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DOE OSTI2019

Density Functional Theory Study of Epitaxially Strained Monolayer Transition Metal Chalcogenides for Piezoelectricity Generation

Two-dimensional transition metal chalcogenides (2D TMCs) are known for their wide range of bandgaps, flexibility, and high strength. Recent synthesis and data mining efforts indicate that 56 2D TMCs have low exfoliation energies and are relatively stable in monolayer form. Under epitaxial strain, we predict using density functional theory (DFT) calculations that the majority of these 2D TMCs can accommodate ±10% strain without breaking their crystal symmetry. The elastic and piezoelectric tensors indicate that 22 of 56 candidates are piezoelectric, and we derive their in-plane piezoelectric coefficient d 11 . The epitaxial strain is further predicted to enhance the d 11 by over 100% at 10% tensile epitaxial strain for most of these piezoelectric 2D TMCs. ReSe 2 at pristine state and Au 2 Se 2 at +5% epitaxial strain are predicted to obtain the extreme d 11 coefficients at -120 and 326 pm/V, respectively. Lastly, these findings have implications for the use of high-performance 2D piezoelectric materials in devices.

2D materials↗

DOE OSTI2020

Effect of Molecular Weight and Layer Thickness on Dielectric Breakdown Strength of Neat and Homopolymer Swollen Lamellar Block Copolymer Films

Designing next-generation lightweight pulsed power devices hinges on understanding the factors influencing the energy storage performance of dielectric materials. Polymer dielectric films have a quadratic dependence of energy storage on the voltage breakdown strength and strategies to enhance the breakdown strength are expected to yield a path toward high energy storage densities. Highly stratified lamellar block copolymer (L-BCP) films of model polystyrene-b-polymethylmethacrylate (PS-b-PMMA) exhibited as much as ~50% enhancement in breakdown voltage (E BD ), (225 % increase in stored energy density, U~ E BD 2 ), compared to unordered as-cast L-BCP films. Such an energy density using amorphous polymer is on par with industry-standard semi-crystalline biaxially oriented polypropylene (BOPP), and as such a notable development in the field. This work develops a deeper understanding of the molecular mechanisms of E BD enhancement in L-BCP films, due to combination of interface formation and spatial isolation of the chain ends into segregated zones. We further confirm the role of chain ends in the breakdown process blending a low M n L-BCP with matched M n homopolymers to attain same layer spacing as neat L-BCP of higher M n . E BD shows a significant decrease at low homopolymer fractions due to increased net chain end density within swollen ordered L-BCP domains in wet-brush regime, followed by increased E BD due to layer thickness increase via segregated “interphase layer” formation by excess homopolymers. Notably, E BD of homopolymer swollen L-BCPs is always lower than neat L-BCP of the same domain spacing, due to overall adverse chain ends contribution from homopolymers. These findings provide important selection rules for L-BCPs for designing next-generation flexible electronics with high energy density solid-state BCP film capacitors.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Pendant Group Modifications Provide Graft Copolymer Silicones with Exceptionally Broad Thermomechanical Properties

Graft copolymers offer a versatile platform for the design of self-assembling materials; however, simple strategies for precisely and independently controlling the thermomechanical and morphological properties of graft copolymers remain elusive. Here, using a library of 92 polynorbornene-graft-polydimethylsiloxane (PDMS) copolymers, we discover a versatile backbone-pendant sequence-control strategy that addresses this challenge. Small structural variations of pendant groups, e.g., cyclohexyl versus n-hexyl, of small-molecule comonomers have dramatic impacts on order-to-disorder transitions, glass transitions, mechanical properties, and morphologies of statistical and block silicone-based graft copolymers, providing an exceptionally broad palette of designable materials properties. For example, statistical graft copolymers with high PDMS volume fractions yielded unbridged body-centered cubic morphologies that behaved as soft plastic crystals. By contrast, lamellae-forming graft copolymers provided robust, yet reprocessable silicone thermoplastics (TPs) with transition temperatures spanning over 160 °C and elastic moduli as high as 150 MPa despite being both unentangled and un-cross-linked. Altogether, this study reveals a new pendant-group-mediated self-assembly strategy that simplifies graft copolymer synthesis and enables access to a diverse family of silicone-based materials, setting the stage for the broader development of self-assembling materials with tailored performance specifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Technoeconomic Model and Pathway to <$2/kg Green Hydrogen Using Integrated Halide Perovskite Photoelectrochemical Cells

The cost of gray hydrogen produced via fossil fuel-based steam-methane reforming has led the U.S. Department of Energy to specify <$\$$2/kg H 2 as a target for commercially competitive green hydrogen generation methods. Integrated photoelectrochemical cells have been proposed as a solar-to-hydrogen conversion technology. In this paper we describe a technoeconomically feasible pathway to reaching <$\$$2/kg green H 2 using integrated photoelectrochemical cells with halide perovskite photoabsorbers, low-cost conductive barriers, and low precious metal-content catalysts in an aqueous, membrane-separated cell. A base-case solar-to-hydrogen conversion efficiency of 20%, stable lifetime of 10 years, and a combined electrocatalyst-plus-panel cost of $\$$50/m 2 enabled a levelized cost of hydrogen of $\$$2.43/kg, which dropped below $\$$2/kg with improved performance metrics including material cost, improvements in process design, or subsidies. We relate these metrics to lab-scale reports to recommend best research practices for scientists and funding agencies working at this intersection of photovoltaics, electrocatalysis, and surface science.

$1/kg↗

DOE OSTI2026

Neutron Reflectometry Reveals Diffusion in Contrast-Matched Brush Particle Bilayers

A material system for performing layer-spread experiments on brush particle bilayers is presented and used to determine the diffusion constant of brush particles in the melt state. Selective deuteration of the core and shell of organo-silica nanoparticles grafted with poly(methyl methacrylate) was used to match the scattering length density of the core and the polymer canopy layer. This subdued the scattering of particle cores (i.e., formfactor scattering) and enabled the analysis of the interdiffusion kinetics using neutron reflectivity. For low molecular grafts, i.e., grafts with a molecular weight below the entanglement limit, the interdiffusion kinetics revealed both a sub- and Fickian diffusion regime. The former was attributed to the local dynamics that was constrained by the slow-moving cores of neighboring brush particles that acted as long-lived physical cross-links. No transition to Fickian diffusion was observed for entangled systems, even at prolonged annealing times. This suggested a higher level of kinetic restraint in entangled brush particle melts as compared to, for example, star polymers with a comparable chain length for which Fickian diffusion has been reported under similar conditions.

Diffusion, Polymer Grafted Nanoparticles, Neutron ↗

DOE OSTI2023

Robust Interfacial Effect in Multi-interface Environment through Hybrid Reconstruction Chemistry for Enhanced Energy Storage

Electrochemical-oxidation-driven reconstruction has emerged as an efficient approach for developing advanced materials, but the reconstructed microstructure still faces challenges including inferior conductivity, unsatisfying intrinsic activity, and active-species dissolution. In this report we present hybrid reconstruction chemistry that synergistically couples electrochemical oxidation with electrochemical polymerization (EOEP) to overcome these constraints. During the EOEP process, the metal hydroxides undergo rapid reconstruction and dynamically couple with polypyrrole (PPy), resulting in an interface-enriched microenvironment. We observe that the interaction between PPy and the reconstructed metal center (i.e., Mn > Ni, Co) is strongly correlated. Theoretical calculation results demonstrate that the strong interaction between Mn sites and PPy breaks the intrinsic limitation of MnO 2 , rendering MnO 2 with a metallic property for fast charge transfer and enhancing the ion-adsorption dynamics. Operando Raman measurement confirms the promise of EOEP-treated Mn(OH) 2 (E-MO/PPy) to stably work under a 1.2 V potential window. The tailored E-MO/PPy exhibits a high capacitance of 296 F g –1 at a large current density of 100 A g –1 . Here our strategy presents breakthroughs in upgrading the electrochemical reconstruction technique, which enables both activity and kinetics engineering of electrode materials for better performance in energy-related fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Effects of Temperature Fluctuations on Surface Mobility of Atomic Steps and Oxidation Dynamics in High-Temperature Alloys

In contrast to the traditional perspective that thermal fluctuations are insignificant in surface dynamics, here we report their influence on surface reaction dynamics. Using real-time low-energy electron microscopy imaging of NiAl(100) under both vacuum and O 2 atmospheres, we demonstrate that transient temperature variations substantially alter the direction of atom diffusion between the surface and bulk, leading to markedly different oxidation outcomes. During heating, substantial outward diffusion of atoms from the bulk to the surface results in step growth. Conversely, cooling induces considerable inward diffusion of adatoms, producing a distinct oxide morphology. In both scenarios, initially formed oxide islands impede local atomic step mobility, thereby increasing step length due to mass transfer between the surface and bulk, with atomic steps acting as adatom sinks during heating and sources during cooling. Furthermore, we show that this pinning effect on atomic step mobility can be mitigated by applying persistent temperature fluctuations. As a result, understanding these nuances is vital for accurately predicting and dynamically manipulating the performance of active materials in various chemical processes under transient thermal conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Reversible Structural Phase Transition by Electrochemically-Driven Ion Injection into a Conjugated Polymer

We nd that conjugated polymers can undergo reversible structural phase transitions during electrochemical oxidation and ion injection. We study poly[2,5-bis(thiophenyl)-1,4-bis(2-(2-(2- methoxyethoxy)ethoxy)ethoxy)benzene] (PB2T-TEG), a conjugated polymer with glycolated side chains. Using grazing incidence wide angle X-ray scattering (GIWAXS), we show that, in contrast to previously known polymers, this polymer switches between two structurally distinct crystalline phases associated with electrochemical oxidation/reduction in an aqueous electrolyte. Importantly, we show that this unique phase change behavior has important physical consequences for ion transport. Notably, using moving front experi- ments visualized by both optical microscopy and super-resolution photoinduced force microscopy (PiFM), we show that a propagating ion front in PB2T-TEG exhibits non-Fickian transport, retaining a sharp step-edge prole, in stark contrast to the Fickian diusion more commonly observed. This structural phase transition is reminiscent of those accompanying ion uptake in inorganic materials like LiFePO4. We propose that engineering similar properties in future conjugated polymers may enable the realization of new materials with superior performance in electrochemical energy storage or neuromorphic memory applications.

Bischak, Connor G.↗

DOE OSTI2021

Plastic and low-cost axial zero thermal expansion alloy by a natural dual-phase composite

Zero thermal expansion (ZTE) alloys possess unique dimensional stability, high thermal and electrical conductivities. Their practical application under heat and stress is however limited by their inherent brittleness because ZTE and plasticity are generally exclusive in a single-phase material. Besides, the performance of ZTE alloys is highly sensitive to change of compositions, so conventional synthesis methods such as alloying or the design of multiphase to improve its thermal and mechanical properties are usually inapplicable. In this study, by adopting a one-step eutectic reaction method, we overcome this challenge. A natural dual-phase composite with ZTE and plasticity was synthesized by melting 4 atom% holmium with pure iron. The dual-phase alloy shows moderate plasticity and strength, axial zero thermal expansion, and stable thermal cycling performance as well as low cost. By using synchrotron X-ray diffraction, in-situ neutron diffraction and microscopy, the critical mechanism of dual-phase synergy on both thermal expansion regulation and mechanical property enhancement is revealed. These results demonstrate that eutectic reaction is likely to be a universal and effective method for the design of high-performance intermetallic-compound-based ZTE alloys.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Forming mechanism of equilibrium and non-equilibrium metallurgical phases in dissimilar aluminum/steel (Al–Fe) joints

Abstract Forming metallurgical phases has a critical impact on the performance of dissimilar materials joints. Here, we shed light on the forming mechanism of equilibrium and non-equilibrium intermetallic compounds (IMCs) in dissimilar aluminum/steel joints with respect to processing history (e.g., the pressure and temperature profiles) and chemical composition, where the knowledge of free energy and atomic diffusion in the Al–Fe system was taken from first-principles phonon calculations and data available in the literature. We found that the metastable and ductile (judged by the presently predicted elastic constants) Al 6 Fe is a pressure ( P ) favored IMC observed in processes involving high pressures. The MoSi 2 -type Al 2 Fe is brittle and a strong P -favored IMC observed at high pressures. The stable, brittle η-Al 5 Fe 2 is the most observed IMC (followed by θ-Al 13 Fe 4 ) in almost all processes, such as fusion/solid-state welding and additive manufacturing (AM), since η-Al 5 Fe 2 is temperature-favored, possessing high thermodynamic driving force of formation and the fastest atomic diffusivity among all Al–Fe IMCs. Notably, the ductile AlFe 3 , the less ductile AlFe, and most of the other IMCs can be formed during AM, making AM a superior process to achieve desired IMCs in dissimilar materials. In addition, the unknown configurations of Al 2 Fe and Al 5 Fe 2 were also examined by machine learning based datamining together with first-principles verifications and structure predictions. All the IMCs that are not P- favored can be identified using the conventional equilibrium phase diagram and the Scheil-Gulliver non-equilibrium simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Tuning valley degeneracy with band inversion

Valley degeneracy is a key feature of the electronic structure that benefits the thermoelectric performance of a material. Despite recent studies which claim that high valley degeneracy can be achieved with inverted bands, our analysis of rock-salt IV–VI compounds using first-principles calculations and k•p perturbation theory demonstrates that mere band inversion is an insufficient condition for high valley degeneracy; rather, there is a critical degree to which the bands must be inverted to induce multiple carrier pockets. The so-called “band inversion parameter” is formalized as a chemically-tunable property, offering a design route to achieving high valley degeneracy in compounds with inverted bands. In conclusion, we predict that the valley degeneracy of rock-salt IV–VI compounds can be increased from N V = 4 to N V = 24, which could result in a corresponding increase in the thermoelectric figure of merit zT .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Design of lightweight BCC multi-principal element alloys with enhanced hydrogen storage using a machine learning-driven genetic algorithm

Body-centered cubic (BCC) based multi-principal element alloy (MPEA) hydrides have demonstrated significant potential for compact and efficient hydrogen storage. In this work, we first leverage machine learning (ML) models to predict the hydrogen affinity, storage capacity and phase stability of BCC MPEAs, creating a unique hydrogen-to-metal (H/M) predictor for materials with unprecedented performance. We developed a metaheuristic optimizer high-throughput framework by interfacing ML models with a genetic algorithm for the accelerated search of {Mg, Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Nb, Mo} based lightweight BCC MPEAs with improved hydrogen storage characteristics. We report five new MPEAs with a predicted gravimetric hydrogen storage capacity of around 3.5 wt% or more, including Cr 0.09 Mg 0.73 Ti 0.18 (4.25 wt% H) and Cr 0.21 Nb 0.11 Ti 0.35 V 0.33 (3.5 wt% H). The electronic structure of the top-performing composition, Cr 0.09 Mg 0.73 Ti 0.18 , was analyzed using density functional theory (DFT) to understand the reasons for its improved hydrogen storage properties compared to TiFe (1.90 wt% H), LaNi 5 (1.37 wt% H) or BCC MPEAs like TiVNbCr (3.70 wt% H). Temperature-dependent molecular dynamics (MD) studies were further performed on optimized BCC MPEAs to qualitatively study hydrogen mobility and analyze the effect of different elemental composition on bulk hydrogen diffusion. Our findings demonstrate how a ML assisted genetic algorithm framework can be used for efficient search of stable, lightweight and cost-effective MPEAs while minimizing the need for expensive ab initio calculations.

DFT↗

DOE OSTI2020

Crystal structures and rotational dynamics of a two-dimensional metal halide perovskite (OA) 2 PbI 4

The extended charge carrier lifetime in metal halide perovskites is responsible for their excellent optoelectronic properties. Recent studies indicate that the superb device performance in these materials is intimately related to the organic cation dynamics. Here, we focus on the investigation of the two-dimensional hybrid perovskite, (C 8 H 17 NH 3 ) 2 PbI 4 (henceforth, OA + = C 8 H 17 NH 3 + ). Using elastic and quasielastic neutron scattering techniques and group theoretical analysis, we studied the structural phase transitions and rotational modes of the C 8 H 17 NH 3 + cation in (OA) 2 PbI 4 . Here, our results show that, in the high-temperature orthorhombic ( T > 310 K) phase, the OA + cation exhibits a combination of a twofold rotation of the NH 3 –CH 2 head group about the crystal c -axis with a characteristic relaxation time of ~6.2 ps, threefold rotations (C 3 ) of NH 3 and CH 3 terminal groups, and slow librations of the other atoms. Contrastingly, only the C 3 rotation is present in the intermediate-temperature orthorhombic (238 K < T < 310 K) and low-temperature monoclinic ( T < 238 K) phases.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Biomimetic composite architecture achieves ultrahigh rate capability and cycling life of sodium ion battery cathodes

Sodium ion batteries are an emerging candidate to replace lithium ion batteries in large-scale electrical energy storage systems due to the abundance and widespread distribution of sodium. Despite the growing interest, the development of high-performance sodium cathode materials remains a challenge. In particular, polyanionic compounds are considered as a strong cathode candidate owing to their better cycling stability, a flatter voltage profile, and stronger thermal stability compared to other cathode materials. Here, we report the rational design of a biomimetic bone-inspired polyanionic Na3V2(PO4)3-reduced graphene oxide composite (BI-NVP) cathode that achieves ultrahigh rate charging and ultralong cycling life in a sodium ion battery. At a charging rate of 1 C, BI-NVP delivers 97% of its theoretical capacity and is able to retain a voltage plateau even at the ultra-high rate of 200 C. It also shows long cycling life with capacity retention of 91% after 10 000 cycles at 50 C. The sodium ion battery cells with a BI-NVP cathode and Na metal anode were able to deliver a maximum specific energy of 350 W h kg−1 and maximum specific power of 154 kW kg−1. In situ and postmortem analyses of cycled BI-NVP (including by Raman and XRD spectra) HRTEM, and STEM-EELS, indicate highly reversible dilation–contraction, negligible electrode pulverization, and a stable NVP-reduced graphene oxide layer interface. The results presented here provide a rational and biomimetic material design for the electrode architecture for ultrahigh power and ultralong cyclability of the sodium ion battery full cells when paired with a sodium metal anode.

25 ENERGY STORAGE↗

DOE OSTI2023

Three-dimensional full-field velocity measurements in shock compression experiments using stereo digital image correlation

Shock compression plate impact experiments conventionally rely on point-wise velocimetry measurements based on laser-based interferometric techniques. This study presents an experimental methodology to measure the free surface full-field particle velocity in shock compression experiments using high-speed imaging and three-dimensional (3D) digital image correlation (DIC). The experimental setup has a temporal resolution of 100 ns with a spatial resolution varying from 90 to 200 μm/pixel. Experiments were conducted under three different plate impact configurations to measure spatially resolved free surface velocity and validate the experimental technique. First, a normal impact experiment was conducted on polycarbonate to measure the macroscopic full-field normal free surface velocity. Second, an isentropic compression experiment on Y-cut quartz–tungsten carbide assembly is performed to measure the particle velocity for experiments involving ramp compression waves. To explore the capability of the technique in multiaxial loading conditions, a pressure shear plate impact experiment was conducted to measure both the normal and transverse free surface velocities under combined normal and shear loading. The velocities measured in the experiments using digital image correlation are validated against previous data obtained from laser interferometry. Numerical simulations were also performed using established material models to compare and validate the experimental velocity profiles for these different impact configurations. The novel ability of the employed experimental setup to measure full-field free surface velocities with high spatial resolutions in shock compression experiments is demonstrated for the first time in this work.

47 OTHER INSTRUMENTATION↗