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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Cryogenics and Fluids at NASA-Goddard Space Flight Center

Abstract: Spacecraft thermal design presents unique thermal and fluid challenges caused by a variety of harsh spacecraft thermal environments. In addition, at cryogenic temperatures (i.e., less than 123 K (-150° C), and sometimes as low as 50 milliKelvin), more challenges arise, such as the increasing importance of mitigating thermal radiation, the effects of coefficient of thermal expansion mismatches between different materials, changes in the physics behavior of materials, and increasing thermodynamic difficulty of lifting heat up large temperature gradients. In this talk, Dr. Rueger will present a broad overview of spacecraft thermal environments; cryogenic design practices; components of interest for the Cryogenics and Fluids Branch (e.g., cryocoolers, adiabatic demagnetization refrigerators, heat switches, and superconducting leads); cryogenic considerations in some current NASA mission examples (e.g., the DragonFly mission to Titan and the X-ray Imaging and Spectroscopy Mission); and capabilities of the Cryogenics and Fluids Branch at NASA.

Paul Rueger↗
DOE OSTI2020

A massively parallel and scalable multi-CPU material point method

Harnessing the power of modern multi-GPU architectures, we present a massively parallel simulation system based on the Material Point Method (MPM) for simulating physical behaviors of materials undergoing complex topological changes, self-collision, and large deformations. Our system makes three critical contributions. First, we introduce a new particle data structure that promotes coalesced memory access patterns on the GPU and eliminates the need for complex atomic operations on the memory hierarchy when writing particle data to the grid. Second, we propose a kernel fusion approach using a new Grid-to-Particles-to-Grid (G2P2G) scheme, which efficiently reduces GPU kernel launches, improves latency, and significantly reduces the amount of global memory needed to store particle data. Finally, we introduce optimized algorithmic designs that allow for efficient sparse grids in a shared memory context, enabling us to best utilize modern multi-GPU computational platforms for hybrid Lagrangian-Eulerian computational patterns. We demonstrate the effectiveness of our method with extensive benchmarks, evaluations, and dynamic simulations with elastoplasticity, granular media, and fluid dynamics. In comparisons against an open-source and heavily optimized CPU-based MPM codebase [Fang et al. 2019] on an elastic sphere colliding scene with particle counts ranging from 5 to 40 million, our GPU MPM achieves over 100x per-time-step speedup on a workstation with an Intel 8086K CPU and a single Quadro P6000 GPU, exposing exciting possibilities for future MPM simulations in computer graphics and computational science. Moreover, compared to the state-of-the-art GPU MPM method [Hu et al. 2019a], we not only achieve 2x acceleration on a single GPU but our kernel fusion strategy and Array-of-Structs-of-Array (AoSoA) data structure design also generalizes to multi-GPU systems. Our multi-GPU MPM exhibits near-perfect weak and strong scaling with 4 GPUs, enabling performant and large-scale simulations on a 10243 grid with close to 100 million particles with less than 4 minutes per frame on a single 4-GPU workstation and 134 million particles with less than 1 minute per frame on an 8-GPU workstation.

Wang, Xinlei↗
DOE OSTI2025

Reward Driven Workflows for Unsupervised Explainable Analysis of Phases and Ferroic Variants From Atomically Resolved Imaging Data

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, the effects of descriptors and hyperparameters are explored on the capability of unsupervised ML methods to distill local structural information, exemplified by the discovery of polarization and lattice distortion in Sm − dopped BiFeO 3 (BFO) thin films. It is demonstrated that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards are designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows the discovery of local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. The reward driven workflow is further extended to disentangle structural factors of variation via an optimized variational autoencoder (VAE). Lastly, the importance of well-defined rewards is explored as a quantifiable measure of the success of the workflow.

Barakati, Kamyar [University of Tennessee, Knoxvil↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗
NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗
DOE OSTI2023

Shock behavior of materials

The physics governing the response of materials to shock loading has many applications in various fields, ranging from Earth and planetary science to engineering and ballistics. Elucidating the behavior of materials experiencing the passage of shock waves motivates active research worldwide, including experimental developments with in situ and time-resolved measurements, modern computational approaches, and multi-scale simulations.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Materials Science Research Rack Onboard the International Space Station Hardware and Operations

The Materials Science Research Rack (MSRR) is a research facility developed under a cooperative research agreement between NASA and ESA for materials science investigations on the International Space Station (ISS). MSRR was launched on STS-128 in August 2009, and is currently installed in the U.S. Destiny Laboratory Module. Since that time, MSRR has performed virtually flawlessly, logging more than 620 hours of operating time. The MSRR accommodates advanced investigations in the microgravity environment on the ISS for basic materials science research in areas such as solidification of metals and alloys. The purpose is to advance the scientific understanding of materials processing as affected by microgravity and to gain insight into the physical behavior of materials processing. MSRR allows for the study of a variety of materials including metals, ceramics, semiconductor crystals, and glasses. Materials science research benefits from the microgravity environment of space, where the researcher can better isolate chemical and thermal properties of materials from the effects of gravity. With this knowledge, reliable predictions can be made about the conditions required on Earth to achieve improved materials. MSRR is a highly automated facility with a modular design capable of supporting multiple types of investigations. Currently the NASA-provided Rack Support Subsystem provides services (power, thermal control, vacuum access, and command and data handling) to the ESA developed Materials Science Laboratory (MSL) which accommodates interchangeable Furnace Inserts (FI). Two ESA-developed FIs are presently available on the ISS: the Low Gradient Furnace (LGF) and the Solidification and Quenching Furnace (SQF). Sample-Cartridge Assemblies (SCAs), each containing one or more material samples, are installed in the FI by the crew and can be processed at temperatures up to 1400 C. Once an SCA is installed, the experiment can be run by automatic command or science conducted via telemetry commands from the ground. Initially, 12 SCAs were processed in the first furnace insert for a team of European and US investigators. After these samples were processed the Furnaces Inserts were exchanged and an additional single sample was processed. The processed samples have been returned to Earth for evaluation and comparison of their properties to samples similarly processed on the ground. A preliminary examination of the samples indicates that the majority of the desired science objectives have been successfully met leading to significant improvements in the understanding of alloy solidification processes. Six SCAs were launched on Space Shuttle Mission STS-135 in July 2011 for processing during the Fall of 2011. Additional batches are planned for future processing. This facility is available to support additional materials science investigations through programs such as the US National Laboratory, Technology Development, NASA Research Announcements, and others.

Lehman, John R.↗
NASA NTRS2013

Materials Science Research Rack Onboard the International Space Station

The Materials Science Research Rack (MSRR) is a research facility developed under a cooperative research agreement between NASA and ESA for materials science investigations on the International Space Station (ISS). MSRR was launched on STS-128 in August 2009 and currently resides in the U.S. Destiny Laboratory Module. Since that time, MSRR has logged more than 1000 hours of operating time. The MSRR accommodates advanced investigations in the microgravity environment on the ISS for basic materials science research in areas such as solidification of metals and alloys. The purpose is to advance the scientific understanding of materials processing as affected by microgravity and to gain insight into the physical behavior of materials processing. MSRR allows for the study of a variety of materials, including metals, ceramics, semiconductor crystals, and glasses. Materials science research benefits from the microgravity environment of space, where the researcher can better isolate chemical and thermal properties of materials from the effects of gravity. With this knowledge, reliable predictions can be made about the conditions required on Earth to achieve improved materials. MSRR is a highly automated facility with a modular design capable of supporting multiple types of investigations. The NASA-provided Rack Support Subsystem provides services (power, thermal control, vacuum access, and command and data handling) to the ESA-developed Materials Science Laboratory (MSL) that accommodates interchangeable Furnace Inserts (FI). Two ESA-developed FIs are presently available on the ISS: the Low Gradient Furnace (LGF) and the Solidification and Quenching Furnace (SQF). Sample Cartridge Assemblies (SCAs), each containing one or more material samples, are installed in the FI by the crew and can be processed at temperatures up to 1400C. ESA continues to develop samples with 14 planned for launch and processing in the near future. Additionally NASA has begun developing SCAs to support US PIs and their partners. The first of these Flight SCAs are being developed for investigations to support research in the areas of crystal growth and liquid phase sintering. Subsequent investigations are in various stages of development. US investigations will include a ground test program in order to distinguish the particular effects of the absence of gravity.

Reagan, Shawn↗
NASA NTRS2013

Materials Science Research Rack Onboard the International Space Station

The Materials Science Research Rack (MSRR) is a research facility developed under a cooperative research agreement between NASA and ESA for materials science investigations on the International Space Station (ISS). MSRR was launched on STS-128 in August 2009 and currently resides in the U.S. Destiny Laboratory Module. Since that time, MSRR has logged more than 1000 hours of operating time. The MSRR accommodates advanced investigations in the microgravity environment on the ISS for basic materials science research in areas such as solidification of metals and alloys. The purpose is to advance the scientific understanding of materials processing as affected by microgravity and to gain insight into the physical behavior of materials processing. MSRR allows for the study of a variety of materials, including metals, ceramics, semiconductor crystals, and glasses. Materials science research benefits from the microgravity environment of space, where the researcher can better isolate chemical and thermal properties of materials from the effects of gravity. With this knowledge, reliable predictions can be made about the conditions required on Earth to achieve improved materials. MSRR is a highly automated facility with a modular design capable of supporting multiple types of investigations. The NASA-provided Rack Support Subsystem provides services (power, thermal control, vacuum access, and command and data handling) to the ESA-developed Materials Science Laboratory (MSL) that accommodates interchangeable Furnace Inserts (FI). Two ESA-developed FIs are presently available on the ISS: the Low Gradient Furnace (LGF) and the Solidification and Quenching Furnace (SQF). Sample Cartridge Assemblies (SCAs), each containing one or more material samples, are installed in the FI by the crew and can be processed at temperatures up to 1400C. ESA continues to develop samples with 14 planned for launch and processing in the near future. Additionally NASA has begun developing SCAs to support US PIs and their partners. The first of these Flight SCAs are being developed for investigations to support research in the areas of crystal growth and liquid phase sintering. Subsequent investigations are in various stages of development. US investigations will include a ground test program in order to distinguish the particular effects of the absence of gravity.

Reagan, Shawn↗
NASA NTRS2016

Materials Science Research Rack Onboard the International Space Station

The Materials Science Research Rack (MSRR) is a research facility developed under a cooperative research agreement between NASA and ESA for materials science investigations on the International Space Station (ISS). MSRR was launched on STS-128 in August 2009 and currently resides in the U.S. Destiny Laboratory Module. Since that time, MSRR has logged more than 1400 hours of operating time. The MSRR accommodates advanced investigations in the microgravity environment on the ISS for basic materials science research in areas such as solidification of metals and alloys. The purpose is to advance the scientific understanding of materials processing as affected by microgravity and to gain insight into the physical behavior of materials processing. MSRR allows for the study of a variety of materials, including metals, ceramics, semiconductor crystals, and glasses. Materials science research benefits from the microgravity environment of space, where the researcher can better isolate chemical and thermal properties of materials from the effects of gravity. With this knowledge, reliable predictions can be made about the conditions required on Earth to achieve improved materials. MSRR is a highly automated facility with a modular design capable of supporting multiple types of investigations. The NASA-provided Rack Support Subsystem provides services (power, thermal control, vacuum access, and command and data handling) to the ESA-developed Materials Science Laboratory (MSL) that accommodates interchangeable Furnace Inserts (FI). Two ESA-developed FIs are presently available on the ISS: the Low Gradient Furnace (LGF) and the Solidification and Quenching Furnace (SQF). Sample Cartridge Assemblies (SCAs), each containing one or more material samples, are installed in the FI by the crew and can be processed at temperatures up to 1400C. ESA continues to develop samples with 14 planned for launch and processing in the near future. Additionally NASA has begun developing SCAs to support US PIs and their partners. The first of these Flight SCAs are being developed for investigations to support research in the areas of crystal growth and liquid phase sintering. Subsequent investigations are in various stages of development. US investigations will include a ground test program in order to distinguish the particular effects of the absence of gravity.

Reagan, Shawn↗
NASA NTRS2016

Materials Science Research Rack Onboard the International Space Station

The Materials Science Research Rack (MSRR) is a research facility developed under a cooperative research agreement between NASA and ESA for materials science investigations on the International Space Station (ISS). MSRR was launched on STS-128 in August 2009 and currently resides in the U.S. Destiny Laboratory Module. Since that time, MSRR has logged more than 1400 hours of operating time. The MSRR accommodates advanced investigations in the microgravity environment on the ISS for basic materials science research in areas such as solidification of metals and alloys. The purpose is to advance the scientific understanding of materials processing as affected by microgravity and to gain insight into the physical behavior of materials processing. MSRR allows for the study of a variety of materials, including metals, ceramics, semiconductor crystals, and glasses. Materials science research benefits from the microgravity environment of space, where the researcher can better isolate chemical and thermal properties of materials from the effects of gravity. With this knowledge, reliable predictions can be made about the conditions required on Earth to achieve improved materials. MSRR is a highly automated facility with a modular design capable of supporting multiple types of investigations. The NASA-provided Rack Support Subsystem provides services (power, thermal control, vacuum access, and command and data handling) to the ESA-developed Materials Science Laboratory (MSL) that accommodates interchangeable Furnace Inserts (FI). Two ESA-developed FIs are presently available on the ISS: the Low Gradient Furnace (LGF) and the Solidification and Quenching Furnace (SQF). Sample Cartridge Assemblies (SCAs), each containing one or more material samples, are installed in the FI by the crew and can be processed at temperatures up to 1400degC. ESA continues to develop samples with 14 planned for launch and processing in the near future. Additionally NASA has begun developing SCAs to support US PIs and their partners. The first of these Flight SCAs are being developed for investigations to support research in the areas of crystal growth and liquid phase sintering. Subsequent investigations are in various stages of development. US investigations will include a ground test program in order to distinguish the particular effects of the absence of gravity.

Reagan, S. E.↗
NASA NTRS2010

Reliability Quantification of Advanced Stirling Convertor (ASC) Components

The Advanced Stirling Convertor, is intended to provide power for an unmanned planetary spacecraft and has an operational life requirement of 17 years. Over this 17 year mission, the ASC must provide power with desired performance and efficiency and require no corrective maintenance. Reliability demonstration testing for the ASC was found to be very limited due to schedule and resource constraints. Reliability demonstration must involve the application of analysis, system and component level testing, and simulation models, taken collectively. Therefore, computer simulation with limited test data verification is a viable approach to assess the reliability of ASC components. This approach is based on physics-of-failure mechanisms and involves the relationship among the design variables based on physics, mechanics, material behavior models, interaction of different components and their respective disciplines such as structures, materials, fluid, thermal, mechanical, electrical, etc. In addition, these models are based on the available test data, which can be updated, and analysis refined as more data and information becomes available. The failure mechanisms and causes of failure are included in the analysis, especially in light of the new information, in order to develop guidelines to improve design reliability and better operating controls to reduce the probability of failure. Quantified reliability assessment based on fundamental physical behavior of components and their relationship with other components has demonstrated itself to be a superior technique to conventional reliability approaches based on utilizing failure rates derived from similar equipment or simply expert judgment.

Shah, Ashwin R.↗
NASA NTRS2013

Commercial Platforms Allow Affordable Space Research

At an altitude of about 240 miles, its orbital path carries it over 90 percent of the Earth s population. It circles the Earth in continuous free fall; its crew of six and one Robonaut pass the days, experiencing 16 sunrises and 16 sunsets every 24 hours, in microgravity, an environment in which everything from bodily functions to the physical behavior of materials changes drastically from what is common on the ground. Outside its shielded confines, temperatures cycle from one extreme to the other, radiation is rampant, and atomic oxygen corrodes everything it touches. A unique feat of engineering, the International Space Station (ISS) also represents the most remarkable platform for scientific research ever devised. In 2005, anticipating the space station s potential for NASA and non-NASA scientists alike, the NASA Authorization Act designated the US segment of the ISS as a national laboratory, instructing the Agency to "increase the utilization of the ISS by other Federal entities and the private sector." With the ISS set to maintain operations through at least 2020, the station offers an unprecedented long-term access to space conditions, enabling research not previously possible. "There will be new drug discoveries, new pharmaceuticals, a better understanding of how we affect the planet and how we can maintain it," says Marybeth Edeen, the ISS National Laboratory manager, based at Johnson Space Center. The ISS, she says, represents a major example of the government s role in making such advancements possible. "The government is key in that researchers cannot afford to build the kind of infrastructure that the government can provide. But we then have to make that infrastructure available at a reasonable cost." Enter Jeff Manber, who saw in the ISS National Lab an extraordinary opportunity to advance science, education, and business in ways never before seen.

Source record↗
DOE OSTI2022

Exploration of the Application of Machine Learning to the improvement of Interatomic Potentials

Current methods for atomistic simulations of material systems suffer from limitations which restrict the ability of the simulation to correctly characterize certain material behavior and physical phenomena. Small scale ab initio (AIMD) modeling is highly accurate but is computationally expensive. Classical molecular dynamics (CMD) simulations use interatomic potentials (IPs) to describe larger systems at a reduced computational cost, but with a reduced accuracy. Creating more robust IPs enable more precise CMD simulations. In this thesis, the application of a specific machine learning process,artifical neural networks (ANNs), to the improvement of IPs and MD simulation is discussed.

O'Connor, Sean↗
DOE OSTI2023

Renewal: Coherent control of spin states in organic electronics – discovery and investigation of complex room-temperature magnetic quantum excitations

In the course of four funding cycles of this project, its research focused on the exploration and understanding of spin-dependent electronic processes in organic semiconductors. It aimed to investigate the potential of harnessing the spin degree of freedom in organic materials for various applications such as spin electronics, quantum information, sensors, and as well as quantum coherent spin phenomena of charge carrier states in organic semiconductors, with the aim to develop room-temperature-based quantum applications. Specifically, the project aimed to: Apply new diagnostic tools, i.e. spin spectroscopy techniques, for paramagnetic charge carrier states in organic semiconductors, based on coherent control with pulsed electron spin resonance to understand spin relaxation and spin mixing mechanisms in organic semiconductor materials and devices such as OLEDs. Explore the nature of charge carrier spin-coupling and pathways for its manipulation to control the physical behaviors of these materials. This involves investigating spin interactions through materials structure, composition, morphological dependencies, isotopic effects, and externally applied electric and magnetic fields, with the goal to manipulate electrical and optical material properties by adjusting spin-orbit, spin-dipolar, spin-exchange, as well as spin-hyperfine interactions to open up this materials class for new applications. Study spin-dependent charge carrier transport and recombination and how these observables are governed by spin-propagation, especially coherent spin propagation as well as collective spin-effects such as the spin-Dicke effect and resonant multi-photon magnetic dipole transitions. The project utilized pulsed electrically detected magnetic resonance (pEDMR) and pulsed optically detected magnetic resonance (pODMR) techniques over a wide frequency range. These techniques enabled the observation and quantification of various spin-spin coupling types, including spin-exchange, spin-dipolar, hyperfine, and spin-orbit interactions. The project will leverage low- to mid-frequency pEDMR/pODMR facilities and collaboration with the National High Magnetic Field Laboratory.

36 MATERIALS SCIENCE↗
DOE OSTI2021

First steps toward predicting corrosion behavior of structural materials in molten salts

To address the need for physics-based models to predict corrosion behavior of materials in molten salts, here we propose potential methods to aid in selection of optimum materials for structural components in molten salt powered technologies. In the present work, the role of alloy thermodynamics and kinetics on governing corrosion rates of Ni-based alloys will be discussed combining experimental and computational methods. A few strategies are presented to quantify corrosion rates of Ni-based materials isothermally exposed in purified KCl-MgCl 2 salts at 600° C-800° C. The influence of capsule material, potential corrosion products and role of alloy composition on the observed corrosion rates was discussed with coupled thermodynamic-kinetic models. Larger depths of corrosion attack were observed in alloy 230 specimens compared to other alloys under similar conditions was attributed to the much higher chemical activity of Cr in the alloy that results in a larger Cr chemical potential gradient.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computational alchemy clarifies origins of alloy strengthening

Solid solution strengthening (SSS) is widely used to enhance mechanical properties of metals. Originally developed for dilute alloys, classical SSS theories are presently challenged by the rise of complex concentrated alloys (CCA) with nearly equiatomic compositions. Here, we propose and develop a method of “computational alchemy” in which interatomic interactions are modified to systematically vary two key physical parameters defining SSS - atomic size misfit and elastic stiffness misfit - over a maximally wide range of two misfits. The resulting alchemical alloys are subjected to massive (~10 8 atoms) molecular dynamics (MD) simulations reproducing full complexity of plastic strength response. At variance with prevailing views, stiffness misfit is observed to contribute to SSS on par if not more than size misfit. Furthermore, depending on exactly how two misfits are combined, they result in synergistic (amplification) or antagonistic (compensation) effect on alloy strengthening. Unlike real CCAs in which each component element comes with its own specific size and stiffness, our alchemical model alloys span the space of two misfits continuously revealing trends in alloy strengthening unrecognized so far. Our study demonstrates unique value of intentionally unrealistic models for gaining deep physical insights into material behaviors that are difficult to reveal otherwise.

36 MATERIALS SCIENCE↗