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DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors

Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Effect of Socio-Political Factors in Nuclear Power Development in Multi-National Framework

Nuclear fuel cycle is a complex process with numerous steps influenced by both engineering and socio-economic factors. A number of studies have been done in this area providing a comprehensive view of nuclear fuel cycle in term of the energy scenarios, material flows, or pricing mechanism. However, the effect of other socio-economic aspects like public acceptance, proliferation risks, or the trans-boundary nature of the nuclear fuel cycle have not been investigated thoroughly. This research is an attempt to develop such a hybrid model of multinational nuclear fuel cycle using system dynamics and the quantifiable effects of political and social impacts on the development of nuclear fuel cycle through multi-attribute analysis of historical data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Phase Stability and Kinetics of Topotactic Dual Ca 2+ –Na + Ion Electrochemistry in NaSICON NaV 2 (PO 4 ) 3

Recent reports of reversible calcium plating and stripping have rekindled interest in the development of Ca-ion batteries (CIBs) as next-generation energy storage devices. This technology has the potential to overcome the limitations of conventional Li-ion batteries, but CIBs are plagued by a paucity of suitable cathode materials. To date, NaSICON-structured NaV 2 (PO 4 ) 3 has been demonstrated as a successful cathode candidate, exhibiting reversible (de)intercalation of 0.6 mol Ca 2+ along with stable cycling performance. However, a complex multiphase mixture forms on discharge so the Ca-ion charge storage mechanism in the NaSICON framework is poorly understood. Here in this work, we report on an investigation of the structure and/or Na + /Ca 2+ environment(s) of a variety of chemically prepared NaSICON Ca x Na y V 2 (PO 4 ) 3 phases which were characterized using synchrotron XRD, SEM-EDS, 23 Na NMR, and TEM. Highly calciated CaV 2 (PO 4 ) 3 , Ca 1.5 V 2 (PO 4 ) 3 , and CaNaV 2 (PO 4 ) 3 phases can be prepared at high temperature, but -unlike Ca 0.6 NaV 2 (PO 4 ) 3 -these materials are electrochemically inactive. To better understand the fundamental factors impacting successful Ca 2+ electrochemistry in this system, DFT was employed to examine the Ca x Na y V 2 (PO 4 ) 3 phase diagram and Ca 2+ diffusion mechanism. Theoretical insights show that phase separation into Na-rich and Ca-rich phases is a reason for the capacity limitation and demonstrate that Na + ions in the host materials assist the migration of neighboring Ca 2+ ions, enabling reversible electrochemistry in Ca x Na y V 2 (PO 4 ) 3 . This investigation of fundamental principles affecting reversible Ca 2+ (de)intercalation in Ca x Na y V 2 (PO 4 ) 3 allows for the development of design principles to enable the discovery of a variety of successful cathodes for CIBs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Probing charge transfer characteristics in a donor–acceptor metal–organic framework by Raman spectroelectrochemistry and pressure-dependence studies

The stimuli responsive behaviour of charge transfer donor–acceptor metal–organic frameworks (MOFs) remains an understudied phenomenon which may have applications in tuneable electronic materials. We now report the modification of donor–acceptor charge transfer characteristics in a semiconducting tetrathiafulvalene–naphthalene diimide-based MOF under applied electrochemical bias and pressure. Here, we employ a facile solid state in situ Raman spectroelectrochemical technique, applied for the first time in the characterisation of electroactive MOFs, to monitor the formation of a new complex TTFTC˙+–DPNI from a largely neutral system, upon electrochemical oxidation of the framework. In situ pressure-dependent Raman spectroscopy and powder X-ray diffraction experiments performed in a diamond anvil cell revealed blue shifts in the donor and acceptor vibrational modes in addition to contractions in the unit cell which are indicative of bond shortening. This study demonstrates the utility of in situ Raman spectroscopic techniques in the characterisation of redox-active MOFs and the elucidation of their electronic behaviours.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

domain adaptation↗

DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Large-scale GW calculations on pre-exascale HPC systems

The ab initio GW approach is a rigorous Green’s-function-based framework that can be employed to compute electronic excitation properties of a wide variety of materials such as extended systems, molecules, as well as confined and nanostructured materials with a very satisfactory accuracy. However, GW calculations on complex systems are often hindered by the high computational cost associated with the method. Here, in this study, we demonstrate how to significantly speedup GW calculations with a novel algorithm for the computationally intense kernel based on a non-blocking chunked cyclic communication scheme that minimizes latency in MPI messages, allows for overlapping of communication and computation, and improves cache usage in matrix multiplication operations. The optimized version of the code, implemented in the BerkeleyGW software package, is capable of scaling well to the full Cori computer at NERSC (Cray XC40) and achieves over 11 Peta FLOP/s of sustained performance. We showcase our work by performing large-scale GW calculations of defect structures on silicon, which require simulation cells containing over 1700 atoms, and which can now be efficiently executed in just a few minutes on large pre-exascale high-performance computing systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

An extended micromechanics method for probing interphase properties in polymer nanocomposites [An extended micromechanics method for overlapping geometries with application to polymer nanocomposites]

Inclusions comprised on filler particles and interphase regions commonly form complex morphologies in polymer nanocomposites. Addressing these morphologies as systems of overlapping simple shapes allows for the study of dilute particles, clustered particles, and interacting interphases all in one general modeling framework. To account for the material properties in these overlapping geometries, weighted-mean and additive overlapping conditions are introduced and the corresponding inclusion-wise integral equations are formulated. An extended micromechanics method based on these overlapping conditions for linear elastic and viscoelastic heterogeneous material is then developed. An important feature of the proposed approach is that the effect of both the geometric overlapping (clustered particles) and physical overlapping (interacting interphases) on the effective properties can be distinguished. Lastly, we apply the extended micromechanics method to a viscoelastic polymer nanocomposite with interphase regions, and estimate the properties and thickness of the interphase region based on experimental data for carbon-black filled styrene butadiene rubbers.

42 ENGINEERING↗

NASA NTRS1973

Gravitation

This textbook on gravitation physics (Einstein's general relativity or geometrodynamics) is designed for a rigorous full-year course at the graduate level. The material is presented in two parallel tracks in an attempt to divide key physical ideas from more complex enrichment material to be selected at the discretion of the reader or teacher. The full book is intended to provide competence relative to the laws of physics in flat space-time, Einstein's geometric framework for physics, applications with pulsars and neutron stars, cosmology, the Schwarzschild geometry and gravitational collapse, gravitational waves, experimental tests of Einstein's theory, and mathematical concepts of differential geometry.

Misner, C. W.↗

DOE OSTI2020

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1983

Complexities of high temperature metal fatigue: Some steps toward understanding

After pointing out many of the complexities that attend high temperature metal fatigue beyond those already studied in the sub-creep range, a description of the micromechanisms of deformation and fracture is presented for several classes of materials that were studied over the past dozen years. Strainrange Partitioning (SRP) is used as a framework for interpreting the results. Several generic types of behavior were observed with regard both to deformation and fracture and each is discussed in the context of the micromechanisms involved. Treatment of cumulative fatigue damage and the possibility of ""healing'' of damage in successive loading loops, has led to a new interpretation of the Interaction Damage Rule of SRP. Using the concept of ""equivalent micromechanistic damage'' -- that the same damage on a microscopic scale is induced if the same hysteresis loops are generated, element for element -- it turns out the Interaction Damage Rule essentially compounds a number of variants of hysteresis loops, all of which have the same damage according to SRP concepts, into a set of loops each containing only one of the generic SRP strainranges. Thus the damage associcated with complex loops comprising several types of strainrange is analyzed by considering a combination of loops each containing only one type of strainrange. This concept is expanded to show how several independent loops can combine to ""heal'' creep damage in a complex loading history.

Manson, S. S.↗

DOE OSTI2019

Parallel general purpose multiobjective optimization framework with application to electron beam dynamics

Particle accelerators are invaluable tools for research in the basic and applied sciences, such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a nontrivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. The Argonne Wakefield Accelerator facility has some unique challenges resulting from its purpose to carry out advanced accelerator R&D. Individual experiments often have challenging beam requirements, and the physical configuration of the beam lines is often changed to accommodate the variety of supported experiments. The need for rapid deployment of different operational settings further complicates the optimization work that must be done for multiple constraints and challenging operational regimes. One example of this is an independently staged two-beam acceleration experiment which requires the construction of an additional beam line (this is now in progress). The high charge drive beam, well into the space charge regime, must be threaded through small aperture (17.6 mm) decelerating structures. In addition, the bunch length must be sufficiently short to maximize power generation in the decelerator. We propose to tackle this problem by means of multiobjective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame, a fast and scalable software framework is required. In this paper, we present a general-purpose framework for simulation-based multiobjective optimization methods that allows the automatic investigation of optimal sets of machine parameters. Using evolutionary algorithms as the optimizer and opal as the forward solver, validation experiments and results of multiobjective optimization problems in the domain of beam dynamics are presented. Optimized solutions for the new high charge drive beam line found by the framework were used to finish the design of a two beam acceleration experiment. The selected solution along with the associated beam parameters is presented.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Microscopy↗

DOE OSTI2018

“Interpenetration Isomerism” of Triptycene-Based Hydrogen-Bonded Organic Frameworks

In this work, we describe an example of “interpenetration isomerism” in three-dimensional (3D) hydrogen-bonded organic frameworks (HOFs). By exploiting the crystallization conditions for a peripherally extended triptycene H6PET, we can modulate the interpenetration of the assembled frameworks, yielding a two-fold interpenetrated structure PETHOF-1 (acs-2c topology) and a five-fold interpenetrated structure PETHOF-2 (acs-5c topology) as “interpenetration isomers”. In PETHOF-1, two individual nets are related by inversion symmetry and form an interwoven topology with a strikingly large guest-accessible volume of about 80%. In PETHOF-2, five individual nets are related by translational symmetry and are stacked in an alternating fashion. Additionally, the activated materials show permanent porosity with high Brunauer–Emmett–Teller (BET) areas exceeding 1100 m2•g-1. Finally, we believe that synthetic control over the framework interpenetration can serve as a new design strategy to construct diverse and complex supramolecular architectures from simple organic building blocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Microstructural Materials Design Via Deep Adversarial Learning Methodology

Identifying the key microstructure representations is crucial for computational materials design (CMD). However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for microstructural materials design. Some MCR approaches are not applicable for microstructural materials design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for microstructural materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural materials design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

Engineering↗

DOE OSTI2018

Return on Investment (ROI) Framework Case Study: CTH.

CTH is a Eulerian code developed at Sandia National Laboratories capable of modeling the hydrodynamic response of explosives, liquids, gases, and solids. The code solves complex multi-dimensional problems characterized by large deformations and strong shocks that are composed of various material configurations. CTH includes models for material strength, fracture, porosity, and high explosive detonation and initiation. The code is an acronym for a complex series of names relating to its origin. A full explanation can be seen in Appendix A. The software breaks penetration simulations into millions of grid-like “cells”. As a modeled projectile impacts and penetrates a target, progressively smaller blocks of cells are placed around the projectile, which show in detail deformations and breakups. Additionally, the code is uniquely suited to modeling blunt impact and blast loading leading to human body injury.

42 ENGINEERING↗

DOE OSTI2023

ElectroCat: Platinum-Group-Metal-Free Engineered Framework Nano-Structure Catalyst (Final Technical Report)

Traditional synthesis methodologies that produce durable, highly active PGM-free electrocatalysts typically consist of a top-down approach utilizing a nitrogen-coordinating precursor, a metal precursor, and a support material. Nitrogen-coordinated transition metal complexes typically are absorbed onto a support material, which is then activated via high-temperature pyrolysis. Selection of the support and the nitrogen coordinating precursor has long been the focal point of efforts to maximize the number of accessible, catalytically active sites. In this project, GWE in collaboration with SRNL and NU will develop durable, highly active, low-cost, PGM-free electrocatalysts for polymer electrolyte membrane fuel cells designed and synthesized using a unique, bottom-up, rationally designed approach. Electrocatalysts are produced via polymerization reaction followed by a high-temperature pyrolysis step. Additionally, this project focuses on catalyst site and ORR mechanistic modeling to better understand the structure-property relationship.

08 HYDROGEN↗