54 quaternary crystals with the structure composition (A2BCQ4) (A = Ag, Cu; B = Zn, Cd, Hg, Mg, Sr, Ba; C = Si, Ge, Sn; Q = S, Se, Te), while the second group contains scattered ternary and quaternary chalcogenide crystals with a more diverse composition (AxByCzQn) (A = Ag, Cu, Ba, Cs, Li, Tl, K, Lu, Sr; B = Zn, Cd, Hg, Al, Ga, In, P, As, La, Lu, Pb, Cu, Ag; C = Si, Ge, Sn, As, Sb, Bi, Zr, Hf, Ga, In; Q = S, Se, Te; x=1, 2, 3; y=0, 1, 2, 5; z=0, 1, 2 and n=3, 4, 5, 6, 9). Moreover, the total bond order density (TBOD) is used as a single quantum mechanical metric to characterize the internal cohesion of these crystals enabling us to correlate them with the calculated properties, especially their mechanical properties. This work provides a very large database for bulk chalcogenides crucial for the future theoretical and experimental studies, opening opportunities for study the properties and potential application of a wide variety of chalcogenides.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

REACTER 2.0: Quantum-Informed Reaction Constraints and Automated Interaction Typing

REACTER is a heuristic method for modeling chemical reactions in classical molecular dynamics simulations, implemented in LAMMPS as fix bond/react. The authors recently extended LAMMPS to support alphanumeric labels for atom types, bond types etc., which enables the pre- and post-reaction templates required by the REACTER protocol to be portable between different simulations and greatly simplifies the task of creating simulation-ready reaction templates. To further increase the generality of reaction templates, support for wildcard characters within atom types has been added, along with the automatic assignment of interaction types for new bonds, angles, etc. based on the involved atom types. In some cases, this feature can express a class of reactions with one pair of reaction templates, where previously dozens may have been required. Advanced reaction constraints have also been added, including an Arrhenius constraint to enforce an effective activation energy, a root-mean-square-deviation option for complex geometrical constraints, as well as a custom constraint that leverages LAMMPS’ powerful built-in variable framework. Other new features include variable support for various inputs (e.g., to allow reaction rates or cutoffs to be dependent on overall conversion), on-the-fly update of molecule IDs, and the ability to create new atoms positioned with respect to the reaction site. The new features are applied to modeling polymeric, thermosetting and composite materials, and advanced applications of the new reaction constraints are demonstrated. For example, REACTER is shown to accurately reproduce mechanically-induced bond breaking, as characterized by third-order DFT-based tight-binding (DFTB3) simulations, via a constraint on the total potential energy of the involved atoms.

polymer simulations↗
DOE OSTI2024

Theory for Equivariant Quantum Neural Networks

Quantum neural network architectures that have little to no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models encoding the symmetries of the learning task. This is materialized through the usage of equivariant neural networks the action of which commutes with that of the symmetry. In this work, we import these ideas to the quantum realm by presenting a comprehensive theoretical framework to design equivariant quantum neural networks (EQNNs) for essentially any relevant symmetry group. We develop multiple methods to construct equivariant layers for EQNNs and analyze their advantages and drawbacks. Our methods can find unitary or general equivariant quantum channels efficiently even when the symmetry group is exponentially large or continuous. As a special implementation, we show how standard quantum convolutional neural networks (QCNNs) can be generalized to group-equivariant QCNNs where both the convolution and pooling layers are equivariant to the symmetry group. We then numerically demonstrate the effectiveness of a S U ( 2 ) -equivariant QCNN over symmetry-agnostic QCNN on a classification task of phases of matter in the bond-alternating Heisenberg model. Our framework can be readily applied to virtually all areas of quantum machine learning. Lastly, we discuss about how symmetry-informed models such as EQNNs provide hopes to alleviate central challenges such as barren plateaus, poor local minima, and sample complexity. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Electric field control of magnetism: multiferroics and magnetoelectrics

This article is written on behalf of a large number of colleagues, collaborators, and researchers in the field of complex oxides as well as current and former students and postdocs who continue to enable and undertake cutting-edge research in the field of multiferroics, magnetoelectrics, and the pursuit of electric-field control of magnetism. What we present is something that is extremely exciting from both a fundamental science and applications perspective and has the potential to revolutionize our world. Needless to say, to realize this potential will require numerous new innovations, both in the fundamental science arena as well as translating these scientific discoveries into real applications. Thus, this article will attempt to bridge the gap between fundamental condensed-matter physics and the actual manifestations of the physical concepts into real-life applications. Here, we hope this article will help spur more translational research within the broad materials physics community.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Magnetic field dependence of low-energy magnons, anisotropic heat conduction, and spontaneous relaxation of magnetic domains in the cubic helimagnet ZnCr 2 Se 4

Here, anisotropic low-temperature properties of the cubic spinel helimagnet ZnCr 2 Se 4 in the single-domain spin-spiral state are investigated by a combination of neutron scattering, thermal conductivity, ultrasound velocity, and dilatometry measurements. In an applied magnetic field, neutron spectroscopy shows a complex and nonmonotonic evolution of the spin-wave spectrum across the quantum-critical point that separates the spin-spiral phase from the field-polarized ferromagnetic phase at high fields. A tiny spin gap of the pseudo-Goldstone magnon mode, observed at wave vectors that are structurally equivalent but orthogonal to the propagation vector of the spin helix, vanishes at this quantum critical point, restoring the cubic symmetry in the magnetic subsystem. The anisotropy imposed by the spin helix has only a minor influence on the lattice structure and sound velocity but has a much stronger effect on the heat conductivities measured parallel and perpendicular to the magnetic propagation vector. The thermal transport is anisotropic at T≲ 2K, highly sensitive to an external magnetic field, and likely results directly from magnonic heat conduction. We also report long-time thermal relaxation phenomena, revealed by capacitive dilatometry, which are due to magnetic domain motion related to the destruction of the single-domain magnetic state, initially stabilized in the sample by the application and removal of magnetic field. Our results can be generalized to a broad class of helimagnetic materials in which a discrete lattice symmetry is spontaneously broken by the magnetic order.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dispersion interactions in proposed covalent superhydride superconductors

Recent developments in high temperature superconducting materials under high pressure have made numerical evaluation of the superconducting transition temperature (T c ) of predicted materials critically important as a means to identification. Existing methods of calculating T c often do not agree with each other or experiment, often due to the large number of complex factors that contribute to this property; among them is the neglect of dispersion interactions in commonly used density functionals. We evaluate the effect of including dispersion interactions on the predicted superconducting properties of two examples of the covalent superhydride class of very high-T c superconducting materials. In both cases, dispersion is found to have sizable effects, increasing the electron-phonon coupling as compared to the reference case of elemental niobium. A detailed investigation traces the origin of this effect in a 270 GPa $\mathrm {R3m CSH_7}$ structure to structural distortions driven by long-range electron-phonon interactions rather than novel bonding networks.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗