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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Identification of mobile genetic elements with geNomad

Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad’s speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Relativistic core–valence-separated equation-of-motion coupled-cluster singles and doubles method: Efficient implementation and benchmark calculations

An efficient implementation for the relativistic exact two-component core–valence-separated equation-of-motion coupled-cluster singles and doubles (X2C-CVS-EOM-CCSD) method is reported. The explicit exclusion of pure valence excitations in the EOM-CCSD excited-state eigenvalue equations significantly improves the efficiency for calculations of core-excited states. Benchmark relativistic CVS-EOM-CC calculations with systematic inclusion of relativistic, correlation, and basis-set effects are shown to provide highly accurate results for core ionized and excited states involving heavy atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

State densities of heavy nuclei in the static-path plus random-phase approximation

We report that nuclear state densities are important inputs to statistical models of compound-nucleus reactions. State densities are often calculated with self-consistent mean-field approximations that do not include important correlations and must be augmented with empirical collective enhancement factors. Here, we benchmark the static-path plus random-phase approximation (SPA + RPA) to the state density in a chain of samarium isotopes 148–155 Sm against exact results (up to statistical errors) obtained with the shell-model Monte Carlo (SMMC) method. The SPA + RPA method incorporates all static fluctuations beyond the mean field together with small-amplitude quantal fluctuations around each static fluctuation. Using a pairing plus quadrupole interaction, we show that the SPA + RPA state densities agree well with the exact SMMC densities for both the even- and odd-mass isotopes. For the even-mass isotopes, we also compare our results with mean-field state densities calculated with the finite-temperature Hartree-Fock-Bogoliubov (HFB) approximation. We find that the SPA + RPA repairs the deficiencies of the mean-field approximation associated with broken rotational symmetry in deformed nuclei and with the violation of particle-number conservation in the pairing condensate. In particular, in deformed nuclei the SPA + RPA reproduces the rotational enhancement of the state density relative to the mean-field state density.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Role of three-body dynamics in nucleon-deuteron correlation functions

Correlation functions of hadrons can be accessed in high-energy collisions of atomic nuclei, revealing information about the underlying interaction. This work complements experimental efforts to study nucleon-deuteron Nd—with N=p (proton) or N=n (neutron)—correlations with theory evaluations using different techniques. The correlation functions C nd and C pd are calculated based on a scattering wave function, extending previous benchmarks for the Nd scattering matrix to this new observable. We use hyperspherical harmonics and Faddeev techniques with one of the widely used nucleon-nucleon (NN) interactions, the Argonne v18 potential. Moreover, in the low-energy region we perform additional calculations in the framework of pionless effective field theory. The pd correlation function is computed in the large-energy region to make contact with a recent measurement by the ALICE Collaboration. We show that the scattering wave function has the proper dynamical input to describe an initial rise and subsequent oscillations of C pd as a function of the energy. Effects on the observables using different NN and three-nucleon potentials are evaluated with the conclusion that variations of around 2% are observed. Although these effects are small, future measurements can go beyond this accuracy, allowing for new detailed studies of strong interaction in light nuclear systems. Furthermore, the present study supports the current efforts devoted to the measurement of correlation functions in systems dominated by the strong interactions, such as pd, ppp, Λd, and ppΛ.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Synthesis and Characterization of Layered Actinide (U, Np, Pu) Oxide and Hydroxide Phases

Systematic structural comparisons across the early actinides remain limited by the scarcity of well-defined transuranic layered oxide and oxyhydroxide phases. Here, we report the synthesis and single-crystal characterization of new layered actinide compounds spanning U, Np, and Pu obtained under mild hydrothermal conditions in concentrated alkali hydroxide media. These include hydrated oxides α-Cs 2 U 2 O 7 •0.5H 2 O and Rb 2 An 2 O 7 •0.5H 2 O (An = Np, Pu), oxy-hydroxides Rb 6 [(AnO 2 ) 6 O 8 (OH) 2 ]•xH 2 O x = 0, 0.5 (An = U, Np) and Rb 4 [(UO 2 ) 5 O 6 (OH) 2 ]•2H 2 O, as well as nitrate-intercalated compounds Cs 6 [(AnO 2 ) 3 O 4 (OH) 2 ](NO 3 ) 2 (An = Np, Pu). Single-crystal X-ray diffraction studies reveal extended two-dimensional architectures constructed from edge- and vertex-sharing actinyl polyhedra, with systematic evolution in equatorial coordination, hydration, and anionic sheet topology across the U–Np–Pu series. Incorporation of nitrate anions within the interlayer region of Cs 6 [(AnO 2 ) 3 O 4 (OH) 2 ](NO 3 ) 2 establishes a previously unobserved structural motif in layered transuranic oxyhydroxides, demonstrating an additional pathway for anion-mediated framework stabilization. Correlation of crystallographic metrics with single-crystal Raman spectroscopy provides new vibrational benchmarks linking differences in An═O yl bond lengths to the equatorial coordination and the interstitial cations. These findings expand the structural hierarchy of layered actinide materials and address clarifying periodic trends governing topology, bonding, and vibrational signatures in high-valent 5ƒ oxide systems.

actinides↗
DOE OSTI2025

Spectroscopic Identification of Carbamate Formation and Synergistic Binding in Amide–CO$^{–}_{2}$ Complexes

Carbamate formation is an elementary step that governs nitrogencentered nucleophilic CO 2 capture across diverse environments, yet a direct, structure-specific understanding has been lacking. Here, we report the first gas-phase characterization of closed-shell carbamate formation by deprotonated amides, using cryogenic ion trap vibrational spectroscopy combined with quantum chemical calculations. Reactions of deprotonated benzamide and isophthalamide anions with CO 2 form carbamate species, with the amide nitrogen serving as the nucleophilic site. Diagnostic, strongly red-shifted antisymmetric CO 2 stretching vibrational bands, supported by a bonding analysis, establish chemisorption with substantial charge transfer. In the multiamide system, an intramolecular N−H···O hydrogen bond provides synergistic stabilization, correlating with larger red shifts and more exothermic binding. These structure-assigned benchmarks provide molecular-level insights into the binding motif, charge redistribution, and hydrogen bond-mediated stabilization of amide carbamates, which aid the characterization of carbamate formation in condensed phase.

Amides↗
DOE OSTI2026

Atomic-Scale Characterization and Electronic Properties of Gold-Capped Niobium Films for Superconducting Qubits

Niobium thin films are central to superconducting qubits, but their complex native oxides contribute significantly to microwave losses. A promising mitigation strategy is to suppress oxide formation using engineered thin-film encapsulation layers. Recent experiments have shown that ∼10 nm metallic overlayers on Nb capacitor films can improve the energy relaxation time T1 of transmon qubits. Here, we present a comparative study of Au-capped Nb films fabricated in situ by molecular beam epitaxy and ex situ by sequential deposition benchmarked against bare Nb films. Using complementary structural, chemical, and spectroscopic techniques, we correlate the interface quality with superconducting electronic properties of the Au surface. Low-temperature scanning tunneling spectroscopy (STS) provides spatially resolved quasiparticle density-of-states maps. While both Au-capped Nb samples exhibit large areas with uniform, fully gapped density of states, clear differences emerge between the two interfaces. Compared to in situ Nb-Au, ex situ Nb-Au exhibits a reduced induced superconducting gap, broadened coherence peak, and localized in-gap states, consistent with a residual NbxOy layer at the interface. Supported by complementary structural and chemical analyses, these findings demonstrate that STS directly links nanoscale superconducting properties to interface preparation, highlighting the importance of controlled in situ encapsulation for minimizing dissipation and improving quantum coherence.

Makita, Junki [MIT; Temple U.] (ORCID:000000026889↗
DOE OSTI2025

Cross-functional transferability in foundation machine learning interatomic potentials

The rapid development of foundation potentials (FPs) in machine learning interatomic potentials demonstrates the possibility for generalizable learning of the universal potential energy surface. The accuracy of FPs can be further improved by bridging the model from lower-fidelity datasets to high-fidelity ones. In this work, we analyze the challenge of this transfer learning (TL) problem within the CHGNet framework. We show that significant energy scale shifts and poor correlations between GGA and r 2 SCAN hinder cross-functional transferability. By benchmarking different TL approaches on the MP-r 2 SCAN dataset, we demonstrate the importance of elemental energy referencing in the TL of FPs. By comparing the scaling law with and without the pre-training on a low-fidelity dataset, we show that significant data efficiency can still be achieved through TL, even with a target dataset of sub-million structures. We highlight the importance of proper TL and multi-fidelity learning in creating next-generation FPs on high-fidelity data.

Huang, Xu [University of California, Berkeley, CA ↗
DOE OSTI2020

Taming the fixed-node error in diffusion Monte Carlo via range separation

By combining density-functional theory (DFT) and wave function theory via the range separation (RS) of the interelectronic Coulomb operator, we obtain accurate fixed-node diffusion Monte Carlo (FN-DMC) energies with compact multi-determinant trial wave functions. In particular, we combine here short-range exchange-correlation functionals with a flavor of selected configuration interaction known as configuration interaction using a perturbative selection made iteratively (CIPSI), a scheme that we label RS-DFT-CIPSI. One of the take-home messages of the present study is that RS-DFT-CIPSI trial wave functions yield lower fixed-node energies with more compact multi-determinant expansions than CIPSI, especially for small basis sets. Indeed, as the CIPSI component of RS-DFT-CIPSI is relieved from describing the short-range part of the correlation hole around the electron-electron coalescence points, the number of determinants in the trial wave function required to reach a given accuracy is significantly reduced as compared to a conventional CIPSI calculation. Importantly, by performing various numerical experiments, we evidence that the RS-DFT scheme essentially plays the role of a simple Jastrow factor by mimicking short-range correlation effects, hence avoiding the burden of performing a stochastic optimization. Considering the 55 atomization energies of the Gaussian-1 benchmark set of molecules, we show that using a fixed value of mu = 0.5 bohr(-1) provides effective error cancellations as well as compact trial wave functions, making the present method a good candidate for the accurate description of large chemical systems.

Scemama, Anthony↗
DOE OSTI2020

Benchmarking an Embedded Adaptive Sampling Configuration Interaction Method for Surface Reactions: H 2 Desorption from and CH 4 Dissociation on Cu(111)

Embedded (emb-) correlated wavefunction (CW) theory enables accurate assessments of both ground- and excited-state reaction mechanisms involved in heterogeneous catalysis. Embedded multireference second-order perturbation theory (emb-MRPT2) based on reference wavefunctions generated via embedded complete active space self-consistent field (emb-CASSCF) theory is currently state-of-the-art. However, the factorial scaling of CASSCF limits the size of active space and the complexity of systems that can be studied. In this work, we assess the efficacy of an alternative CW method, adaptive sampling configuration interaction (ASCI)–which enables large active spaces to be used–for studying surface reactions. We couple ASCI with density functional embedding theory (DFET) and benchmark its performance for two reactions: H 2 desorption from and CH 4 dissociation on the Cu(111) surface. Unlike embedded complete active space second-order perturbation theory (emb-CASPT2) that accurately reproduces a measured H 2 desorption barrier, embedded ASCI, using a very large active space (though one that still comprises a small portion of the full set of orbitals) fails to do so. Adding an extra correlation term from embedded Møller–Plesset second-order perturbation theory (emb-MP2) improves the desorption barrier and endothermicity predictions. Thus, the inaccuracy of embedded ASCI comes from the missing dynamic correlation from the many other electrons and orbitals not included in the active space. For CH 4 dissociation, again embedded ASCI overestimates the dissociation barrier compared to emb-CASPT2 predictions. Adding dynamic correlation from emb-MP2 helps correct the barrier. However, this composite approach suffers from double counting of correlation within embedded ASCI followed by emb-MP2 calculations. We therefore conclude that the state-of-the-art emb-MRPT2 based on reference wavefunctions generated via emb-CASSCF remains the method of choice for studying surface reactions. emb-ASCI is useful when large active spaces beyond the limit of emb-CASSCF are essential, such as to study complex surface reactions with significant multiconfigurational character (static correlation) but weak dynamic correlation.

08 HYDROGEN↗
DOE OSTI2024

Availability of Critical Benchmark Experiments for the Pebble Tanker Transportation Model for Nuclear Criticality Safety Validation of TRISO Pebbles

This study addresses the need for comprehensive investigations into TRi-structural ISOtropic (TRISO) fuel pebble transportation validation. In this work, an exploratory model, the pebble tanker(PT), was developed with the aim of facilitating the validation of nuclear criticality safety calculations in the context of industrial-scale transportation of TRISO fuel. The PT model was designed to investigate the availability and applicability of critical benchmark experiments crucial for assessing the transportation of these pebbles. This work incorporated sensitivity/uncertainty (S/U) similarity studies to quantify the applicability of critical benchmark experiments and to address nuclear data uncertainties in the context of TRISO transportation. Two container models were investigated: one for the Hermes-type pebble and one for the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering fuel, containment, and either water or air, to enable a focus on the underlying physics of applications involving TRISO fuel pebbles using the PT model. A crucial aspect under consideration was the capacity of the transport package to hold pebbles while ensuring subcriticality in the flooded state. An approach in the criticality validation process involves assessing the similarity between systems through an integral index parameter evaluation. This involves calculating a correlation coefficient (referred to as c k ) based on shared nuclear data–induced uncertainty between a benchmark experiment and the application of the PT model. To facilitate this analysis, the SCALE tools, particularly the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences, were employed for comprehensive studies in neutronics and S/U analysis. Our findings showed that there are sufficient critical experimental benchmarks to perform this validation of the PT model in the most reactive state, i.e. when the tanker is flooded. This paper provides valuable insights into validating a transport package for Generation IV TRISO fuel pebbles.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Assessment of Critical Experiment Benchmark Applicability to a Large-Capacity HALEU Transportation Package Concept

This work presents an assessment of the applicability of existing benchmark critical experiments to the criticality safety code validation for a large-capacity high-assay low-enriched uranium (HALEU) transportation package concept. Numerous next-generation nuclear reactor designs require HALEU fuel, which is characterized by an enrichment between 5 and 20 wt% 235 U. The U.S. Department of Energy (DOE) has proposed to recover and downblend highly enriched uranium from DOE-owned used nuclear fuel to accelerate the demonstration of commercially viable microreactor technologies. One element of the infrastructure needed to demonstrate HALEU-fueled reactors is the ability to safely transport enriched product to be used for fuel fabrication. There is uncertainty as to whether existing critical benchmark experiment data are sufficient to support criticality safety code validation for HALEU transportation applications. The anticipated chemical form of the HALEU in the proposed transportation concept is UO 2 with 20 wt% 235 U/U. The concept uses a combination of an existing transportation packaging design and a novel basket design, including borated aluminum flux traps. The basket provides space for 18 reusable, stainless steel canisters that contain the HALEU. In 10 CFR 71, normal conditions of transport (NCTs) and hypothetical accident conditions (HACs) are defined for fissile material transportation packages. NCT and HAC KENO-VI models of the transportation package were developed using the Standardized Computer Analyses for Licensing Evaluation (SCALE) 6.2.3 computer code package, and optimum moderation conditions were determined using the SCALE SAMPLER sequence. The SCALE Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI) sequences were then used to compare the neutronic characteristics of 1584 International Criticality Safety Benchmark Evaluation Project benchmark critical experiments with the NCT and HAC HALEU transportation models. The TSUNAMI integral correlation coefficient c k was the criterion used to rank neutronic similarity. Thirty-four experiments were identified as similar (c k ≥ 0.9) to the NCT model, and 55 experiments were identified as similar to the HAC model. Hundreds of experiments were also identified as at least marginally similar (c k ≥ 0.8) to both models. The results indicate that additional critical experiments are unlikely to be needed to support HALEU transportation criticality safety analyses for package concepts similar to the concept package analyzed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Measuring Heat Transfer Coefficients for Supercritical Carbon Dioxide Cycle Recuperators

This paper describes the measurement of convective heat transfer coefficients and friction factors for sCO 2 flowing in pin-fin patterned pipes at the US DoE’s National Energy Technology Laboratory (NETL) in Morgantown, WV. The measurement procedures were validated by conducting benchmark tests with smooth stainless-steel tube and comparing the results with published correlations for Nusselt number (𝑁𝑢) and friction factor. Over Reynolds numbers (Re D ) ranging from 5×104 to 2.5×105 , measured 𝑁𝑢 was within 5% of the Dittus-Boelter correlation and measured friction factors were within 5% of the McAdams correlation for smooth tube flow.

42 ENGINEERING↗
DOE OSTI2022

Measurement of Convective Heat Transfer Coefficients with Supercritical CO2 in Novel Additively Manufactured Helically Patterned Pin Fin Tubes Using the Wilson Plot Technique

This paper describes the measurement of convective heat transfer coefficients and friction factors for sCO2 flowing in pin-fin patterned pipes in the Heat Exchange and Experimental Testing (HEET) facility at the US DoE’s National Energy Technology Laboratory (NETL) in Morgantown, WV. The measurement procedures in the HEET rig were validated by conducting benchmark tests with smooth stainless-steel tube and comparing the results with published correlations for Nusselt number (Nu) and friction factor. Over typical Reynolds number range in sCO2, the measured Nu and friction factors were within 7% of classical correlations for smooth tube flow.The candidate pin fin patterned pipes were additively manufactured (AM) at the Oak Ridge National Laboratory. The pins were circular or elliptical in cross-section. Pin length to diameter aspect ratios were 1.33 and 2, while the pin diameter to tube diameter ratio was 0.188 and 0.125. Tests were performed for ReD varying from 6.9×104 to 2.2×105 and at conditions equivalent to the low pressure outlet (8.69 MPa, 361 K) of the low temperature recuperator (LTR) in an indirect sCO2 power cycle. The Wilson plot technique was utilized to measure the bulk heat transfer coefficients.For the better performing design (tube A, pin length to tube diameter ratio: 1.33, pin diameter to tube diameter ratio: 0.188), the local heat transfer coefficient increased by 112% relative to the Dittus-Boelter correlation at the LTR low pressure outlet. This corresponded to a 282% increase in the product of the heat transfer coefficient and the surface area. Large pressure drops across the test articles were observed.

Searle, Matthew↗
DOE OSTI2024

Modeling Multi-Step Organic Reactions: Can Density Functional Theory Deliver Misleading Chemistry?

Many organic reactions are characterized by a complex mechanism with a variety of transition states and intermediates of different chemical natures. Their correct and accurate theoretical characterization critically depends on the accuracy of the computational method used. In this work, we study a complex ambimodal cycloaddition with five transition states, two intermediates, and three products, and we ask whether density functional theory (DFT) can provide a correct description of this type of complex and multifaceted reaction. Our work fills a gap in that most systematic benchmarks of DFT for chemical reactions have considered much simpler reactions. Our results show that many density functionals not only lead to seriously large errors but also differ from one another in predicting whether the reaction is ambimodal. Only a few of the available functionals provide a balanced description of the complex and multifaceted reactions. The parameters varied in the tested functionals are the ingredients, the treatment of medium-range and nonlocal correlation energy, and the inclusion of Hartree–Fock exchange. Furthermore, these results show a clear need for more benchmarks on the mechanisms of large molecules in complex reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Quantum simulations of excited states with active-space downfolded Hamiltonians

Many-body techniques based on the double unitary coupled cluster ansatz (DUCC) can be used to downfold electronic Hamiltonians into low-dimensional active spaces. It can be shown that the resulting dimensionality reduced Hamiltonians are amenable for quantum computing. Recent studies performed for several benchmark systems using quantum phase estimation (QPE) algorithms demonstrated that these formulations can recover a significant portion of ground-state dynamical correlation effects that stem from the electron excitations outside of the active space. These results have also been confirmed in studies of ground-state potential energy surfaces using quantum simulators. In this letter, we study the effectiveness of the DUCC formalism in describing excited states. We also emphasize the role of the QPE formalism and its stochastic nature in discovering/identifying excited states or excited-state processes in situations when the knowledge about the true configurational structure of a sought after excited state is limited or postulated (due to the specific physics driving excited-state processes of interest). In this context, we can view the QPE algorithm as an engine for verifying various hypotheses for excited-state processes and providing statistically meaningful results that correspond to the electronic state(s) with the largest overlap with a postulated configurational structure. We illustrate these ideas on examples of strongly cor- related molecular systems, characterized by small energy gaps and high density of quasi-degenerate states around the Fermi level.

quantum chemistry, quantum computing, computationa↗
DOE OSTI2022

Machine learning prediction on the fractional free volume of polymer membranes

Fractional free volume (FFV) characterizes the microstructural level features of polymers and affects their properties including thermal, mechanical, and separation performance. Experimental measurements and theoretical analyses have been used to quantify the FFV of polymers, but challenges remain because of their limitations. Experimental measurements are laborious and based on semi empirical equations, while Bondi’s group contribution theory involves ambiguities like the determination of van der Waals volume and the choice of factor values in the theoretical equation. To efficiently evaluate the FFV of polymers, this study utilizes high-throughput molecular dynamics (MD) simulations to build a large dataset regarding polymer’s FFV. Based on this large dataset, we further build machine learning (ML) models to establish the composition-structure relation. Inspired by group contribution theory which correlates polymer’s functional groups to FFV, our ML models correlate polymer’s substructures or physico-chemical indexes to FFV. Here, our study first benchmarks the MD simulation protocol to obtain reliable FFV of polymers and then carries out high-throughput MD simulations for more than 6,500 homopolymers and 1,400 polyamides. Such a large and diverse dataset makes the well-trained ML models more generalizable, compared with the group contribution theory. The efficiency of a feed forward neural network model is further demonstrated by applying it to a hypothetical polyimide dataset of more than 8 million chemical structures. The predicted FFVs of hypothetical polyimides are further validated by MD simulations. The obtained FFVs of the 8 million polymers, plus their previously reported gas separation performances, demonstrate the promising capability of ML virtual screening for the discovery of polymer membranes with exceptional permeability/selectivity.

36 MATERIALS SCIENCE↗