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DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗

DOE OSTI2020

An organogel library for solution NMR analysis of nanoparticle suspensions in non-aqueous samples

Surface contrast solution NMR methods (scNMR) are emerging as powerful tools to investigate the adsorption of small molecule ligands to the surface of nanoparticles (NP), returning fundamental insight into the kinetics and thermodynamics of sorption, as well as structural information on the adsorbed species. A prerequisite for the acquisition of high quality solution NMR data is the preparation of homogeneous and stable samples that return consistent NMR spectra and allow extensive signal averaging. Unfortunately, this condition does not apply to NMR samples containing NPs that often show a tendency to sediment and accumulate at the bottom of the NMR tube over the course of the experiment. We have recently shown that preparing NMR samples in an agarose gel matrix inhibits sedimentation and allows the characterization of small molecule-NP interactions by scNMR. Unfortunately, as the agarose gel only forms in aqueous solution, this sample preparation method cannot be used to stabilize NP suspensions in a non-aqueous environment. In this work, we introduce a library of 48 organogels, based on low molecular-mass organic gelators (LMOGs), to prepare NMR samples of small molecule/NP systems in a wide range of organic solvents. In addition, we present a simple method that takes advantage of 1H transverse relaxation (1H-R2) measurements to screen the library and identify the best gelator to characterize the small molecule-NP interaction of interest in the solvent of choice. We expect the results of this study will enable the preparation of homogeneous and stable samples of NPs in non-aqueous environments, therefore dramatically increasing the applicability of scNMR to the characterization of heterogeneous interactions and to the investigation of the role played by solvent molecules in regulating the kinetics and thermodynamics of sorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Impact of Crystalline Phases on Low-Activity Waste Glass Durability: Insights from PCT and VHT

During vitrification of nuclear wastes, slow cooling along the container centerline promotes crystalline phase formation, which can alter residual glass composition and reduce chemical durability. This study investigates the effects of crystalline phases on the chemical durability of low-activity waste (LAW) borosilicate glasses using the product consistency test (PCT) and vapor hydration test (VHT) on container centerline cooled (CCC) samples. A preliminary model (R2 = 0.88) was developed to predict CCC PCT responses based on glass composition, PCT data from quenched glasses, and measured crystal fractions. Using the latest LAW glass dataset, the feasibility of predictive modeling is evaluated, limitations in current data and methods are identified, and challenges for improving model accuracy are discussed to guide future data collection and model development.

borosilicate glass↗

DOE OSTI2022

Susceptibility weighted MRI pinpoints spontaneous intracerebral hemorrhage in stroke-prone spontaneously hypertensive rats

Purpose: To find magnetic resonance imaging (MRI) precursors of spontaneous intracerebral hemorrhage in stroke-prone spontaneously hypertensive rats (SHRSP). Method: SHRSP rats were used with both a low/high salt (n = 18 or 11) Japanese diet and salty drinking water to generate spontaneous intracerebral hemorrhage (ICH). Various MRI sequences, and in particular, susceptibility weighted imaging (SWI), were used and combined with a gadolinium (Gd) contrast agent or oxygen gas to identify the rupture of the blood brain barrier (BBB) and the temporal ICH. Results: Most rats developed hypertensive ICH stroke in the high salt group during the 10–13 week period compared to only one third of rats in the low salt group during the 14–18 week period. The location of stroke for both the low/high-salt groups was highest in the striatum (58%/43%), followed by the cortex (21%/30%). The edematous enhancement on T2 weighted (T2W) imaging or Gd based T1 weighted (Gd-T1W) imaging due to the ruptured BBB preceded the striatal hemorrhages seen on SWI. The most recent bleeds were observed on temporal SWI or on oxygen-enhanced SWI. The mode of the volume of bleeds was 0.4 mm 3 . A positive correlation between susceptibility x volume and R2* x volume of the bleeds was observed. Conclusions: SHRSP rats with the high salt diet effectively generated a hypertensive hemorrhagic stroke which could be monitored by various MRI sequences. The venous dilation on SWI may precede any abnormality on T2W or Gd-T1W imaging. The edematous enhancement on T2W or Gd-T1W indicated a BBB breakdown that may precede striatal ICH by several days. This suggests the need for immediate treatment to improve outcome if this finding is observed. Here, the use of oxygen with SWI was able to help differentiate old bleeds from very recent bleeds.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Long-term thermal stability and calibration of Type-II fiber Bragg grating array inscribed in radiation-hardened fibers

This paper investigates the long-term thermal stability of Type-II fiber Bragg grating (FBG) arrays, inscribed by femtosecond laser in radiation-hardened fiber, for potential applications as multiplexed sensors in high-temperature energy systems. The thermal stability of FBG sensors was assessed through 16 thermal cycles from room temperature (RT) to 750 ℃ about two months, involving 100 FBG sensors. The results show that the absolute temperature drift of FBG sensors can be reduced to less than 0.4 pm/day after 54 h thermal annealing process at a constant temperature of 800 ℃. As temperature sensors, the FBGs demonstrated stable performance, achieving a standard deviation (STD) of 1.8 pm (corresponding to a temperature resolution of 0.118 ℃) post-annealing. Repeated thermal cycles revealed a random drift of 2.3 pm in the FBG wavelength at RT. Polynomial fitting was explored as a calibration method to convert FBG wavelength shifts into absolute temperature measurements. By optimizing calibration temperature points (RT, 200 ℃, 400 ℃, and 750 ℃), the study shows that cubic polynomial calibration using four points yields an average R2 of 0.9997 and an RMSE of 3.58 ℃ across the entire temperature range (RT to 750 ℃). This approach represents an 11.53-fold improvement over empirical slope calibration and a 1.34-fold improvement over four-point piecewise fitting. The findings indicate that Type-II FBGs inscribed in radiation-hardened fibers can function as accurate temperature sensors, with performance on par with or exceeding that of thermocouples. With their multiplexing capability, robust signal transmission over long lead cables, and immunity to electromagnetic interference, FBG sensors offer a promising alternative to traditional electronic sensors for energy system monitoring.

Dominguez-Ontiveros, Elvis [ORNL] (ORCID:000000018↗

DOE OSTI2022

Bay capping via acetylene addition to polycyclic aromatic hydrocarbons: Mechanism and kinetics

The bay-capping mechanism on PAH armchair edges and the kinetics of acetylene addition to 6–6–5 and 5–6–5 bays have been explored by ab initio/RRKM-ME calculations. The bays on the edges were modeled by C 21 H 11 and C 20 H 9 radicals produced by H abstractions from 7H-benzo[c]cyclopenta[e]pyrene and dicyclopenta[cf]pyrene. The C 20 H 9 + C 2 H 2 reaction is shown to have a low entrance barrier and to rapidly form the capped product, indaceno[2,1,8,7-cdefg]pyrene, along with ethynyl substituted dicyclopenta[cf]pyrene at temperatures above 1400 K. The reactivity of C 21 H 11 is shown to be governed by the location of the unpaired electron; the π radical R1 formed by H abstraction from the CH 2 group in 7H-benzo[c]cyclopenta[e]pyrene reacts with C 2 H 2 very slowly owing to a high entrance barrier, with the bay-capping rate constant approaching 10 -16 cm 3 molecule -1 s -1 only at temperatures above 2000K. Further, this result reaffirms that the growth of π aryl radicals via acetylene addition is inefficient and reflects the generally low reactivity of such radicals where the spin density is highly delocalized over the entire polyaromatic system. Alternatively, the σ C 21 H 11 radical R2 produced by H abstraction from the five-membered ring at the bay rapidly reacts with C 2 H 2 forming the bay-capped product, with the rate constant on the order of 10 -12 cm 3 molecule -1 s -1 at T≥1500K. Rate constants for the capping reactions at the 6–6–5 and 5–6–5 bays are compared with those at the 6–0–6, 6–6–6, and 6–5–6 bays. The site-specific bay-capping rate constants have been utilized in kMC simulations of the PAH growth and the results showed measurable differences when the 6–6–5 and 5–6–5 bay-capping reactions are taken into account, including an increase of the growth rate and the formation of closed-shell PAH and a rise of the number of embedded five-membered rings accompanied with a slight decrease of their overall amount.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Analysis of alternative climate datasets and evapotranspiration methods for the Upper Mississippi River Basin using SWAT within HAWQS

This study reports the application of Soil andWater Assessment Tool (SWAT) within the Hydrologic andWater Quality System(HAWQS) on-line platform, for the UpperMississippi River Basin (UMRB). TheUMRB is an important ecosystem located in the north central U.S. that is experiencing a range of ecological stresses. Specifically, testing of SWAT was performed for: (1) Hargreaves (HG) and Penman-Monteith (PM) PET methods, and (2) Livneh, National Climatic Data Center (NCDC) and Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate datasets. The Livneh-PM combination resulted in the highest average annual water yield of 380.6 mm versus the lowest estimated water yield of 193.9 mm for the Livneh-HG combination, in response to 23-year uncalibrated simulations. Higher annual ET and PET values were predicted with HG method versus the PM method for all three weather datasets in response to the uncalibrated simulations, due primarily to higher HG-based estimates during the growing season. Based on these results, it was found that the HG method is the preferred PET option for the UMRB. Initial calibration of SWAT was performed using the Livneh data and HGmethod for threeMississippi Rivermain stem gauge sites, which was followed by spatial validation at 10 other gauge sites located within the UMRB streamnetwork. Overall satisfactory results were found for the calibration and validation gauge sites, with the majority of R2 values ranging between 0.61 and 0.82, Nash- Sutcliffe modeling efficiency (NSE) values ranging between 0.50 and 0.79, and Kling-Gupta efficiency (KGE) values ranging between 0.61 and 0.84. The results of an additional experimental suite of six scenarios,which represented different combinations of climate data sets and calibrated parameters, revealed that suggested statistical criteria were again satisfied by the different scenario combinations. Overall, the PRISM data exhibited the strongest reliability for the UMRB.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Predicting measures of soil health using the microbiome and supervised machine learning

Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ~0.65, for categorical assessments, and a R2 value of ~0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae, and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Furthermore, our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health.

16S rRNA gene↗

DOE OSTI2022

Microbe-iron interactions control lignin decomposition in soil

Lignin decomposition is critically linked to terrestrial carbon (C) cycle due to the enormous C mass of lignin and its importance in controlling overall rates of litter decomposition. Interactions between lignin and iron (Fe) minerals have been increasingly recognized as key mediators of lignin decomposition in experimental studies. However, we still lack a quantitative understanding of how Fe minerals interact with microbes to control lignin decomposition. Here, we leveraged experimental results from an incubation of Fe-rich soil, in which lignin decomposition rates were measured at aerobic conditions after four levels of pre-treated O2 availability, to examine microbe-Fe (MiFe) interactions in lignin decomposition with a MiFe model. We quantified how Fe redox cycling interacted with microbial activities to control lignin decomposition via data-model integration. Our results showed that the MiFe model with time-dependent growth and mortality functions better represented CO2 release from lignin decomposition (R2 ranging from 0.96 to 0.97) than models assuming either first-order or Michaelis-Menten kinetics. Reduction of Fe(III) to Fe(II) after pre-treatments with lower O2 availability stimu-lated the Fenton reaction to break down macro-molecular lignin into small molecules available to microbes. The small molecules of lignin and necromass bounded with oxidized Fe and were protected from decomposition. After 1-year incubation, the model implied that most of C stabilized with Fe minerals was derived from small molecular lignin C. Our quantitative analysis of microbe-Fe interactions sheds new light on lignin decomposition and preservation and helps improve model prediction of soil C persistence under global change.

Liao, Cuijuan↗

DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗

DOE OSTI2021

Effects of varying gelatin coating concentrations on RANKL induced osteoclastogenesis

Here, we assessed the effects of varying concentrations of gelatin coating on Receptor Activator of Nuclear Factor κ-B Ligand (RANKL)-induced RAW264.7 murine macrophage differentiation into osteoclast (OC) via osteoclastogenesis. The microstructures of coating surfaces with different concentrations of gelatin were examined by scanning electron microscopy and atomic force microscopy. Increased gelatin coating concentrations led to decreased gel rigidity but increased surface adhesion force attenuated OC differentiation and the decreased actin ring formation in RANKL-induced osteoclastogenesis. The decreased actin ring formation is associated with decreased lysosomal-associated membrane protein 1 (LAMP1) activity and bone resorption in the differentiated OCs with different gelatin coating concentrations as compared to the cells differentiated without gelatin coatings. In addition, increasing concentrations of gelatin coating attenuated the medium TGF-β1 protein levels and the expression levels of TGF-β and type-I (R1) and type-II (R2) TGF-β receptors in OCs, suggesting the gelatin-induced suppression of TGF-β signaling for the regulation of RNAKL-induced OC differentiation. Taken together, these findings showed that changes in gelatin coating concentrations, which were associated with altered gel thickness and substrate rigidity, might attenuate TGF-β signaling events to modulate OC differentiation and concomitant actin ring formation and bone matrix resorption in RANKL-induced osteoclastogenesis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Crystal structure of diclazuril, C 17 H 9 Cl 3 N 4 O 2

The crystal structure of diclazuril has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Diclazuril crystallizes in space group P2 1 /a (#14) with a = 27.02080(18), b = 11.42308(8), c = 5.36978(5) Å, β = 91.7912(7)°, V = 1656.629(15) Å 3 , and Z = 4. The crystal structure consists of layers of molecules parallel to the ac-plane. A strong N–H∙∙∙O hydrogen bond links the molecules into dimers along the a-axis with a graph set R2,2(8). The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗

DOE OSTI2022

Crystal structure of vismodegib, C 19 H 14 Cl 2 N 2 O 3 S

The crystal structure of vismodegib has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Vismodegib crystallizes in space group P2 1 /a (#14) with a = 16.92070(20), b = 10.20235(4), c = 12.16161(10) Å, β = 108.6802(3)°, V = 1988.873(9) Å 3 , and Z = 4. The crystal structure consists of corrugated layers of molecules parallel to the bc-plane. There is only one classical hydrogen bond in the structure, between the amide nitrogen atom and the N atom of the pyridine ring. Pairs of these hydrogen bonds link the molecules into dimers, with a graph set R2,2(14) > a > a. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗

DOE OSTI2023

Crystal structure of oxibendazole, C 12 H 15 N 3 O 3

The crystal structure of oxibendazole has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Oxibendazole crystallizes in space group C2/c (#15) with a = 23.18673(22), b = 5.35136(5), c = 19.88932(13) Å, β = 97.0876(9)°, V = 2449.018(17) Å 3 , and Z = 8. The structure consists of hydrogen-bonded layers of planar molecules parallel to the bc-plane. Strong N–H∙∙∙N hydrogen bonds link the molecules into dimers, with a graph set R2,2(8). N–H∙∙∙O hydrogen bonds further link these dimers into layers parallel to the bc-plane. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗

DOE OSTI2023

Crystal structure of elvitegravir Form II, C 23 H 23 ClFNO 5

The crystal structure of elvitegravir Form II has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Elvitegravir Form II crystallizes in space group P2 1 (#4) with a = 11.54842(7), b = 14.04367(5), c = 13.33333(8) Å, β = 90.0330(6)°, V = 2162.427(14) Å 3 , and Z = 4. The crystal structure consists of alternating layers of parallel molecules perpendicular to the b-axis. The mean planes of the oxoquinoline ring systems in molecules 1 and 2 are 1(22)-1 and -1(22)1. Between the stacks are layers of the halogenated phenyl rings. These exhibit herringbone stacking. In each molecule, the carboxylic acid group forms a strong intramolecular O–H∙∙∙O hydrogen bond to the nearby carbonyl group. The hydroxyl group of each molecule forms a strong hydrogen bond to the carbonyl group of the carboxylic acid of the other molecule. These O–H∙∙∙O hydrogen bonds link the molecules into dimers, with a graph set R2,2(18) > a > c. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗

DOE OSTI2021

Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptides into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental data set from the Immune Epitope Database, and theoretical data sets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of nine neutral residues from a six-residue subset with an average R 2 coefficient of determination value of 0.62 ± 0.04 (±95% CI), average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an receiver operating characteristic (ROC) AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 ± 0.04, average error of 0.07 ± 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting seven neutral residues from an eight-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗