Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence.

Ligands play a critical role in the optical properties and chemical stability of colloidal nanocrystals (NCs), but identifying ligands that can enhance NC properties is daunting, given the high dimensionality of chemical space. Here, we use machine learning (ML) and robotic screening to accelerate the discovery of ligands that enhance the photoluminescence quantum yield (PLQY) of CsPbBr3 perovskite NCs. We developed a ML model designed to predict the relative PL enhancement of perovskite NCs when coordinated with a ligand selected from a pool of 29,904 candidate molecules. Ligand candidates were selected using an active learning (AL) approach that accounted for uncertainty quantified by twin regressors. After eight experimental iterations of batch AL (corresponding to 21 initial and 72 model-recommended ligands), the uncertainty of the model decreased, demonstrating an increased confidence in the model predictions. Feature importance and counterfactual analyses of model predictions illustrate the potential use of ligand field strength in designing PL-enhancing ligands. Our versatile AL framework can be readily adapted to screen the effect of ligands on a wide range of colloidal nanomaterials.

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science.

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.

Active meta-learning for predicting and selecting perovskite crystallization experiments.

Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.

Predicting inorganic dimensionality in templated metal oxides.

Amine-templated metal oxides are a class of hybrid organic-inorganic compounds with great structural diversity; by varying the compositions, 0D, 1D, 2D, and 3D inorganic dimensionalities can be achieved. In this work, we created a dataset of 3725 amine-templated metal oxides (including some metalloid oxides), their composition, amine identity, and dimensionality, extracted from the Cambridge Structure Database (CSD), which spans 71 elements, 25 main group building units, and 349 amines. We characterize the diversity of this dataset over reactants and in time. Artificial neural network models trained on this dataset can predict the most and least probable outcome dimensionalities with 71% and 95% accuracies, respectively, using only information about reactant identities, without stoichiometric information. Surprisingly, the amine identity plays only a minor role in most cases, as omitting this information only reduces the accuracy by <2%. The generality of this model is demonstrated on a time held-out test set of 36 amine-templated lanthanide oxalates, vanadium tellurites, vanadium selenites, vanadates, molybdates, and molybdenum sulfates, whose syntheses and structural characterizations are reported here for the first time, and which contain two new element combinations and four amines that are not present in the CSD.

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion.

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

Machine-Learning-Assisted Synthesis of Polar Racemates.

Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)2(H2O)]2[MF6]2·3H2O (M = Ti, Zr, Hf; bpy = 2,2'-bipyridine) family (space group: Pna21). In each (CuO, MO2)/bpy/HF(aq) (M = Ti, Zr, Hf) system, the polar noncentrosymmetric racemate (M-NCS) forms in competition with a centrosymmetric one-dimensional chain compound (M-CS) based on alternating Cu(bpy)(H2O)22+ and MF62- basic building units (space groups: Ti-CS (Pnma), Zr-CS (P1̅), Hf-CS (P2/n)). Machine learning models were trained on reaction parameters to gain unbiased insight into the underlying statistical trends in each composition space. A human-interpretable decision tree shows that phase selection is driven primarily by the bpy:CuO molar ratio for reactions containing Zr or Hf, and predicts that formation of the Ti-NCS compound requires that the amount of HF present be decreased to raise the pH, which we verified experimentally. Predictive leave-one-metal-out (LOO) models further confirm that behavior in the Ti system is distinct from that of the Zr and Hf systems. The chemical origin of this distinction was probed via fluorine K-edge X-ray absorption spectroscopy. Pre-edge features in the F1s X-ray absorption spectra reveal the strong ligand-to-metal π bonding between Ti(3d - t2g) and F(2p) states that distinguishes the TiF62- anion from the ZrF62- and HfF62- anions.

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis.

Most chemical experiments are planned by human scientists and therefore are subject to a variety of human cognitive biases1, heuristics2 and social influences3. These anthropogenic chemical reaction data are widely used to train machine-learning models4 that are used to predict organic5 and inorganic6,7 syntheses. However, it is known that societal biases are encoded in datasets and are perpetuated in machine-learning models8. Here we identify as-yet-unacknowledged anthropogenic biases in both the reagent choices and reaction conditions of chemical reaction datasets using a combination of data mining and experiments. We find that the amine choices in the reported crystal structures of hydrothermal synthesis of amine-templated metal oxides9 follow a power-law distribution in which 17% of amine reactants occur in 79% of reported compounds, consistent with distributions in social influence models10-12. An analysis of unpublished historical laboratory notebook records shows similarly biased distributions of reaction condition choices. By performing 548 randomly generated experiments, we demonstrate that the popularity of reactants or the choices of reaction conditions are uncorrelated to the success of the reaction. We show that randomly generated experiments better illustrate the range of parameter choices that are compatible with crystal formation. Machine-learning models that we train on a smaller randomized reaction dataset outperform models trained on larger human-selected reaction datasets, demonstrating the importance of identifying and addressing anthropogenic biases in scientific data.

Machine-learning-assisted materials discovery using failed experiments.

Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on 'dark' reactions--failed or unsuccessful hydrothermal syntheses--collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.

Role of noncovalent interactions in vanadium tellurite chain connectivities.

Structural differences in [V2Te2O10]n(2n-) chain metrics are directly ascribed to variations in noncovalent interactions in a series of organically templated vanadium tellurites, including [C6H17N3][V2Te2O10]·H2O, [C5H16N2][V2Te2O10], and [C4H14N2][V2Te2O10]. The noncovalent interaction (NCI) method was used to locate, quantify, and visualize intermolecular interactions in [C4H14N2][V2Te2O10] and [C5H16N2][V2Te2O10]. Variations in the van der Waals attractions between [1,4-diaminobutaneH2](2+) and [1,5-diaminopentaneH2](2+) result in divergent packing motifs for these cations, which causes a reorganization of N-H···O hydrogen bonding and variances in the [V2Te2O10]n(2n-) chain metrics. The application of the NCI method to this type of solid-state structure provides a direct method to elucidate the structural effects of weak noncovalent interactions.

Formation principles for vanadium selenites: the role of pH on product composition.

A series of organically templated vanadium selenites has been prepared under mild hydrothermal conditions. Single crystals of [C5H14N2][(VO)3(SeO3)2(HSeO3)4], [C5H14N2][VO(SeO3)2], [(R)-C5H14N2][(VO)3(SeO3)2(HSeO3)4], and [(S)-C5H14N2][(VO)3(SeO3)2(HSeO3)4] were grown from VOSO4, SeO2, and 2-methylpiperazine. Controlling the initial pH of the reaction mixture allows for one to select between the compounds found in the VOSO4/SeO2/2-methylpiperazine system, as the solution pH directly affects the relative ratio of the HSeO3(-) and SeO3(2-) concentrations. Moreover, partial resolution of racemic 2-methylpiperazine is observed in [C5H14N2][(VO)3(SeO3)2(HSeO3)4], which is understood through the use of a one-dimensional Ising model. The use of enantiomerically pure 2-methylpiperazine results in fully ordered and fully resolved structures.

catena-Poly[2,2',2''-nitrilo-tris-(ethan-aminium) [tri-µ-oxido-tris-[dioxido-vanadate(V)]] monohydrate].

The title compound, {(C6H21N4)[V3O9]·H2O} n , crystallizes as a salt with [trenH3](3+) cations [tren is tris-(2-amino-eth-yl)amine], and one-dimensional anionic {[V(V)O3](-)} n (metavanadate) chains along the c-axis direction. Three crystallographically distinct V(V) sites and one occluded water mol-ecule are present for every [trenH3](3+) cation in the unit cell. The {[V(V)O3](-)} n chains are composed of vertex-sharing [VO4] tetra-hedra and have a repeat unit of six tetra-hedra. Each tetra-hedron in the chain contains two terminal and two µ(2)-bridging oxide ligands. The [trenH3](3+) cations, {[V(V)O3](-)} n anions and occluded water mol-ecules participate in an extensive three-dimensonal hydrogen-bonding network. The three terminal ammonium sites of the [trenH3](3+) cations each form strong N-H⋯O hydrogen bonds to terminal oxide ligands on the {[V(V)O3](-)} n chain. Each occluded water mol-ecule also donates two O-H⋯O hydrogen bonds to the terminal oxide ligands.

Role of hydrogen-bonding in the formation of polar achiral and nonpolar chiral vanadium selenite frameworks.

A series of organically templated vanadium selenites have been prepared under mild hydrothermal conditions. Single crystals were grown from mixtures of VOSO(4), SeO(2), and either 1,4-dimethylpiperazine, 2,5-dimethylpiperazine, or 2-methylpiperazine in H(2)O. Each compound contains one-dimensional [VO(SeO(3))(HSeO(3))](n)(n-) secondary building units, which connect to form three-dimensional frameworks in the presence of 2,5-dimethylpiperazine or 2-methylpiperazine. Differences in composition and both intra-secondary building unit and organic-inorganic hydrogen-bonding between compounds dictate the dimensionality of the resulting inorganic structures. [1,4-dimethylpiperazineH(2)][VO(SeO(3))(HSeO(3))](2) contains one-dimensional [VO(SeO(3))(HSeO(3))](n)(n-) chains, while [2,5-dimethylpiperazineH(2)][VO(SeO(3))(HSeO(3))](2)·2H(2)O contains a three-dimensional [VO(SeO(3))(HSeO(3))](n)(n-) framework. The use of racemic 2-methylpiperazine also results in a compound containing a three-dimensional [VO(SeO(3))(HSeO(3))](n)(n-) framework, crystallizing in the noncentrosymmetric polar, achiral space group Pca2(1) (no. 29), while analogous reactions containing either (R)-2-methylpiperazine or (S)-2-methylpiperazine result in noncentrosymmetric, nonpolar chiral frameworks that crystallize in P2(1)2(1)2 (no. 18). The formation of these noncentrosymmetric framework materials is dictated by the structure, symmetry, and hydrogen-bonding properties of the [2-methylpiperazineH(2)](2+) cations.

The role of stereoactive lone pairs in templated vanadium tellurite charge density matching.

The role of charge density matching was investigated in the formation of templated vanadium tellurites under mild hydrothermal conditions. Reactions were conducted using a fixed NaVTeO(5):amine ratio in an ethanol/water solution to isolate the effects of amine structure. The use of 1,4-diaminobutane, 1,3-diaminopropane, and piperazine resulted in three distinct vanadium tellurite connectivities, [V(2)Te(2)O(10)](n)(2n-) chains, [V(2)TeO(8)](n)(2n-) layers, and [V(2)Te(2)O(10)](n)(2n-) layers, respectively. Charge density matching with the protonated amines is the primary influence over the structure of each vanadium tellurite anion, as quantified by molecular surface area and geometric decomposition methods. Electron localization functions were calculated using the Stuttgart tight-binding linear muffin-tin orbital, atomic sphere approximation code, to visualize the location and relative size, shape, and orientation of the stereoactive lone pair in the tellurite groups. [C(4)H(14)N(2)][V(2)Te(2)O(10)]: a = 5.649(5) A, b = 6.348(5) A, c = 9.661(5) A, alpha = 84.860(5) degrees , beta = 85.380(5) degrees , gamma = 81.285(5) degrees , triclinic, P1 (No. 2), Z = 1.

Noncentrosymmetry in new templated gallium fluorophosphates.

Two new noncentrosymmetric polar gallium fluorophosphates have been synthesized under mild hydrothermal conditions through the use of enantiomorphically pure sources of either R-2-methylpiperazine or S-2-methylpiperazine. A centrosymmetric analogue was also prepared using a racemic source of the amine. Novel [Ga(3)F(PO(4))(4)](n)(4n-) layers, constructed from [Ga(3)O(3)F(PO(4))(4)] building units, are observed in all three compounds. The use of racemic 2-methylpiperazine results in crystallographic disorder of the amines and creation of inversion centers, while using a single enantiomer destroys the inversion symmetry and orders the amines. Second harmonic generation measurements were performed on [(R)-C(5)H(14)N(2)](2)[Ga(3)F(PO(4))(4)] x 5.5 H(2)O and [(S)-C(5)H(14)N(2)](2)[Ga(3)F(PO(4))(4)] x 4.75 H(2)O, both of which display type 1 phase-matching capabilities and exhibit activities of approximately 50 x alpha-SiO(2). The structures of these compounds were determined using single crystal X-ray diffraction, infrared spectroscopy, and thermal analyses. [C(5)H(14)N(2)](2)[Ga(3)F(PO(4))(4)] x 5.25 H(2)O, a = 13.0863(5) A, c = 9.9023(4) A, trigonal, P-3 (No. 147), Z = 2; [(R)-C(5)H(14)N(2)](2)[Ga(3)F(PO(4))(4)] x 5.5 H(2)O, a = 13.0887(2) A, c = 29.9439(4) A, trigonal, P3(1) (No. 144), Z = 6; [(S)-C(5)H(14)N(2)](2)[Ga(3)F(PO(4))(4)] x 4.75 H(2)O, a = 13.0871(2) A, c = 29.8350(6) A, trigonal, P3(2) (No. 145), Z = 6.

Synthetic approaches for noncentrosymmetric molybdates.

The use of second-order Jahn-Teller active Mo (VI) centers and chiral organic amines is discussed as an approach to crystallographic noncentrosymmetry. Several series of reactions, conducted under mild hydrothermal conditions, were designed to probe important reaction variables. Correlations between reagent and solvent concentrations and the molybdate structure were investigated using composition space analysis, which allows for the isolation of specific reaction variables. The effects of amine structure variation were probed using multiple series of related amines, which consisted of either linear diamines or ethylenediamine derivatives. The addition of fluoride results in the loss of amine-based structural variations. Chiral organic amines were used to demonstrate the viability of using such components to control the three-dimensional symmetry in new materials. The synthesis, structure, and characterization of eight new organically templated polyoxomolybdates and polyoxofluoromolybdates are reported.

[Mo16O53F2]12-: a new polyoxofluoromolybdate anion.

Single crystals of a new beta-octamolybdate salt containing protonated 1,4-diazabicyclo[2.2.2]octane cations were prepared under mild hydrothermal conditions. This compound, [C6H13N2]2[C6H14N2][Mo8O26], was then used as a starting material in the synthesis of [C6H13N2]6[Mo16O53F2].4H2O, which contains previously unreported [Mo16O53F2]12- anions. The structure-directing properties of gamma-[Mo8O26]4-, a likely intermediate in this pH-dependent transformation, are responsible for the site selection of the fluoride incorporation. [Mo16O53F2]12-, the largest reported polyoxofluoromolybdate cluster, expands upon the limited number of such anions in the literature. The structures of both compounds were determined using single-crystal X-ray diffraction.

Directed synthesis of noncentrosymmetric molybdates using composition space analysis.

A systematic investigation of the factors governing the reaction product composition, hydrogen bonding, and symmetry was conducted in the MoO3/3-aminoquinuclidine/H2O system. Composition space analysis was performed through 36 individual reactions under mild hydrothermal conditions using racemic 3-aminoquinuclidine. Single crystals of three new compounds, [C7H16N2][Mo3O10] x H2O, [C7H16N2]2[Mo8O26] x H2O, and [C7H16N2]2[Mo8O26] x 4 H2O, were grown. The relative phase stabilities for these products are dependent upon the reactant mole fractions in the initial reaction gel. This phase stability information was used to direct the synthesis of two new noncentrosymmetric compounds, using either (S)-(-)-3-aminoquinuclidine dihydrochloride or (R)-(+)-3-aminoquinuclidine dihydrochloride. [(R)-C7H16N2]2[Mo8O26] and [(S)-C7H16N2]2[Mo8O26] both crystallize in the noncentrosymmetric space group P2(1) (No. 4), which has the polar crystal class 2 (C2). The second-harmonic generation activities were measured on sieved powders. The structure-directing properties of the molybdate components in each compound were determined using bond valence sums. The structures of all five compounds were determined using single-crystal X-ray diffraction.

The role of amine sulfates in hydrothermal uranium chemistry.

A series of novel uranium sulfates containing organic structure directing cations has been synthesized from amine sulfate precursors under hydrothermal conditions. The amine sulfates act as a soluble source of the protonated amines and sulfate ions at low temperature and provide a reaction pathway in which no amine decomposition is observed. The protonated amines act as both space fillers and hydrogen-bond donors in the three-dimensional structure. The factors governing the formation of the observed hydrogen-bonding networks were probed through the use of bond valence sums, which allow the quantification of residual negative charge and determination of the relative nucleophilicity of each oxide ligand. The hydrogen bonding in these new compounds is dependent upon two factors. First, the oxide ligands with the highest nucleophilicities are preferential acceptors with respect to their less nucleophilic counterparts. Second, geometric constraints that result from the formation of multiple hydrogen bonds from a single ammonium center can dictate the donation to oxides with smaller negative charges. Crystal data for [N4C6H12][SO4]2 x 2H2O, a = 7.2651(2) A, b = 7.3012(2) A, c = 8.3877(3) A, alpha = 90.260(1) degrees, beta = 100.323(1) degrees, gamma = 113.0294(15) degrees, triclinic, P-1 (No. 2), Z = 1; for [N4C6H22][UO2(H2O)(SO4)2]2 x 6H2O, a = 6.7318(1) A, b = 9.2975(1) A, c = 13.1457(3) A, alpha = 72.3395(6) degrees, beta = 89.1401(7) degrees, gamma = 70.0267(12) degrees, triclinic, P-1 (No. 2), Z = 1; for [N4C6H22][UO2(SO4)2)2, a = 9.3771(2) A, b = 12.9523(3) A, c = 18.9065(6) A, orthorhombic, Pbca (No. 61), Z = 4; for [N5C8H28]2[(UO2)5(H2O)5(SO4)10] x H2O, a = 7.76380(5) A, b = 14.16890(5) A, c = 56.46930(5) A, orthorhombic, Pbnm (No. 62), Z = 4.

The effects of hydrofluoric acid addition on the hydrothermal synthesis of templated uranium sulfates.

The effect of employing hydrofluoric acid as a mineraliser in the formation of organically templated uranium sulfate materials has been studied. Variable amounts of HF((aq)) were added to a series of reaction gels in which all other reactant concentrations were invariant, resulting in the formation of three different phases, depending upon the fluoride concentration. Two of these phases are novel; [N(2)C(4)H(14)][UO(2)(H(2)O)(SO(4))(2)] is a new templated uranium sulfate, containing anionic [UO(2)(H(2)O)(SO(4))(2)](2-) chains that hydrogen bond to one-another forming pseudo-layers, and [N(2)C(4)H(14)][UO(2)F(SO(4))](2) is the first example of a templated uranium sulfate fluoride, which consists of uranium fluoride chains linked by sulfate groups to form [UO(2)F(SO(4))](-) layers. The role of F(-) in these reactions is two-fold; it acts as a mineraliser when present in small concentrations, while it is incorporated into the reaction product when present in larger mole fractions. Both of the new materials have been characterised using a range of physical techniques including single crystal X-ray structure analysis.

[C5H14N2][(MoO3)3(SO4)].H2O: sulfated alpha-molybdena chains.

Recent work in the preparation of organically templated metal sulfates under hydrothermal conditions has been extended to include the sulfation of alpha-molybdena through the synthesis of [C(5)H(14)N(2)][(MoO(3))(3)(SO(4))].H(2)O. Single crystals were grown under hydrothermal conditions from molybdenum oxide, water, sulfuric acid, and an enantiomerically pure (R)-2-methylpiperazine source and characterized using both single-crystal X-ray diffraction and infrared spectroscopy. One-dimensional [(MoO(3))(3)(SO(4))](n)(2n-) chains, based on a neutral alpha-molybdena backbone, are connected through an extensive hydrogen-bonding network containing [C(5)H(14)N(2)](2+) cations and occluded water molecules. The direction of the hydrogen bonding is primarily dictated by the nucleophilicity of the respective oxide ligands, as determined using bond valence sums.