An optimal property of sequential decision procedures related to Bayes solutions
Optimal property of sequential decision procedures
DECISION MAKING↗
SEARCH · Search NASA
Results for “Property optimization”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.
3D molecular generation↗
Development of Directed Energy Deposited NASA HR-1 to Optimize Properties for Liquid Rocket Engine Applications
Metal additive manufacturing (AM) processes have been demonstrated to be effective at reducing costs and lead times associated with fabrication of complex propulsion component designs. Laser powder directed energy deposition (LP-DED) and laser powder bed fusion (L-PBF) are metal AM technologies that have effectively been used to produce a variety of parts for space applications. These technologies have enabled new designs and materials for these applications. The National Aeronautics and Space Administration (NASA) has identified the need to develop and advance new materials in unique space applications such as high-pressure hydrogen environments. One such metal alloy being developed for these applications is NASA HR-1. A high-strength Fe-Ni based superalloy, NASA HR-1 was designed to resist high pressure hydrogen environment embrittlement, oxidation, and corrosion. AM technologies have enabled the manufacturing of NASA HR-1 to be a feasible material for components in hydrogen environments. Development efforts for NASA HR-1 have completed build and heat treatment optimization, material characterization, thermophysical property testing, and mechanical testing in air and hydrogen environments to mature the understanding of the material. This poster will summarize the development of NASA HR-1 for AM including composition and heat treatment optimization, microstructure characterization, and results of mechanical testing in air and high pressure hydrogen.
NASA HR-1↗
A New Approach to Identify Optimal Properties of Shunting Elements for Maximum Damping of Structural Vibration Using Piezoelectric Patches
The use of shunted piezoelectric patches in reducing vibration and sound radiation of structures has several advantages over passive viscoelastic elements, e.g., lower weight with increased controllability. The performance of the piezoelectric patches depends on the shunting electronics that are designed to dissipate vibration energy through a resistive element. In past efforts most of the proposed tuning methods were based on modal properties of the structure. In these cases, the tuning applies only to one mode of interest and maximum tuning is limited to invariant points when based on den Hartog's invariant points concept. In this study, a design method based on the wave propagation approach is proposed. Optimal tuning is investigated depending on the dynamic and geometric properties that include effects from boundary conditions and position of the shunted piezoelectric patch relative to the structure. Active filters are proposed as shunting electronics to implement the tuning criteria. The developed tuning methods resulted in superior capabilities in minimizing structural vibration and noise radiation compared to other tuning methods. The tuned circuits are relatively insensitive to changes in modal properties and boundary conditions, and can applied to frequency ranges in which multiple modes have effects.
Park, Junhong↗
Property optimized energy absorber for automotive bumpers utilizing multi-material and structural design strategies
This study proposes a novel design for automotive bumper using optimized lattice structures and multi-materials to balance low-speed collision and high-speed pedestrian impact performance. Different blends of 20 % carbon fiber-reinforced acrylonitrile butadiene styrene with thermoplastic polyurethane were used to tailor material properties. The energy absorber features lattice structures with customized mechanical responses, created by varying the incline angle θ from 0 to 180°. We conducted 576 finite element simulations on a half-scale model to optimize energy absorption and stiffness, leading to 66 optimized designs that met both low-speed and high-speed impact criteria. Two sub-scale optimized energy absorbers with different peak forces—both meeting low-speed impact requirements—were 3D printed and validated through drop-weight testing. The one with lower peak stress demonstrated a more compliant response, exhibiting approximately 90 % lower initial peak force and an increase in energy absorption of around 33 % (from 24 J to 32 J). Compared to the baseline triangular lattice, the optimized absorber increased energy absorption by 68 % from (19 J to 32 J) and reduced peak stress by 70 %. It also showed near-complete recovery with minimal fractures, making it suitable for repeated use. This design improves safety while offering a lightweight, durable, and cost-effective bumper system.
36 MATERIALS SCIENCE↗
Soil Moisture Active Passive Mission Mass Properties Optimization and Analysis
The Soil Moisture Active Passive (SMAP) mission creates global soil moisture measurements every 2-3 days by spinning a lightweight, ~6 meter diameter deployable mesh reflector. This represents the largest spinning antenna ever to be used on a NASA satellite. The paper discusses the mass properties challenges that were overcome in implementing the mission, including the definition of a combined imbalance term called the Effective Product of Inertia, the selection of an optimum Spun Instrument focal length and edge offset that fostered a reduced mass properties sensitivity, the inclusion of late game Spun Instrument configuration flexibility, the use of balance mass locations that leveraged the large moment arm provided by the reflector, and a subassembly measurement campaign that reduced mass properties uncertainties on testable components.
Hendry, Morgan L., IV↗
Precursor Selection for Property Optimization in Biomorphic SiC Ceramics
Biomorphic SiC ceramics have been fabricated using different wood precursors. The evolution of volume, density and microstructure of the woods, carbon performs, and final SiC products are systematically studied in order to establish experimental guidelines that allow materials selection. The wood density is a critical characteristic, which results in a particular final SiC density, and the level of anisotropy in mechanical properties in directions parallel (axial) and perpendicular (radial) to the growth of the wood. The purpose of this work is to explore experimental laws that can help choose a type of wood as precursor for a final SiC product, with a given microstructure, density and level of anisotropy. Preliminary studies of physical properties suggest that not only mechanical properties are strongly anisotropic, but also electrical conductivity and gas permeability, which have great technological importance.
Varela-Feria, F. M.↗
Development of Directed Energy Deposited NASA HR-1 to Optimize Properties for Liquid Rocket Engine Applications
Metal additive manufacturing (AM) processes have been demonstrated to be effective at reducing costs and lead times associated with fabrication of complex propulsion component designs. Laser powder directed energy deposition (LP-DED) is a metal AM technology that has effectively been used to produce a variety of parts for liquid rocket engine applications. The LP-DED technology has enabled new designs and materials for these applications. The National Aeronautics and Space Administration (NASA) has identified the need to develop and advance new materials in unique engine applications such as hydrogen environments. One such metal alloy being developed for these applications is NASA HR-1. A high-strength Fe-Ni based superalloy, NASA HR-1 was designed to resist high pressure hydrogen environment embrittlement, oxidation, and corrosion. Enabled by LP-DED, NASA HR-1 has completed build and heat treatment optimization, characterization, mechanical and thermophysical testing, and hot-fire testing to demonstrate its capability as an alloy used in liquid rocket engine applications. The evolution of the alloy through material characterization analysis and mechanical testing results from development and optimization efforts will be presented.
additive manufacturing↗
Development of Laser Powder Directed Energy Deposited NASA HR-1 to Optimize Properties for Liquid Rocket Engine Applications
Explore the source record for details and available documents.
Additive Manufacturing↗
A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties
Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.
Biswas, Arpan↗
Optimization of rotor blades for combined structural, dynamic, and aerodynamic properties
Optimal helicopter blade design with computer-based mathematical programming has received more and more attention in recent years. Most of the research has focused on optimum dynamic characteristics of rotor blades to reduce vehicle vibration. There is also work on optimization of aerodynamic performance and on composite structural design. This research has greatly increased our understanding of helicopter optimum design in each of these aspects. Helicopter design is an inherently multidisciplinary process involving strong interactions among various disciplines which can appropriately include aerodynamics; dynamics, both flight dynamics and structural dynamics; aeroelasticity: vibrations and stability; and even acoustics. Therefore, the helicopter design process must satisfy manifold requirements related to the aforementioned diverse disciplines. In our present work, we attempt to combine several of these important effects in a unified manner. First, we design a blade with optimum aerodynamic performance by proper layout of blade planform and spanwise twist. Second, the blade is designed to have natural frequencies that are placed away from integer multiples of the rotor speed for a good dynamic characteristics. Third, the structure is made as light as possible with sufficient rotational inertia to allow for autorotational landing, with safe stress margins and flight fatigue life at each cross-section, and with aeroelastical stability and low vibrations. Finally, a unified optimization refines the solution.
He, Cheng-Jian↗
RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design
Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.
Genetic algorithms↗
Computational design of thermoelectric alloys through optimization of transport and dopability
Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage etc. Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba 2(1–x) Sr 2x CdP 2 Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. Here, we assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE.
36 MATERIALS SCIENCE↗
High‐throughput combinatorial approach expedites the synthesis of a lead‐free relaxor ferroelectric system
Abstract Developing novel lead‐free ferroelectric materials is crucial for next‐generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time‐consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high‐throughput combinatorial synthesis approach to fabricate lead‐free ferroelectric superlattices and solid solutions of (Ba 0.7 Ca 0.3 )TiO 3 (BCT) and Ba(Zr 0.2 Ti 0.8 )O 3 (BZT) phases with continuous variation of composition and layer thickness. High‐resolution x‐ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well‐controlled compositional gradients. Ferroelectric and dielectric property measurements identify the “optimal property point” achieved at the composition of 48BZT–52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT–BZT} N superlattice geometry. This high‐throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth. image
36 MATERIALS SCIENCE↗
High-throughput combinatorial approach expedites the synthesis of a lead-free relaxor ferroelectric system
Developing novel lead-free ferroelectric materials is crucial for next-generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time-consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high-throughput combinatorial synthesis approach to fabricate lead-free ferroelectric superlattices and solid solutions of (Ba 0.7 Ca 0.3 )TiO 3 (BCT) and Ba(Zr 0.2 Ti 0.8 )O 3 (BZT) phases with continuous variation of composition and layer thickness. High-resolution x-ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well-controlled compositional gradients. Ferroelectric and dielectric property measurements identify the “optimal property point” achieved at the composition of 48BZT–52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT–BZT} N superlattice geometry. This high-throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth.
36 MATERIALS SCIENCE↗
Structural reliability and robustness properties of optimal linear-quadratic multivariable regulators
Strong sufficient conditions are derived for the robustness of optimal linear-quadratic (LQ) regulators to large parameter perturbations. In particular, it is shown that under certain conditions LQ designs remain stable in the presence of actuator channel failures. The general results can be specialized to provide insight into the gain margin, gain reduction, and phase margin properties of optimal LQ regulators.
Wong, P.-K.↗
The analytical representation of viscoelastic material properties using optimization techniques
This report presents a technique to model viscoelastic material properties with a function of the form of the Prony series. Generally, the method employed to determine the function constants requires assuming values for the exponential constants of the function and then resolving the remaining constants through linear least-squares techniques. The technique presented here allows all the constants to be analytically determined through optimization techniques. This technique is employed in a computer program named PRONY and makes use of commercially available optimization tool developed by VMA Engineering, Inc. The PRONY program was utilized to compare the technique against previously determined models for solid rocket motor TP-H1148 propellant and V747-75 Viton fluoroelastomer. In both cases, the optimization technique generated functions that modeled the test data with at least an order of magnitude better correlation. This technique has demonstrated the capability to use small or large data sets and to use data sets that have uniformly or nonuniformly spaced data pairs. The reduction of experimental data to accurate mathematical models is a vital part of most scientific and engineering research. This technique of regression through optimization can be applied to other mathematical models that are difficult to fit to experimental data through traditional regression techniques.
Hill, S. A.↗