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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Atomic-scale mechanism of carbon nucleation from a deep crustal fluid by replica exchange reactive molecular dynamics simulation

Here we present a mechanistic model of carbon nucleation and growth from a fluid at elevated temperature (T) and pressure conditions, typical of those found in the shallow Earth’s lithosphere. Our model uses a replica exchange reactive molecular dynamics framework in which molecular configurations are swapped between adjacent T replica at regular intervals according to underlying statistical mechanics. This framework allows predicting complex molecular structures and thermodynamics while remaining computationally efficient. Here we simulate the reactivity of an unstable mixture of CO 2 and CH 4 at 1000 K and 1 GPa. We find that the path to thermodynamic equilibrium is initially entropy-driven, producing a diversity of short-lived species, including various alcohols with intermediate carbon oxidation states. Cyclic and polycyclic radicals that are sometimes resonance-stabilized form next and set the stage for carbon nucleation. The carbon exsolution process releases abundant water, is exothermic and starts with the nucleation of a large aggregate of hydrogenated graphene flakes from covalently bonded polycyclic units. The carbon backbone of this nucleus subsequently grows into a hydrogen-depleted fullerene-like structure, before evolving toward a partially bilayered graphene layer. Overall, our results show that the mechanism of graphitic C formation is certainly not bimolecular, and that it may involve a combination of key condensation and radical chain reactions. This will help understand the isotopic, and reactive characteristics of carbon-bearing fluids during their upward transit through the Earth’s mantle and crust. Moreover, the mechanistic insights outlined here present intriguing similarities with the process of soot and interstellar dust formation, which suggests that the widespread distribution of abiotic polyaromatic and graphitic material on Earth and beyond may reflect the prevalence of a fundamental chemical pathway.

58 GEOSCIENCES↗
DOE OSTI2025

An experimental and numerical investigation of HD diesel engine DOC efficiency in oxidizing NO to NO 2

Reducing pollutant emissions from heavy-duty (HD) diesel engines is critical due to their significant environmental impact, particularly concerning NOx emissions. Understanding and optimizing modern diesel oxidation catalyst (DOC) and selective catalytic reduction (SCR) performance is essential for improving exhaust aftertreatment (EAT) system efficiency to meet stringent emissions regulations. The oxidation of nitric oxide (NO) to nitrogen dioxide (NO 2 ) in DOC plays a key role in improving SCR efficiency in reducing NO x . This study investigates the DOC performance in oxidizing NO to NO2 and its impact on the SCR efficiency of a 2021 MY Navistar E39 HD diesel engine. The influence of engine speed, load, exhaust gas temperature, and composition on DOC efficiency is experimentally investigated. The relationships between DOC inlet temperature, oxygen availability and NO 2 /NO x ratio at the DOC inlet are examined to better understand their effects on the overall DOC efficiency. The results indicate that DOC NO oxidation efficiency is highly dependent on exhaust temperature, with optimal oxidation occurring within a specific temperature range (275-350°C). Below this threshold, the chemical reactions are kinetically limited, while at higher temperatures, thermodynamic constraints reduce the efficiency of DOC in oxidizing NO to NO 2 . The experimental data further reveal that the NO 2 /NO x ratio peaks at medium loads before declining at higher loads due to reduced residence time and mass transfer effects. Additionally, the SCR NO x conversion efficiency is significantly influenced by the NO 2 /NO x ratio, achieving peak performance when the NO 2 /NO x ratio approaches 0.5. A DOC chemistry model was developed and validated against the experimental data to predict DOC oxidation behavior under various operating conditions. The findings of this study provide insights into the interdependencies between DOC and SCR performance, contributing to the optimization of SCR systems for optimized NOx reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Planet Formation by Gas-assisted Accretion of Small Solids

We compute the accretion efficiency of small solids, with radii 1 cm ≤ R s ≤ 10 m, on planets embedded in gaseous disks. Planets have masses 3 ≤ M p ≤ 20 Earth masses (M ⊕ ) and orbit within 10 au of a solar mass star. Disk thermodynamics is modeled via 3D radiation-hydrodynamics calculations that typically resolve the planetary envelopes. Both icy and rocky solids are considered, explicitly modeling their thermodynamic evolution. The maximum efficiencies of 1 ≤ R s ≤ 100 cm particles are generally ≲10%, whereas 10 m solids tend to accrete efficiently or be segregated beyond the planet’s orbit. A simplified approach is applied to compute the accretion efficiency of small cores, with masses M p ≤ 1 M ⊕ and without envelopes, for which efficiencies are approximately proportional to $M^{2/3}_{p}$. The mass flux of solids, estimated from unperturbed drag-induced drift velocities, provides typical accretion rates dM p /dt ≲ 10 -5 M ⊕ yr -1 . In representative disk models with an initial gas-to-dust mass ratio of 70–100 and total mass of 0.05–0.06 M ⊙ , the solids’ accretion falls below 10 -6 M ⊕ yr -1 after 1–1.5 Myr. The derived accretion rates, as functions of time and planet mass, are applied to formation calculations that compute dust opacity self-consistently with the delivery of solids to the envelope. Assuming dust-to-solid coagulation times of ≈0.3 Myr and disk lifetimes of ≈3.5 Myr, heavy-element inventories in the range 3–7 M ⊕ require that ≈90–150 M ⊕ of solids cross the planet’s orbit. The formation calculations encompass a variety of outcomes, from planets a few times M ⊕ , predominantly composed of heavy elements, to giant planets. The peak luminosities during the epoch of the solids’ accretion range from ≈10 -7 to ≈10 -6 L ⊙ .

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1981

Thermodynamic limits for solar energy conversion by a quantum-thermal hybrid system

The limits are presented fo air mass 1.5 conditions. A maximum conversion efficiency of 74 percent is thermodynamically achievable for the quantum device operating at 3500 K and the heat engine in contact with a reservoir at 0 K. The efficiency drops to 56 percent for a cold reservoir at approximately room temperature conditions. Hybrid system efficiencies exceed 50 percent over receiver temperatures ranging from 1400 K to 4000 K, suggesting little benefit is gained in operating the system above 1400 K. The results are applied to a system consisting of a photovoltaic solar cell in series with a heat engine.

Byvik, C. E.↗
NASA NTRS1978

Computer Program for the Design and Off-Design Performance of Turbojet and Turbofan Engine Cycles

The rapid computer program is designed to be run in a stand-alone mode or operated within a larger program. The computation is based on a simplified one-dimensional gas turbine cycle. Each component in the engine is modeled thermo-dynamically. The component efficiencies used in the thermodynamic modeling are scaled for the off-design conditions from input design point values using empirical trends which are included in the computer code. The engine cycle program is capable of producing reasonable engine performance prediction with a minimum of computer execute time. The current computer execute time on the IBM 360/67 for one Mach number, one altitude, and one power setting is about 0.1 seconds. about 0.1 seconds. The principal assumption used in the calculation is that the compressor is operated along a line of maximum adiabatic efficiency on the compressor map. The fluid properties are computed for the combustion mixture, but dissociation is not included. The procedure included in the program is only for the combustion of JP-4, methane, or hydrogen.

Morris, S. J.↗
DOE OSTI2026

Assessing the Accuracy of Property Model Predictions for Cost Optimization of Desalination Technologies

Accurate modeling of seawater thermophysical and thermodynamic properties is critical for optimizing desalination processes. This study compares three seawater property models, a Reaktoro multicomponent model, the thermophysical seawater properties library from the Massachusetts Institute of Technology, and a simplified sodium chloride model, in the context of levelized cost of water (LCOW) minimization for reverse osmosis (RO) and mechanical vapor compression systems. Process simulations and cost optimizations reveal that although all three models yield comparable LCOW and specific energy consumption (SEC) estimates under baseline conditions, deviations among their predictions increase with salinity. Relative differences in LCOW and SEC reach up to 6% and 8%, respectively. RO results show greater variability due to differences in osmotic pressure predictions, which affect pressure constraints at high recoveries. Computational performance varies substantially; specifically, Reaktoro simulations are up to 28 times slower than empirical models due to their detailed equilibrium calculations. These results suggest that empirical models offer acceptable accuracy for routine desalination process design, while Reaktoro provides advantages in scenarios requiring detailed speciation, such as scaling or pH adjustment studies. These findings underscore the importance of selecting appropriate property models based on the modeling objective of desalination applications and motivate future work integrating thermodynamic rigor with empirical efficiency.

Physical and chemical properties↗
DOE OSTI2022

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computing chemical potentials with machine-learning-accelerated simulations to accurately predict thermodynamic properties of molten salts

The successful design and deployment of next-generation nuclear technologies heavily rely on thermodynamic data for relevant molten salt systems. However, the lack of accurate force fields and efficient methods has limited the quality of thermodynamic predictions from atomistic simulations. Here we propose an efficient free energy framework for computing chemical potentials, which is the central free energy quantity behind many thermodynamic properties. We accelerate our simulations without sacrificing accuracy by using machine learning interatomic potentials trained on density functional theory (DFT) data. Using lithium chloride as our model system, we compute chemical potentials with DFT-accuracy for solid and liquid phases by transmuting ions into noninteracting particles. Notably, in the liquid phase, we demonstrate consistency whether we transmute one ion pair or the entire system into ideal gas particles. By locating the temperature where the chemical potential of solid and liquid phases cross, we predict a melting point of 880 ± 18 K for lithium chloride, which is remarkably close to the experimental value of 883 K. With this successful demonstration, we lay the foundation for high-throughput thermodynamic predictions of many properties that can be derived from the chemical potentials of the minority and majority components in molten salts.

Gibson, Luke D. [Oak Ridge National Laboratory (OR↗
DOE OSTI2020

Soft Lattice and Defect Covalency Rationalize Tolerance of β‐CsPbI 3 Perovskite Solar Cells to Native Defects

Abstract Although all‐inorganic metal halide perovskites (MHPs) have shown tremendous improvement, they are still inferior to the hybrid organic–inorganic MHPs in efficiency. Recently, a conceptually new β‐CsPbI 3 perovskite reached 18.4 % efficiency combined with good thermodynamic stability at ambient conditions. We use ab initio non‐adiabatic molecular dynamics to show that native point defects in β‐CsPbI 3 are generally benign for nonradiative charge recombination, regardless of whether they introduce shallow or deep trap states. These results indicate that MHPs do not follow the simple models used to explain defect‐mediated charge recombination in the conventional semiconductors. The strong tolerance is due to the softness of the perovskite lattice, which permits separation of electrons and holes upon defect formation, and only allows carriers to couple to the low‐frequency vibrations. Both factors decrease notably the non‐adiabatic coupling and slow down the dissipation of energy to heat.

Chu, Weibin↗
DOE OSTI2022

Tuning Enzyme Thermostability via Computationally Guided Covalent Stapling and Structural Basis of Enhanced Stabilization

Enhancing the thermostability of enzymes without impacting their catalytic function represents an important yet challenging goal in protein engineering and biocatalysis. We recently introduced a novel method for enzyme thermostabilization that relies on the computationally guided installation of genetically encoded thioether “staples” into a protein via cysteine alkylation with the noncanonical amino acid O-2-bromoethyl tyrosine (O2beY). Here, we demonstrate the functionality of an expanded set of electrophilic amino acids featuring chloroacetamido, acrylamido, and vinylsulfonamido side-chain groups for protein stapling using this strategy. Using a myoglobin-based cyclopropanase as a model enzyme, our studies show that covalent stapling with pchloroacetamido-phenylalanine (pCaaF) provides higher stapling efficiency and enhanced stability (thermodynamic and kinetic) compared to the other stapled variants and the parent protein. Interestingly, molecular simulations of conformational flexibility of the cross-links show that the pCaaF staple allows fewer energetically feasible conformers than the other staples, and this property may be a broader indicator of stability enhancement. Using this strategy, pCaaF-stapled variants with significantly enhanced stability against thermal denaturation (ΔT m ' = +27 °C) and temperature-induced heme loss (ΔT 50 = +30 °C) were obtained while maintaining high levels of catalytic activity and stereoselectivity. Crystallographic analyses of singly and doubly stapled variants provide key insights into the structural basis for stabilization, which includes both direct interactions of the staples with protein residues and indirect interactions through adjacent residues involved in heme binding. This work expands the toolbox of protein stapling strategies available for protein stabilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Strong mutual increase in the efficiency of isotope-selective laser IR dissociation of molecules under nonequilibrium thermodynamic conditions of the compression shock under irradiation in a bimolecular mixture

We have revealed a strong (by a factor of 2 to 5) mutual increase in the yield of IR molecular dissociation (by the example of CF{sub 2}HCl and CF{sub 3}Br) and a significant (by a factor of 1.5 to 3) lowering of dissociation thresholds in the nonequilibrium thermodynamic conditions of compression shock in the irradiation of the molecules by resonance IR laser radiation in the bimolecular mixture in comparison with their individual irradiation. This opens up the possibility to perform efficient isotope-selective IR dissociation of molecules at lower excitation energy densities (Φ ⩽ 1.5 – 2.0 J cm{sup −2}) and thereby to improve the dissociation selectivity. This was demonstrated by the example of chlorine- and bromine-isotope selective dissociation of the specified molecules, which are characterised by quite small (less than 0.25 cm{sup −1}) isotope shifts in the IR vibrational absorption spectra excited by laser radiation. The enrichment coefficients K {sub enr}({sup 35}Cl / {sup 37}Cl) = 0.90 ± 0.05 in the residual CF{sub 2}HCl gas and K {sub enr}({sup 79}Br / {sup 81}Br) in the resultant Br{sub 2} product are obtained when the CF{sub 2}HCl : CF{sub 3}Br = 1 : 1 molecular mixture and CF{sub 3}Br molecules, respectively, are irradiated by the 9R(30) CO{sub 2} laser line (frequency, 1084.635 cm{sup −1}) at an energy density Φ ≈ 1.3 J cm{sup −2}. (laser isotope separation)

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predicting Thermochemical Equilibria with Interacting Defects: Sr 1 − x Ce x Mn O 3 − δ Alloys for Water Splitting

Solar thermochemical hydrogen is one of the few potential routes towards direct fuel production from renewable energy sources, but the thermodynamic boundary conditions for efficient and economic energy conversion are challenging. Success or failure of a given oxide working material depends on the subtle balance between enthalpy and entropy contributions in the redox processes. Developing a mechanistic understanding of the behavior of materials on the basis of atomistic models and first-principles calculations is an important part of advancing the technology. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox-active defects start to interact with each other, thereby impeding the formation of additional defects. This problem is of more general importance to applications that rely on high levels of off-stoichiometry or doping, including, for example, batteries, thermoelectrics, and ceramic fuel cells. To account for such repulsive defect interactions, we introduce a statistical mechanics approach, defining an expression for the free energy of defect interaction based on limited sampling of defect configurations in density functional theory supercell calculations. The parameterization of this energy contribution as a function of defect concentration and temperature allows on-the-fly simulation of thermochemical equilibria. The approach consistently incorporates finite temperature effects by including the leading contributions to the temperature-dependent free energy for the case at hand, i.e., the ideal gas and configurational enthalpies and entropies. We demonstrate the capability and utility of the approach by simulating the water splitting redox processes for Sr 1 − x Ce x Mn O 3 − δ alloys. Published by the American Physical Society 2024

08 HYDROGEN↗
DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

High Throughput Computational Framework of Materials Properties for Extreme Environments

This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).

, Ni-based superalloys↗
NASA NTRS2020

Computational Design of Eutectic Molten Salt Mixtures: What Can Thermodynamic Models Do?

In the search for efficient energy storage battery technologies, designing stable electrolytes has been a long-standing challenge. Electrolytes based on molten salt eutectics are known for their stability with minimum parasitic reactions when compared to their widely used organic counterparts. However, the operating temperatures of these molten salt electrolyte-based batteries are dictated by the melting point of the eutectic mixtures. Design and high throughput screening of low melting temperature eutectic molten salt mixtures have been hindered by the lack of computational models. In this work, we develop thermodynamic models to predict the eutectic points of several molten salt mixtures. The framework of the COSMO-SAC model is used for the predictions and is compared with experimental data and other thermodynamic approaches. Rapid thermodynamics-based approaches, as shown in this study, can accelerate the discovery of new materials, complementing experimental techniques.

Ashwin Ravichandran↗
DOE OSTI2021

Design of Efficient Molecular Electrocatalysts for Water and Carbon Dioxide Reduction Using Predictive Models of Thermodynamic Properties

Objectives: 1) To design and synthesize water soluble transition metal complexes that function as electrocatalysts for the reduction of water to hydrogen or carbon dioxide to formate 2) To experimentally measure thermochemical properties, such as hydride donor ability, and use these quantities to optimize catalyst performance in solutions of varying pH 3) To incorporate results from detailed mechanistic and kinetic studies to direct improvements in catalyst design 4) To use thermochemical data from objective 2 to generate predictive models for the properties of new complexes to apply to the development of catalysts for other reductive reactions, with an emphasis on carbon dioxide reduction to methanol Description: Wide-spread implementation of renewable but intermittent energy sources, such as solar, requires the development of efficient methods for energy storage. The high energy density of chemical bonds makes chemical fuels an ideal solution for energy storage. Hydrogen and reduced carbon compounds have been proposed as ideal candidates for chemical fuels. However, the generation of chemical fuels from electricity requires competent electrocatalysts. The proposed research focuses on developing electrocatalysts for the reduction of water to hydrogen, and carbon dioxide to formate. Both products can be used directly as an energy carrier in fuel cells, or as a reductant for more saturated chemical fuels. Hydrogen is the most common fuel used in current commercial fuel cells. Additionally, there is interest in formic acid as a liquid carrier for hydrogen because of its increased storage density and ease of dehydrogenation. Formate is also an intermediate in the sequential reduction of CO 2 to methanol, another potential chemical fuel with high energy storage density. Despite the utility of formate as a chemical fuel or precursor, there are very few examples of electrocatalysts for its production from CO 2 , and even fewer demonstrate high product selectivity. In heterogeneous catalysis, the Sabatier principle is used to generate volcano curves that describe optimal thermochemical properties for key intermediates that result in peak catalytic activity. In most cases, such as hydrogen production and oxidation, the most favorable metals (Pt, Re, Rh, and Ir) are rare and expensive. Molecular inorganic complexes provide an opportunity to use electronic and steric ligand effects to tune the critical thermodynamic parameters of abundant metals to values comparable to key surface intermediates on precious metals. This principle will be applied to the design of aqueous homogeneous catalysts for the reduction of H 2 O and CO 2 optimized to function at specific pH ranges. The critical intermediate in both of these reactions is a metal hydride. The strength of this bond, or hydricity (ΔGH - ) dictates the overall thermodynamics of the reduction of H + to H 2 and the sequential reduction of C1 substrates, such as CO 2 , CO, and H 2 CO (the latter to CH 3 OH). ΔGH - will be systematically measured for a series of first row metal complexes to form predictive models for metal and ligand electronic effects. Benefits and Outcomes: This approach addresses thermodynamic requirements essential for energy-efficient catalyst design and transition state barrier considerations for faster catalysis. The advancements made from this research will include the discovery of efficient, stable, and fast catalysts for hydrogen and formate generation from electricity. The ability to store energy efficiently is critical to the widespread use of renewable energy schemes. This provides a viable solution to produce high energy density fuels for stationary and mobile energy applications. The fundamental research conducted is also directly applicable to the design of catalysts for the generation of further reduced products such as methanol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential

Thermodynamic phase stability of three elemental boron allotropes, i.e., α-B, β-B, and γ-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP potentials trained with datasets from on-the-fly active learning achieve quantum mechanical level accuracy when employed in molecular dynamics simulations to predict wide-ranging thermodynamic, structural, and vibrational properties. The simulated phase diagram (500~1400 K and 0~16 GPa) agrees with experimental measurements. The SGP-based MD simulations also successfully predicted that the B13 defect is critical in stabilizing β-B below 700 K. At higher temperatures, the entropy becomes the dominant factor, making β-B the more stable phase over α-B. Furthermore, this Letter demonstrates that SGP potentials based on a training set consisting of defect-free-only systems could make correct predictions of defect-related phenomena in solid-state crystals, paving the path to investigate crystal phase stability and transitions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗