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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

PANNA: Properties from Artificial Neural Network Architectures

We report prediction of material properties from first principles is often a computationally expensive task. Recently, artificial neural networks and other machine learning approaches have been successfully employed to obtain accurate models at a low computational cost by leveraging existing example data. Here, we present a software package “Properties from Artificial Neural Network Architectures” (PANNA) that provides a comprehensive toolkit for creating neural network models for atomistic systems following the Behler–Parrinello topology. Besides the core routines for neural network training, it includes data parser, descriptor builder for Behler–Parrinello class of symmetry functions and force-field generator suitable for integration within molecular dynamics packages. PANNA offers a variety of activation and cost functions, regularization methods, as well as the possibility of using fully-connected networks with custom size for each atomic species. PANNA benefits from the optimization and hardware-flexibility of the underlying TensorFlow engine which allows it to be used on multiple CPU/GPU/TPU systems, making it possible to develop and optimize neural network models based on large datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Electronic parameters in cobalt-based perovskite-type oxides as descriptors for chemocatalytic reactions

Perovskite-type transition metal (TM) oxides are effective catalysts in oxidation and decomposition reactions. Yet, the effect of compositional variation on catalytic efficacy is not well understood. The present analysis of electronic characteristics of B-site substituted LaCoO3 derivatives via in situ X-ray absorption spectroscopy (XAS) establishes correlations of electronic parameters with reaction rates: TM t2g and eg orbital occupancy yield volcano-type or non-linear correlations with NO oxidation, CO oxidation and N2O decomposition rates. Covalent O 2p-TM 3d interaction, in ultra-high vacuum, is a linear descriptor for reaction rates in NO oxidation and CO oxidation, and for N2O decomposition rates in O2 presence. Covalency crucially determines the ability of the catalytically active sites to interact with surface species during the kinetically relevant step of the reaction. The nature of the kinetically relevant step and of surface species involved lead to the vast effect of XAS measurement conditions on the validity of correlations.

Science & Technology - Other Topics↗
DOE OSTI2022

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2022

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure–Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD 50 ). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R 2 = 0.88), validation set (R 2 = 0.95), and true external test set (R 2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Identification of Active Metal Carbide and Nitride Catalytic Facets for Hydrodeoxygenation Reactions

The catalytic hydrodeoxygenation (HDO) reaction is of considerable interest for biomass conversion to valuable chemicals and fuels, where one of the critical bottlenecks is the lack of cost-effective and efficient catalysts. To discover cost-efficient catalysts for the HDO reaction, we employed a density functional theory-based hierarchical catalyst design strategy based on catalytic descriptors, reaction energy profiles, and microkinetic modeling (MKM). We focused on the carbide and nitride catalyst space, for which we calculated 121 catalyst surfaces of Mo 2 C, MoC, Mo 2 N, W 2 C, NbC, VC, VN, and NbN catalysts. Based on the computed surface energies, reaction energies of oxygen removal, carbon binding strength, and the surface area of nanoparticles, the likely active facets are the Mo 2 C(111), MoC(011), VN(100), Mo 2 N(001), Mo 2 N(011), and Mo 2 N(100) surfaces. Further, detailed energy profiles were obtained, and MKM was performed for a model reaction (glycolaldehyde + 2H 2 . ethylene + 2H 2 O) on the Mo 2 C(111), VN(100), and MoC(100) surfaces. Based on the computed volcano map obtained from MKM, the predicted active facets for this HDO reaction are the Mo 2 C(111), MoC(011), VN(011), Mo 2 N(001), Mo 2 N(011), and Mo 2 N(100) surfaces. Additionally, none of the carbide and nitride catalyst surfaces are located in the optimal catalytic activity part. Therefore, it is essential to modify the catalyst via adding dopants or alloying to improve the catalytic activity. Catalytic modifications that can destabilize the surface adsorption of O*/H 2 O* and decrease the energy barriers of O-H bond formation are recommended to facilitate the HDO on the carbide and nitride catalysts. These a priori investigations provide guidelines for future low-cost HDO catalyst development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A complete grain-level assessment of the stress-strain evolution and associated deformation response in polycrystalline alloys

Polycrystalline alloys are used pervasively across structural applications contingent upon extensive experimental testing. A statistically representative number of tests are required to expose the variability in the material's performance, as a result of non-uniform microstructures and associated micromechanical fields. In a more direct means of capturing this pertinent information, multi-modal experimental techniques are presented to measure and track the complete micromechanical state, evolving during loading, of each and every grain within the regions of interest. Specifically, a combination of high-energy X-ray diffraction microscopy and digital image correlation coupled with electron backscatter diffraction are conducted on a specimen for each of the alloys, Haynes 282 and Ti7Al. The results of the multi-modal analysis definitively demonstrate that the degree of heterogeneity increases with deformation level and is used to assess the number of grains necessary for a representative volume element description of the stress state for each of these materials. Moreover, higher resolution imaging is used for identification of the slip system activity and subsequently used to study slip transmission events. An accurate knowledge of the resolved shear stress in adjacent grains (grain interactions) is demonstrated to be a key descriptor of the slip transmission events.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Toward the Development of a High-Voltage Mg Cathode Using a Chromium Sulfide Host

Development of Mg-ion batteries as advanced electrochemical energy storage systems relies on the design and discovery of high-voltage positive electrode (cathode) materials. To date, a variety of sulfide cathodes have been reported (e.g., Mg x Mo 6 S 8 , Mg x Ti 2 S4, etc.), but the voltages of these materials are too low to prepare a high energy density Mg cell. Theoretical computations predicted that Mg x Cr 2 S 4 operating with the high-voltage Cr 3+/4+ redox couple would serve as a suitable cathode candidate, but experimental attempts to extract Mg 2+ from the lattice have been largely unsuccessful. Here, we show that reversible electrochemical activity within a thiospinel framework (AB 2 S 4 ) relies on a redox-active transition metal present in the B site; otherwise, anionic redox activity triggers decomposition of the spinel structure. Since Cr and S states are highly coupled in MgCr 2 S 4 , the Cr 3+/4+ redox couple is inaccessible so that reversible (de)intercalation of Mg 2+ cannot occur and charging leads to dissolution of the active material. These findings point to an insufficiency in the screening criteria previously used to identify MgCr 2 S 4 as a promising Mg cathode. Thus, a computable descriptor based on the electronic structure of the discharged material is proposed to predict the prevalence of cation vs anion redox and improve future surveys of potential candidates. It is unlikely that the high-voltage Cr redox couple will be accessible to oxidation in the presence of sulfur within the restrictions of a spinel framework; however, it is possible that a suitable layered Mg x CrS 2 structure could serve as a reversible high-voltage Mg cathode.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Limits of Detection for EXAFS Characterization of Heterogeneous Single-Atom Catalysts

Single-atom catalysts (SACs), consisting of individual metal atoms dispersed on a support, attract attention due to their unique reactivity, efficient use of precious metals, and precise chemical tunability. Characterization of the metal species is crucial to substantiate structure–function relationships. Authors often use—and referees often require—X-ray absorption spectroscopy (XAS) data to prove the absence of clustered metal (or metal oxide) structures after pre-treatment and under in situ or operando conditions. However, there has been no critical assessment of the limitations of XAS in substantiating such conclusive statements, which is particularly important given the potential outsized influence of minority catalyst structures in dictating catalytic activity. Here, in this article, we quantitatively assess the detection limits of XAS to identify metal (or metal oxide) clusters in samples containing predominantly single atoms by modeling the extended X-ray absorption fine structure (EXAFS) of mixtures of structures. We identified that a significant fraction of clusters can coexist with SAC active sites (e.g., ~10% metallic Pt or ~40% oxidized Pt clusters in Pt/CeO 2 SACs), while eluding detection via EXAFS with any statistical significance. To generalize these conclusions, a descriptor-based screening of bulk metal oxides using a continuous Cauchy wavelet transform was proposed that suggests certain materials for which differentiating atomically dispersed metal species and metal oxide clusters would be infeasible by EXAFS (e.g., ReO x ). Based on this analysis, we suggest best practices for the study of SACs using EXAFS and provide recommendations to ensure that conclusions do not outpace the evidence used to support them. In this rapidly expanding research area, rigorous characterization will lead to greater understanding of the behavior of SACs and ultimately improved catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Anion Association Strength as a Unifying Descriptor for the Reversibility of Divalent Metal Deposition in Nonaqueous Electrolytes

Developing next-generation battery chemistries that move beyond traditional Li-ion systems is critical to enabling transformative advances in electrified transportation and grid-level energy storage. Here, we provide the first evidence for common descriptors for improved reversibility of metal plating/stripping in nonaqueous electrolytes for multivalent ion batteries. Focusing first on the specific role of chloride (Cl – ) in promoting electrochemical reversibility in multivalent systems, rotating disk (RDE) and ring-disk electrode (RRDE) investigations were performed utilizing a variety of divalent cations (Mg 2+ , Zn 2+ , and Cu 2+ ) and the bis-(trifluoromethane sulfonyl) imide (TFSI – ) anion. By introducing varying concentrations of Cl – , a cooperative effect is observed between TFSI – and Cl – that yields the more reversible behavior of mixed electrolytes relative to electrolytes containing only TFSI – . This effect is shown to be general for Mg, Zn, and Cu electrodeposition, and mechanistic understanding of the role of Cl – in improving reversibility of TFSI-based electrolytes is obtained through the combination of R(R)DE experimental results and density functional theory (DFT) evaluation of the redox activity and thermodynamic stability of various TFSI- and Cl-based solution complexes of metal ions. The cooperative anion effect is further generalized to other mixed-anion systems, where systematic variations in anion association strength predicted from DFT (i.e., Cl – > OTf – ≈ TFSI – > BF 4 – > PF 6 – ) yield corresponding trends in redox potentials and improvements of ≥200 mV in the reversibility of metal deposition/dissolution. These results identify anion association strength as a common descriptor for the reversibility of divalent metal anodes and suggest a set of general design principles for developing new electrolytes with improved activity and stability.

25 ENERGY STORAGE↗
DOE OSTI2022

Medium-independent hydrogen atom binding isotherms of nickel oxide electrodes

Adsorption and transfer of hydrogen atoms at solid/solution interfaces are fundamental to heterogeneous catalysis for chemical energy transformations and other processes. Reported here are electrochemical and spectroelectrochemical measurements of the thermodynamics of H-atom binding to nickel oxide electrodes, both the average and the distribution of NiO–H bond dissociation free energies (BDFEs). These are perhaps the first measurements of binding isotherms at non-metal electrodes. Remarkably, both the BDFEs and the non-Langmuirian isotherms are the same in water, acetonitrile, and dimethylformamide, and with different buffers and proton activities. Such medium independence of the BDFEs and isotherms has not been previously reported for any binary material. The medium independence supports the common use of computed hydrogen binding energies as intrinsic descriptors of surface reactivity, while the broadened isotherms add a level of complexity to such analyses. Furthermore, this work demonstrates the capability to derive key thermodynamic parameters at chemically reactive solid-liquid interfaces.

13 HYDRO ENERGY↗
DOE OSTI2020

O 2 dosage as a descriptor of TWC performance under lean/rich dithering in stoichiometric natural gas engines

The effects of oscillations in gas composition, known as lean/rich dithering, on the performance of a commercial Pd-based three-way catalyst (TWC) for stoichiometric natural gas (NG) engines were evaluated using synthetic exhaust flow reactor experiments. Under simulated NG exhaust conditions, NO conversion was intimately correlated to CH 4 conversion at slightly fuel-rich operating conditions. CH 4 conversion significantly varied with lambda (or O 2 concentration) and depended on the direction of the lambda change. The dynamic CH 4 conversion is likely related to the change of catalyst oxidation state and structures of Pd active sites. The CH 4 -NO cross-over point was found at a rich-biased lambda rather than stoichiometry. Compared to static operation, catalyst performance was much higher under a realistic dithering condition. The impacts of dithering parameters including amplitude and frequency on CH 4 and NO conversions were explored. O 2 dosage is proposed as an effective descriptor of the TWC dithering performance. In the O 2 -dosage space, the performance of distinct dithering parameters collapse, helping to elucidate the fundamental influence of dithering parameters on TWC performance. With the help of O2 dosage, an optimal operating window leading to high CH 4 and NO conversions was identified. A higher dithering amplitude is required to keep the catalyst in a reduced state for higher CH 4 and NO conversions, while an appropriate O 2 dosage that does not exceed the breakthrough OSC is necessary to avoid excess O 2 that results in NO slip. The dithering amplitude and O 2 -dosage-metric maps provide a new method for studying dynamic TWC performance and identifying optimum operation strategies.

03 NATURAL GAS↗
DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗
DOE OSTI2024

Computational discovery of fast interstitial oxygen conductors

New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, for example, below ~400 °C. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here, in this study, we combine physically motivated structure and property descriptors, ab initio simulations and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found, which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La 4 Mn 5 Si 4 O 22+δ , a representative member of the perrierite/chevkinite family. We found that La 4 Mn 5 Si 4 O 22+δ has higher oxygen-ion conductivity than the widely used yttria-stabilized ZrO 2 , and among the highest surface oxygen exchange rates at the intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. We propose that the essential features for forming an effective interstitial oxygen conductor are the availability of electrons and structural flexibility, enabling a sufficient accessible volume. This work provides a powerful approach for understanding and discovering new interstitial oxygen conductors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces

Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.

Adsorption↗
DOE OSTI2021

Bias free multiobjective active learning for materials design and discovery

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗