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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The effects of warming and soil chemistry on bacterial community structure in Arctic tundra soils

Soil microbial communities in the Arctic play a critical role in regulating the global carbon (C) cycle. Vast amounts of C are stored in northern latitude soils, and rising temperatures in the Arctic threaten to thaw permafrost, making relatively inaccessible C sources more available for mineralization by soil microbes. Few studies have characterized how microbial community structure responds to thawing permafrost in the context of varying soil chemistries associated with contrasting tundra landscapes. Here, we subjected active layer and permafrost soils from upland and lowland tundra sites on the North Slope of Alaska to a soil-warming incubation experiment and compared soil bacterial community profiles (obtained by 16S rRNA amplicon sequencing) before and after incubation. The influence of soil composition (characterized by mid-infrared [MIR] spectroscopy) on bacterial community structure and class abundance was analyzed using redundancy and correlation analyses. We found increased abundances of Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes [Sphingobacteriia] post incubation, particularly in permafrost soils. The categorical descriptors site and soil layer had the most explanatory power in our predictive models of bacterial community structure, highlighting the close relationship between soil bacteria and the soil environment. Specific soil chemical attributes characterizing the soil environments that were found to be the best predictors include MIR spectral bands associated with inorganic C, silicates, amide II (C=N stretch), and carboxylics (C-O stretch), and MIR peak ratios representing C substrate quality. Overall, these results further characterize soil bacterial community shifts that may occur as a frozen environment with limited access to C sources, such as is found in undisturbed permafrost, transitions to warmer and more C-available environments, such as is predicted in thawing permafrost due to climate change.

16S rRNA sequencing↗
DOE OSTI2025

Systematic computational study of oxide adsorption properties for applications in photocatalytic CO 2 reduction

While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understanding of semiconductor catalysts has not yet been achieved. In this work, we present a high-throughput density functional theory investigation into the adsorption properties of 5 oxides of interest to the photocatalytic CO 2 reduction reaction: TiO 2 (rutile and anatase), SrTiO 3 , NaTaO 3 , and CeO 2 . Using a systematic approach, we exhaustively identify unique surfaces and construct adsorption structures to undergo geometry optimizations. We then perform a data-driven analysis, which reveals the presence of weak adsorption energy scaling relations, the propensity of adsorbates of interest to interact with oxygen surface sites, and the importance of slab deformation upon adsorption. Our findings are presented in the context of experimental observations and in comparison to previously studied classes of catalysts, such as pure metals and tellurium-containing semiconductors, and reinforce the need for a comprehensive approach to the study of site-specific surface phenomena on semiconductors.

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

Electrocatalysts Development for Hydrogen Oxidation Reaction in Alkaline Media: from Mechanism Understanding to Materials Design

Anion exchange member (AEM) fuel cells have gained great attention recently mainly owing to the advantages of non-precious metal catalysts application. Despite of the similar oxygen reduction reaction (ORR) kinetics in acid and base, the reaction kinetics of hydrogen oxidation reaction (HOR) is two orders of magnitude slower in alkaline systems than in acid. To understand the slower kinetics of HOR in base, two major interpretations have been proposed, including 1) hydrogen binding energy as a major descriptor for HOR in both acid and base; and 2) bifunctional theory based on the contributions from both hydrogen and hydroxide adsorption for HOR in alkaline electrolyte. Here, we discuss the possible HOR mechanisms in alkaline electrolytes with the corresponding Tafel behavior change. Besides the discussion of traditional Tafel-Volmer and Heyrovsky-Volmer HOR mechanisms, the recently proposed hydroxide adsorption step is discussed to illustrate the HOR mechanism difference in acid and base. We further correlate the hydroxide adsorption step involved mechanism with HOR catalyst design, and summarize its role in HOR and HER activities improvement in alkaline electrolyte over the same catalyst. The representative works of alkaline HOR catalyst development were briefly introduced, and the strategy of strengthening interaction between H2 desorption and H2O recombination was finally proposed for future HOR catalyst design in alkaline environment.

Hydrogen oxidation reaction, Hydrogen evolution re↗
DOE OSTI2024

Metal Independent Correlations for Site-specific Binding Energies of Relevant Catalytic Intermediates

Establishing energy correlations among different metals can accelerate the discovery of efficient and cost-effective catalysts for complex reactions. Using a recently introduced coordination-based model, we can predict site specific metal binding energies (∆EM) that can be used as a descriptor for chemical reactions. In this study, we have examined a range of metals including Ag, Au, Co, Cu, Ir, Ni, Os, Pd, Pt, Rh, and Ru and found linear correlations between predicted ∆EM and adsorption energies of CH and O (∆ECH and ∆EO) at various coordination environment for all the considered metals. Interestingly, all the metals correlate with one another under specific surface site coordination, indicating that different metals are interrelated in a particular coordination environment. Furthermore, we have tested and verified for PtPd and PtIr-based alloys that they follow a similar behavior. Moreover, we have expanded the metal space by taking some early transition metals along with few s-block metals and shown a cyclic behavior of the adsorbate binding energy (∆EA) versus ∆EM. Therefore, ∆ECH and ∆EO can be efficiently interpolated between metals, alloys and intermetallics based on information related to one metal only. This simplifies the process of screening new metal catalyst formulations and their reaction energies.

Density function theory↗
DOE OSTI2024

Development of a Platform for High-Resolution Ion Mobility Separations Coupled with Messenger Tagging Infrared Spectroscopy for High-Precision Structural Characterizations

The ability to uniquely identify a compound requires highly precise and orthogonal measurements. Here we describe a newly developed analytical platform that integrates high resolution ion mobility and cryogenic vibrational ion spectroscopy for high-precision structural characterizations. This platform allows for the temporal separation of isomeric/isobaric ions and provides a highly sensitive description of the ion’s adopted geometry in the gas phase. The combination of these orthogonal structural measurements yields precise descriptors that can be used to resolve between and confidently identify highly similar ions. The unique benefits of our instrument, which integrates a structures for lossless ion manipulations ion mobility (SLIM IM) device with messenger tagging infrared spectroscopy, include the ability to perform high-resolution ion mobility separations and to record the IR spectra of all ions simultaneously. The SLIM IM device, with its 13 m separation path length, allows for multipass experiments to be performed for increased resolution as needed. It is integrated with an Agilent qTOF MS where the collision cell was replaced with a cryogenically held (30 K) TW-SLIM module. The cryo-SLIM is operated in a novel manner that allows ions to be streamed through the device and collisionally cooled to a temperature where they can form noncovalently bound N 2 complexes that are maintained as they exit the device and are detected by the TOF mass analyzer. The instrument can be operated in two modes: IMS+IR where the IR spectra for mobility-selected ions can be recorded and IR-only mode where the IR spectra for all mass-resolved ions can be recorded. In IR-only mode, IR spectra (400 cm –1 spectral range) can be recorded in as short as 2 s for high throughput measurements. Further, this work details the construction of the instrument and modes of operation. It provides initial benchmarking of CCS and IR measurements to demonstrate the utility of this instrument for targeted and untargeted approaches.

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

Production of C 2 /C 3 Oxygenates from Planar Copper Nitride-Derived Mesoporous Copper via Electrochemical Reduction of CO 2

Electrochemical reduction of CO 2 provides an opportunity to produce fuels and chemicals in a carbon-neutral manner, assuming that CO 2 can be captured from the atmosphere. To do so requires efficient, selective, and stable catalysts. In this study, we report a highly mesoporous metallic Cu catalyst prepared by electrochemical reduction of thermally nitrided Cu foil. Under aqueous saturated CO 2 reduction conditions, the Cu 3 N-derived Cu electrocatalyst produces virtually no CH 4 , very little CO, and exhibits a faradaic efficiency of 68% in C 2+ products (C 2 H 4 , C 2 H 5 OH, and C 3 H 7 OH) at a current density of ~18.5 mA cm –2 and a cathode potential of -1.0 V versus the reversible hydrogen electrode. Under these conditions, the catalyst produces more oxygenated products than hydrocarbons. We show that surface roughness is a good descriptor of catalytic performance. The roughest surface reached 98% CO utilization efficiency for C 2+ product formation from CO 2 reduction and the ratio of oxygenated to hydrocarbon products correlates with the degree of surface roughness. These effects of surface roughness are attributed to the high population of undercoordinated sites as well as a high pH environment within the mesopores and adjacent to the surface of the catalyst.

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

The Role of Cation Coordination in the Electrical and Optical Properties of Amorphous Transparent Conducting Oxides

Amorphous oxide semiconductor materials have demonstrated numerous advantages without compromise of electrical properties as compared to their crystalline counterparts, yet understanding of the fundamental principles allowing this has remained elusive. To study the origins of enhanced optoelectronic properties, we apply high-throughput, combinatorial sputtering, structural and spectral mapping, and computationally intensive ab initio molecular dynamics simulations with density functional theory to a ternary, post-transition metal oxide system, namely, zinc tin oxide. The deposited thin films exhibit a high figure of merit, achieving carrier densities in the range of 1019 to 1020 cm–3 and carrier mobilities up to 35 cm2/Vs. These results highlight the role of local distortions and cation coordination in determining the microscopic origins of carrier generation and transport. In particular, we identify the strong likelihood of Sn undercoordination in both Zn-poor and Zn-rich phases leading to the high carrier concentrations observed. This not only diverges from the still widespread historical indictment of oxygen vacancies controlling carrier population in crystalline oxides but also provides a comprehensive framework to describe the unique structure–property relationships using specific structural and electronic descriptors in disordered phase materials.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS,M↗
DOE OSTI2020

Predicting the Phase Stability of Multicomponent High-Entropy Compounds

A generic method to estimate the relative feasibility of formation of high-entropy compounds in a single phase, directly from first principles, is developed. As a first step, the relative formation abilities of 56 multicomponent, AO, oxides were evaluated. These were constructed from five cation combinations chosen from A = {Ca, Co, Cu, Fe, Mg, Mn, Ni, Zn}. Candidates for multicomponent oxides are predicted from descriptors related to the enthalpy and configurational entropy obtained from the mixing enthalpies of two-component oxides. The utility of this approach is evaluated by comparing the predicted combinations with the experimentally realized entropy-stabilized oxide, (MgCoCuNiZn)O. In the second step, Monte Carlo simulations are utilized to investigate the phase composition and local ionic segregation as a function of temperature. This approach allows for the evaluation of potential secondary phases, thereby making realistic predictions of novel multicomponent compounds that can be synthesized.

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

Computationally Accelerated Discovery of High Entropy Pyrochlore Oxides

High entropy ceramics provide enhanced flexibility for tailoring a wide range of physical properties, emerging from the diverse chemical and configurational degrees of freedom. Expanding upon the endeavors of recently synthesized high entropy ceramics in rock salt, fluorite, spinel, and perovskite structures, we explore the relative feasibility of formation of high entropy pyrochlore oxides, A 2 B 2 O 7 , with multication occupancy of the B-site, estimated from first-principles-based thermodynamic descriptors. Subsequently, we used Monte Carlo simulations to estimate the phase composition, oxygen vacancy concentration, and local ionic segregation as a function of temperature and oxygen partial pressure. In synergy with the theoretical calculations, we have investigated the synthesis of several multicomponent oxides with a pyrochlore composition, related to our computational investigations, resulting in four phase pure and one 97.4% pure high entropy pyrochlore oxides. Ultimately, our approach allows us to evaluate potential impurity phases, ionic disorder, and oxygen vacancy concentration in response to the experimental variables, thereby making realistic predictions that can direct and accelerate experimental synthesis of novel multicomponent ceramics.

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

Covalent Functionalization of Nickel Phosphide Nanocrystals with Aryl-Diazonium Salts

Covalent functionalization of Ni 2 P nanocrystals was demonstrated using aryl-diazonium salts. Spontaneous adsorption of aryl functional groups was observed, with surface coverages ranging from 20 to 96% depending on the native reactivity of the salt as determined by the aryl substitution pattern. Increased coverage was possible for low reactivity species using a sacrificial reductant. Functionalization was confirmed using thermogravimetric analysis, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The structure and energetics of this nanocrystal electrocatalyst system, as a function of ligand coverage, were explored with density functional theory calculations. The Hammett parameter of the surface functional group was found to linearly correlate with the change in Ni and P core-electron binding energies and the nanocrystal's experimentally and computationally determined work function. The electrocatalytic activity and stability of the functionalized nanocrystals for hydrogen evolution were also improved when compared to the unfunctionalized material, but a simple trend based on electrostatics was not evident. Density functional theory was used to understand this discrepancy, revealing that H adsorption energies on the covalently functionalized Ni 2 P also do not follow the electrostatic trend and are predictive descriptors of the experimental results.

14 SOLAR ENERGY↗
DOE OSTI2024

Artificial Intelligence for Conjugated Polymers

Conjugated polymers have garnered significant attention due to their diverse applications in electronics, photonics, and energy storage. However, realizing their full potential poses a formidable challenge, as their design has historically relied on iterative adjustments and continuous inspiration from researchers. Traditional methods often struggle to efficiently navigate their vast chemical landscape. In this work, the application of artificial intelligence (AI), specifically machine learning (ML), needs to be discussed in the realm of conjugated polymers. Our paper emphasizes the importance of understanding the structure–property relationships of these polymers and how ML can facilitate property prediction and inverse-design. We delve into various chemical fingerprints, structural descriptors, and ML algorithms, showcasing their utility across a spectrum of applications, including simulations, glass transition temperature determination, photovoltaics, reorganization energy for charge transport, photocatalysts, and sensors. Finally, we give some outlooks in this filed and propose unexplored areas within the field that hold the potential to benefit from ML techniques.

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 OSTI2024

Tuning Perovskites’ Hydration-Induced Chemical Expansion with Octahedral Tilt Angles

Hydration-induced strains in proton-conducting oxides compromise chemo-mechanical stability when these materials are applied in protonic ceramic electrochemical cells. Here, to develop design principles for zero-strain materials, we systematically studied the hydration coefficients of chemical expansion (CCEs) in perovskite (Sr, Ba)(Ce, Zr, Y)O 3–x solid solutions with in situ dilatometry and thermogravimetric analysis in the range of 430–630 °C. By including and decoupling a wide range of tolerance factors and lattice parameters, we were able to identify a minimum in hydration CCEs (0–0.02) at intermediate tolerance factor values (t ≈ 0.95). Conversely, despite expectations of lower CCEs in larger unit cells, no general trend in CCE versus lattice parameter was found, and opposite trends could be seen for Sr(Ce, Zr, Y)O 3–x versus Ba(Ce, Zr, Y)O 3–x separately. In situ neutron diffraction (ND) enabled atomistic insight. Upon decreasing t, chemical strain anisotropy increased, but this trend did not match the U-shaped dependence of macroscopic CCEs on t. Instead, perovskites with intermediate t, hosting intermediate octahedral tilt angles in the nominally dry state, underwent the largest change in the B–O–B angles during hydration. Accommodating hydration through decreasing B–O–B angles is beneficial because it does not result in large lattice parameter changes. We propose an intermediate tolerance factor as a simple structural descriptor to enable near-zero hydration strains in proton-conducting perovskites.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluating Material Design Principles for Calcium-Ion Mobility in Intercalation Cathodes

Multivalent-ion batteries offer an alternative to Li-based technologies, with the potential for greater sustainability, improved safety, and higher energy density, primarily due to their rechargeable system featuring a passivating metal anode. Although a system based on the Ca 2+ /Ca couple is particularly attractive given the low electrochemical plating potential of Ca 2+ , the remaining challenge for a viable rechargeable Ca battery is to identify Ca cathodes with fast ion transport. In this work, a high-throughput computational pipeline is adapted to (1) discover novel Ca cathodes in a largely unexplored space of empty intercalation hosts and (2) develop material design rules for Ca-ion mobility. One candidate from the screening, W 2 O 3 (PO 4 ) 2 , is confirmed to have a low Nudged Elastic Band (NEB) barrier of 168 meV within a one-dimensional (1D) ion percolation topology. This candidate is subsequently synthesized and electrochemically tested, achieving reversible Ca cycling with a capacity of 25 mA h/g. To further accelerate the screening for promising Ca intercalation electrodes, machine learning (ML) Random Forest (RF) and Extreme Gradient Boosting (XGB) classification models are created with local environment descriptors based on a large, structurally and chemically diverse dataset of minimum energy pathways, spanning over 5,000 density functional theory (DFT) site energy calculations. Accuracies of 92% are achieved, material design metrics are quantified, ML force-fields are leveraged in an accelerated iteration of the screening, and a total of 27 novel Ca cathode materials are highlighted for further investigation.

25 ENERGY STORAGE↗
DOE OSTI2025

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications

Here, the migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials.

14 SOLAR ENERGY↗
DOE OSTI2026

Bifunctional Electrocatalysts with High-Entropy Alloys: Bridging Hydrogen Evolution and Oxygen Reduction

High-entropy alloys (HEAs) have emerged as a promising class of bifunctional electrocatalysts capable of simultaneously driving the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) with high activity and durability. Their near-equiatomic multicomponent compositions give rise to unique physicochemical characteristics, including lattice distortion, sluggish diffusion, high-entropy stabilization, and pronounced electronic heterogeneity, that collectively generate diverse and synergistic active sites inaccessible in conventional alloys. This review summarizes recent progress in HEA-based bifunctional electrocatalysis, with a focus on the fundamental mechanisms governing HER and ORR activity, stability, and selectivity. We discuss advances in synthesis strategies, ranging from confined growth and step-alloying to scalable continuous-flow methods, that enable precise control over composition, size, and surface structure. Complementary computational and data-driven approaches, including density functional theory, machine-learning-assisted screening, and descriptor development, are highlighted as essential tools for navigating the vast HEA design space and establishing structure−property relationships. Particular attention is paid to adsorption-energy distributions, multisite cooperativity, and environmental effects under realistic electrochemical conditions. Finally, we outline current challenges and future opportunities for integrating mechanistic understanding with AI-guided, closed-loop design frameworks to accelerate the discovery of next-generation HEA bifunctional electrocatalysts for sustainable energy conversion.

Alloys↗
DOE OSTI2026

Data-Driven Discovery and Experimental Validation of Solvent Polarity Effects on Conjugated Polymer Solution-to-Film Assembly Pathways

Understanding how solvent properties influence the solution-to-film assembly of conjugated polymers remains a critical challenge due to the complex and intertwined nature of polymer–solvent interactions. In this study, we integrate a data-driven framework with experimental validation to identify key parameters influencing the assembly and performance of poly[2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno[3,2-b]thiophene)] (DPP-DTT) in organic field-effect transistors (OFETs). A machine learning (ML) approach identified the normalized Reichardt polarity parameter (E T N ) as a significant descriptor correlated with DPP-DTT hole mobility (μ). Systematic DPP-DTT devices fabricated using solvents across a wide E T N range revealed that higher E T N solvents yield enhanced μ. To elucidate the structural origins of high μ, we conducted comprehensive analyses using UV–vis–NIR spectroscopy and grazing incidence wide angle X-ray scattering (GIWAXS) measurements. The results revealed that films processed from high E T N solvents exhibit reduced paracrystallinity. By analyzing the solution-state behavior using optical microscopy and solution WAXS, we revealed polymer solubility differences in the various solvents and associated distinct polymer assembly pathways, elucidating why the high E T N solvent produces long-range ordered films. Notably, the high E T N solvent shows a pronounced preference for liquid-crystal (LC)-mediated assembly, providing a mechanistic explanation for the enhanced structural order. Therefore, these results demonstrate that solvent polarity, as evaluated by E T N , serves as an important parameter that plays a significant role in the DPP-DTT assembly pathway and resultant solid-state morphology. This work provides a strategy for integrating data science with experiments to identify critical parameters associated with complex polymer systems and helps guide rational process design for high-performance organic electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural Tolerance Factor Approach to Defect-Resistant I 2 -II-IV-X 4 Semiconductor Design

Recent work on quaternary semiconductors Cu 2 BaSn(S,Se) 4 and Ag 2 BaSnSe 4 for photovoltaic and thermoelectric applications, respectively, has shown the promise of exploring the broader family of defect-resistant I 2 -II-IV-X 4 materials (where I, II, and IV refer to the formal oxidation state of the metal cations and X is a chalcogen anion) with tetrahedrally coordinated I/IV cations and larger II cations (i.e., Sr, Ba, Pb, and Eu) for optoelectronic and energy-related applications. Chemical dissimilarity among the II and I/IV atoms represents an important design motivation because it presents a barrier to antisite formation, which otherwise may act as electronically harmful defects. We herein show how all 31 experimentally reported I 2 -II-IV-X 4 examples (with large II cations and tetrahedrally coordinated smaller I/IV cations), which form within five crystal structure types, are structurally linked. Based on these structural similarities, we derive a set of tolerance factors that serve as descriptors for phase stability within this family. Despite common usage in the well-studied perovskite system, Shannon ionic radii are found to be insufficient for predicting metal–chalcogen bond lengths, pointing to the need for experimentally derived correction factors as part of an empirically driven learning approach to structure prediction. Here, we use the tolerance factors as a predictive tool and demonstrate that four new I 2 -II-IV-X 4 compounds, Ag 2 BaSiS 4 , Ag 2 PbSiS 4 , Cu 2 PbGeS 4 , and Cu 2 SrSiS 4 , can be synthesized in correctly predicted phases. One of these compounds, Ag 2 PbSiS 4 , shows potentially promising optoelectronic properties for photovoltaic applications.

36 MATERIALS SCIENCE↗