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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.

cloud-optimized↗
DOE OSTI2023

New Tables for Infrasound Event Catalogs with a Focus on Machine Learning

Natural and anthropogenic sources such as volcanoes, earthquakes, auroral processes, chemical and nuclear explosions, rocket launches, and aircraft can generate infrasound, sound with frequencies less than 20 Hz. Both the availability of infrasound data and interest in machine learning (ML) applications have grown in recent years. Large, open-source datasets are essential to solving complex ML problems, but the field of infrasound is lacking in this arena. To increase the utility of ML for infrasound, here we present new tables for infrasound event catalogs. It is the aim that these tables be incorporated into both existing and future infrasound processing pipelines to generate large datasets ripe for use with ML/DL methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Code Generators for Floating-Point Unit Design in Integrated Circuits (OpenFloat) v1.0

This IP provides a comprehensive set of code generators for various floating-point units (FPUs) essential for integrated circuit design and integration, targeting a broad spectrum of applications, including machine learning and scientific computing. The suite includes FP adders, multipliers, subtractors, dividers, reciprocals, exponentials, square roots, trigonometric functions (sine, cosine, arctangent), and more. It supports customizable hardware design parameters, such as precision (16, 32, 64, and 128 bits) and pipeline depths, offering users enhanced flexibility and productivity. The generated code is in an industry-standard hardware description language, ensuring compatibility with standard design flows, including simulation, verification, synthesis, and implementation on both field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs).

Shalf, JohnM. [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Undergraduate Women in STEM Internship Program

In collaboration with the Universities Research Association, Inc. (URA), establishment of the Undergraduate Women in STEM Internship pilot program assists Fermi Research Alliance, LLC (FRA) with honoring its commitment to increasing gender, racial and ethnic workforce diversity. This focused initiative is intended to invite, support and yield a qualified pipeline of alumnae underrepresented in FRA’s science and technology management and technical staff positions. Interns in this program spend 10-12 weeks at Fermilab working under the direct leadership, guidance and supervision of scientists, engineers, computing professionals, technicians and staff across all directorates performing hands-on research. In addition, the intern cohort meets with mentors and advisors to grow their professional networks, to take part in engaging and inclusive intellectual discussions, to directly benefit from workplace resources, and to participate in professional development and social engagement opportunities. The design of each research project and the supervision, mentorship and evaluation is conducted by a scientific, engineering, computing professional at Fermilab.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

The "PVLib" of Degradation: PVDeg

The Photovoltaic (PV) industry constantly aims for lower costs through higher-efficiency cells, improved module designs, and improvements in durability. This leads to the use of new materials, designs, and manufacturing processes, and not always with a sufficient amount of durability testing. To help drive down costs there is a desire to create modules that will last for up to 50 years of service life. To accomplish this, every degradation mode and mechanism must be identified and either eliminated or otherwise mitigated. This involves the extrapolation of laboratory results to the field conditions. There is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. While the basic equations used to model degradation are sometimes very simple, the full analysis involves calculations are cumbersome but ubiquitous for many degradation processes. A simplified, modeling framework to accomplish these repetitive processes will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this talk, we will describe our progress creating the open-source tool PVDeg. This tool can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. PVDeg simplifies many of the common foundational computational operations for obtaining meteorological data and using it to generate a model of the PV deployment. This prediction tool repository also contains various degradation models as well as a library of material parameters suitable for estimating the durability assessment of materials and components. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database, and other weather sources, to perform geospatial degradation analysis in the US and worldwide. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry. During the talk, we will provide the PVPMC attendees the opportunity to interact with the tool via a Google Collab tutorial they can run on their phones or laptops.

durability↗
DOE OSTI2022

Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC‐GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Sengupta, Aunnasha↗
DOE OSTI2023

Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.

big scientific dataset↗
DOE OSTI2023

MFA-DVR: direct volume rendering of MFA models

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. Here, in this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Sparse Distributed Gigascale Resolution Material Point Method

In this paper, we present a four-layer distributed simulation system and its adaptation to the Material Point Method (MPM). The system is built upon a performance portable C++ programming model targeting major High-Performance-Computing (HPC) platforms. A key ingredient of our system is a hierarchical block-tile-cell sparse grid data structure that is distributable to an arbitrary number of Message Passing Interface (MPI) ranks. We additionally propose strategies for efficient dynamic load balance optimization to maximize the efficiency of MPI tasks. Our simulation pipeline can easily switch among backend programming models, including OpenMP and CUDA, and can be effortlessly dispatched onto supercomputers and the cloud. Finally, we construct benchmark experiments and ablation studies on supercomputers and consumer workstations in a local network to evaluate the scalability and load balancing criteria. We demonstrate massively parallel, highly scalable, and gigascale resolution MPM simulations of up to 1.01 billion particles for less than 323.25 seconds per frame with 8 OpenSSH-connected workstations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Wide Bandgap Generation (WBGen): Developing the Future Wide Bandgap Power Electronics Engineering Workforce

This Final Technical Report (FTR) summarizes the work conducted at the Center for Power Electronics Systems (CPES) under the Wide-Bandgap Generation (WBGen) fellowship and traineeship program established by the Advanced Manufacturing and Technologies Office (AMMTO) of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy (EERE) at Virginia Tech, which had as main objective to train the next generation of U.S. citizen power engineers with wide-bandgap (WBG) power semiconductor expertise, with the intent to aid in fulfilling the future workforce needs in this field. The latter was deemed of strategic importance given the fast-paced growth observed—and predicted—in the demand of this technical expertise, whose practitioners have become the enablers and executioners of the electrification transformation process that not just the U.S., but the whole world, is currently undergoing as it seeks for more effective and efficient ways to use energy. As such, the WBGen program set forth to achieve its educational goals, which in addition sought to broaden the range of WBG-based power electronics by conducting research and development on high-efficiency grid apparatus and high-efficiency electrical power systems, and to also enhance the power engineering curriculum by formalizing WBG-oriented design procedures replacing existent yet now obsolete design procedures developed for Silicon (Si) based power electronics. This effort led CPES to spearhead the development of a new major within The Bradley Electrical and Computer Engineering (ECE) Department at Virginia Tech, namely Electronic Power and Energy Systems (EPES), which coalesced power electronics and power systems courses to provide undergraduate students with a strong formation in the power engineering field, while creating a pipeline of graduate students that could pursue the WBG-based curriculum and conduct research at CPES. In all, in what is considered a true success, eight of the twenty WBGen fellows that graduated program were recruited from the ECE department undergraduate cohort. The traineeship emphasized as well, from its beginning, the partnership with industry and national laboratories, which took advantage of the successful industry consortium at CPES that has historically been formed by 80–90 power and energy companies working in close collaboration with the center. This gave WBGen fellows the accessibility and possibility to conduct internships at partner facilities during the summer months, focused solely on the evaluation, testing, and adoption of WBG devices, which were many times tightly related to their respective research work and plans. In addition, WBGen fellows conducted their main research work within the confines of research programs at CPES conducted with these industry partners, providing them with a unique opportunity to develop not just their technical expertise—while advancing their knowledge, but to also learn and practice a slew of skills needed for their professional growth. As such, the fellows tackled a variety of WBG-related research topics, from device reliability and capability aspects as well as packaging and integration, encompassing the use of advanced materials and new structures, current sharing challenges, and insulation systems, to advanced gate-drivers with integrated sensors and protection mechanisms and active current- and voltage-based control, to optimized layouts seeking to maximize the switching and power processing performance of these devices, to power processing solutions adopting Gallium-Nitride (GaN) and Silicon-Carbide (SiC) power semiconductors for a variety of applications; including radiation-hardened converters for space dc distribution systems, direct three-phase ac-to-ac power converters for aerospace systems, dc-ac inverters for automotive traction drives, high-frequency isolated dc-dc battery chargers also for heavy transportation systems, and medium-voltage dc-dc and dc-ac converters for distribution systems and future power grids. The WBGen program ultimately graduated a total of 20 power engineers, all experts on WBG-based power electronics, awarding 18 M.S. and 2 PhD degrees in the process. These fellows, all U.S. citizens, allowed CPES to increase the number of citizens students to 33 % at the peak of the program, as the traineeship made possible the recruitment of talent with more attractive graduate research assistantship (GRA) contracts. Unfortunately, the present U.S. citizen enrollment at CPES has declined back to historic levels—approximately 10 %, as the regular GRA rates are not competitive enough when compared with entry-level industry jobs. In all, WBGen fellows published a total of 7 peer-reviewed journal articles, 44 papers at international technical conferences, made 42 presentations at international technical conferences, and filed 4 invention disclosures and patent applications, which have since then been granted by the U.S. Patent and Trademark Office (USPTO). Their contribution to CPES, Virginia Tech, the United States, and the world, has been significant, and continues to yield results thanks to the exemplary career that the fellows have initiated at many of the partners of the program, which include Wolfspeed, Raytheon Technologies, Infineon, Lockheed Martin, Dominion Energy, Northrop Grumman, Rivian, Aerospace Corporation, Sandia National Laboratory, National Renewable Energy Laboratory, John Hopkins University, and Virginia Tech.

14 SOLAR ENERGY↗
DOE OSTI2026

Expanding the Domain of Applicability of Machine Learning Models with Limited Data for Drug Property Prediction

Accurate machine learning models for predicting small molecule interactions with biological targets are essential for therapeutic discovery, biothreat response, and computational drug design, but their performance is often limited for understudied targets with sparse experimental data. To address this challenge, we developed and evaluated methods to improve molecular property prediction under low-data conditions, using the NimA-related kinase (NEK) family as a proof-of-concept. This work focused on two complementary goals within the ATOM Modeling PipeLine (AMPL) and the Generative Molecular Design (GMD) loop: expanding model applicability through transfer learning, representation learning, feature scaling, sampling strategies, and active-learning-inspired compound selection; and enabling efficient virtual screening to prioritize compounds that balance predicted activity, design objectives, and synthetic accessibility.

organic↗
DOE OSTI2022

The Status and Ambitions of the US Heavy Element Program

The aim of this whitepaper is to highlight the current capabilities and priorities of the US Heavy Element community and to provide the framework for a coordinated advancement of nuclear science from these studies. This is an organized effort to reflect on what has been achieved in the field given the recommendations and initiatives of the 2015 NSAC Long Range Plan, and on what can be realized in the next decade given current and possibly expanded investments. Current investments have positioned the US community to be among the world leaders in studies of the nuclear and chemical properties of the heaviest elements. These include studies of reaction mechanisms, moving us ever closer to the “island of stability”, in spectroscopy, allowing us to better understand nuclear structure at these extreme proton numbers, in chemical behavior, looking to determine how these elements should be placed on the Periodic Table, in performing the first measurements where isotopes are directly identified by their mass numbers, and in laying the foundation for a potential US-led new element discovery experiment. At present, the highest priority of the US Heavy Element community is to capitalize on the current investments by supporting the operations of US facilities at optimal values. These facilities include the Argonne Tandem Linac Accelerator System at Argonne National Laboratory and other Department of Energy facilities such as the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory, which has a dedicated superheavy element program, as well as university laboratories, including Texas A&M University. The High Flux Isotope Reactor at Oak Ridge National Laboratory is crucial to providing the radioactive isotopes required for heavy element science targets. This facility should be supported to provide the actinide materials that are essential for US-based science. Production of stable, rare isotopes for beam material, including 48 Ca, 50 Ti, 54 Cr and 58 Fe, at the Stable Isotope Production and Research Center is critical to continued research in heavy element science and should be a priority. The continued development of targets for heavy element science and retaining US-based expertise is critical for the heavy element community. This is an area that is currently under pressure. For example, the target laboratory at Argonne National Laboratory serves a broad community and is currently under threat due to loss of critical personnel. The skills needed to make targets for nuclear science and develop new targetry methods need to be supported long term at Argonne, Oak Ridge and Lawrence Livermore National Laboratories as well as maintaining the programs at Oregon State University and San José State University as vital pipelines for training students. Advances in theory are the foundation to understand how nuclei behave and to predict those behaviors in new circumstances. Progress in these studies will necessitate continued and new investment and access to high-performance computing. The future health of the heavy element field is dependent on the continuous support of talented early-career professionals at all levels. It is critical that opportunities continue to be created for the next generation to become established in heavy element research so that we can ensure the field is attracting and retaining the best minds for continued success. It is also clear that to ensure diversity of ideas, perspectives and techniques, we need to recruit diverse personnel that are trained at the best facilities. The heavy element community is in support of continued investment to programs with initiatives in diversity, equity, and inclusion. Looking to the next decade of research, support needs to maintain and grow US leadership in heavy element science. Specifically, new investments in state-of-the-art instrumentation will be essential to scientific development of the field and in expanding scientific knowledge. Advances in the next generation of electron cyclotron resonance ion sources, multi-reflection time-of-flight devices, laser spectroscopy, trapping methods and next generation alpha and gamma spectroscopy systems should be prioritized.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Wildfire Smoke PM2.5 per Zipcode

This dataset contains daily aggregated measurements of daily PM2.5 from ambient wildfire smoke in the contiguous United States, spanning from 2006 to 2016. The data is sourced from a study by Childs et al. (2022), titled "Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US" published in Environmental Science & Technology. To compute the standardized weight across different zip codes, we computed the weight calculation on a 10km grids. To compute the weight, we obtain area-weights that add up to 1 in each polygon of zipcodes. This enabled us to calculate the smoke values per zipcode for the aforementioned period. Those interested in replicating our data processing pipeline can access it at https://github.com/NSAPH-Data-Processing/smoke_aggregation.

PM2.5↗
DOE OSTI2020

MARTINI-Compatible Coarse-Grained Model for the Mesoscale Simulation of Peptoids

Peptoids (poly-N-substituted glycines) are a class of synthetic polymers that are regioisomers of peptides (poly-C-substituted glycines), in which the point of side-chain connectivity is shifted from the backbone C to the N atom. Peptoids have found diverse applications as peptidomimetic drugs, protein mimetic polymers, surfactants, and catalysts. Computational modeling is valuable in the understanding and design of peptoid-based nanomaterials. In this work, we report the bottom-up parameterization of coarse-grained peptoid force fields based on the MARTINI peptide force field against all-atom peptoid simulation data. Our parameterization pipeline iteratively refits coarse-grained bonded interactions using iterative Boltzmann inversion and nonbonded interactions by matching the potential of mean force for chain extension. We assure good sampling of the amide bond cis/trans isomerizations in the all-atom simulation data using parallel bias metadynamics. We develop coarse-grained models for two representative peptoids—polysarcosine (poly(N-methyl glycine)) and poly(N-((4-bromophenyl)ethyl)glycine)—and show their structural and thermodynamic properties to be in excellent accord with all-atom calculations but up to 25-fold more efficient and compatible with MARTINI force fields. Here, this work establishes a new rigorously parameterized coarse-grained peptoid force field for the understanding and design of peptoid nanomaterials at length and time scales inaccessible to all-atom calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗