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DOE OSTI2025

VUV Photoionization Dynamics and Reactivity of Heterogeneous Water Clusters

This feature focuses on bridging isolated water and bulk water studies. It assembles experiments and theory on water clusters, mainly probed by vacuum ultraviolet (VUV) radiation, and summarizes what the effects are of intermolecular interactions on both the spectroscopy and the VUV-induced processes in water. In particular, it highlights studies of heterogeneous water clusters─those incorporating other molecular species such as naphthalene, methane, formic acid, and glycerol─which serve as model systems to investigate the fundamental roles of water in hydrogen bonding networks, proton transfer, and astrochemical processes. These mixed clusters provide a platform to revisit the stability of protonated water clusters and compile observations of ionization-induced structural rearrangements and fragmentation, especially in systems involving hydroxyl-rich cosolvents. This feature then explores energy transfer mechanisms in molecular clusters, following VUV photoexcitation. It concludes with potential future directions. First, we investigate excited-state dynamics in molecular clusters through direct probing, complementing the synchrotron studies discussed here. Second, exploring gas-phase molecule evaporation from confined spaces and interfaces using advanced spectroscopic techniques sheds light on these ubiquitous, yet currently debated, molecular processes.

Cluster chemistry↗
DOE OSTI2020

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable (i.e., modular) Python module, chespa, built to easily assess different chemical space definitions through cluster-ing of compounds in these spaces and visualize trends of these clusters. To demonstrate this, chespa currently streamlines prediction of vari-ous molecule descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test 6 different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, i.e., the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT), where blinded samples had been analyzed previously, providing 1,398 data points. Improved understanding of observability would offer many advantages in small molecule identifica-tion, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property non-trivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

Nunez, Jamie↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-Throughput Discovery Illuminates Design Principles and Limits for Long-Lived Charged Species in Organic Electrolytes

The chemical stability of charged molecules in all-organic redox flow batteries (RFBs) is required for the prolonged operation of these devices. Molecular engineering and electrolyte optimization are used to mitigate parasitic reactions and extend the lifetimes of the charge carriers. However, how much can structural variation extend the lifetime? To probe this query, we designed a high-throughput kinetic study of the radical cation of N-methylphenothiazinium, guided by statistical sampling and learning algorithms. Using Argonne’s autonomous discovery facility, we conducted over 6,000 kinetic experiments with robotic sample preparation, parallel kinetic measurements, and machine learning inputs, testing 188 solvent molecules selected from a space of over 540 candidates from 11 chemical classes. Algorithmic selections guided us to stable solvent candidates, which were further tested in high concentration with and without supporting electrolyte. Our findings reveal the inherent difficulty of exceeding the current state of the art through solvent variation. The desired stability is statistically rare and poorly predictable. Among the many tested, only three solvents significantly outperformed our baseline, acetonitrile─and none by more than a factor of 3─suggesting a general challenge in achieving the necessary techno-economic targets. Furthermore, we suggest that self-discharge through solvent homolysis is the cause of the observed limitations. Several structural motifs contribute to >1,000 h half-life stability including molecular simplicity, symmetry, oxidation complement, and strategic fluorination. Importantly, this workflow establishes effective assays for diagnosing and predicting oxidative stress for highly stable liquid electrolytes in all batteries.

Batteries↗
DOE OSTI2021

Intramolecular Electric Field Construction in Metal Phthalocyanine as Dopant‐Free Hole Transporting Material for Stable Perovskite Solar Cells with >21 % Efficiency

Abstract Low conductivity and hole mobility in the pristine metal phthalocyanines greatly limit their application in perovskite solar cells (PSCs) as the hole‐transporting materials (HTMs). Here, we prepare a Ni phthalocyanine (NiPc) decorated by four methoxyethoxy units as HTMs. In NiPc, the two oxygen atoms in peripheral substituent have a modified effect on the dipole direction, while the central Ni atom contributes more electron to phthalocyanine ring, thus efficiently increasing the intramolecular dipole. Calculation analyses reveal the extracted holes within NiPc are mainly concentrated on the phthalocyanine core induced by the intramolecular electric field, and further to be transferred by π‐π stacking space channel between NiPc molecules. Finally, the best efficiency of PSCs with NiPc as dopant‐free HTMs realizes a record value of 21.23 % (certified 21.03 %). The PSCs also exhibit the good moisture, heating and light stabilities. This work provides a novel way to improve the performance of PSCs with free‐doped metal phthalocyanines as HTMs.

Yu, Zefeng↗
DOE OSTI2020

Role of Undercoordinated Sites for the Catalysis in Confined Spaces Formed by Two-Dimensional Material Overlayers

Adding a two-dimensional (2D) overlayer on a metal surface is a promising route for activating reactants confined in the interfacial space. However, an atomistic understanding of the role played by undercoordinated sites of the 2D overlayer in the activation of molecules in this nanoscaled confined space is yet to be developed. In this paper, we study CO dissociation as a prototypical reaction to investigate CO activation in the confined space enclosed by Rh(111) and a monolayer of hexagonal boron nitride (h-BN). The effect of the space size (i.e., the distance between h-BN and the metal surface), the type of undercoordinated sites, and the size of the defect are explicitly studied by density functional theory with dispersion correction. The following temperature-programmed X-ray photoelectron spectroscopy measurement suggests that a small portion of the CO dissociated during the desorption, leaving the residual atomic oxygen incorporated into the h-BN lattice, which validates the theoretical prediction. Finally, the combination of theory and experiment calls for further attention to be paid to the role of undercoordinated sites in the 2D overlayers in confined systems forming potential new catalytic environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.

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

Elliptic flow coalescence to identify the $f_0$(980) content

Here we use a simple coalescence model to generate $f_0$(980) particles for three configurations: a $s\bar{s}$ meson, a $u\bar{u}s\bar{s}$ tetraquark, and a $K^+K^–$ molecule. The phase-space information of the coalescing constituents is taken from a multiphase transport (AMPT) simulation of heavy-ion collisions. It is shown that the number of constituent quarks scaling of the elliptic flow anisotropy can be used to discern $s\bar{s}$ from $u\bar{u}s\bar{s}$ and $K^+K^–$ configurations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Nucleon localization function in rotating nuclei

An electron localization function was originally introduced to visualize in positional space bond structures in molecules. It became a useful tool to describe electron configurations in atoms, molecules, and solids. In nuclear physics, a nucleon localization function (NLF) has been used to characterize cluster structures in light nuclei, formation of fragments in fission, and pasta phases appearing in the inner crust of neutron stars. In the case of a deformed rotating nucleus, several NLFs can be introduced, depending on the definition of the spin-quantization axis, direction of the total angular momentum, and self-consistent symmetries of the system. Contributions to the NLF from the current density, spin-current tensor density, and density gradient terms are negligible in the nuclear interior. The oscillating pattern of the simplified NLF can be explained in terms of a constructive interference between kinetic-energy and particle densities. The characteristic nodal pattern seen in the NLF in the direction of major axis of a rotating nucleus comes from single-particle orbits carrying large aligned angular momentum. The variation of the NLF along the minor axis of the nucleus can be traced back to deformation-aligned orbits.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Structural characterization of a new samarium–sodium heterometallic coordination polymer

Lanthanide-containing materials are of interest in the field of crystal engineering because of their unique properties and distinct structure types. In this context, a new samarium–sodium heterometallic coordination polymer, poly[tetrakis(μ 2 -2-formyl-6-methoxyphenolato)samarium(III)sodium(I)], {[SmNa(C 8 H 7 O 3 ) 4 ]·solvent} n ( Sm-1 ), was synthesized and crystallized via slow evaporation from a mixture of ethanol and acetonitrile. The compound features alternating Sm III and Na I ions, which are linked by ortho -vanillin ( o -vanillin) ligands to form a mono-periodic chain-like coordination polymer. The chains propagate along the [001] direction. Residual electron density of disordered solvent molecules in the void space could not be reasonably modeled, thus the SQUEEZE function was applied. The structural, vibrational, and optical properties are reported.

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

Structures of glasses created by multiple kinetic arrests

X-ray scattering has been used to characterize glassy itraconazole (ITZ) prepared by cooling at different rates. Faster cooling produces ITZ glasses with lower (or zero) smectic order with more sinusoidal density modulation, larger molecular spacing, and shorter lateral correlation between the rod-like molecules. We find that each glass is characterized by not one, but two fictive temperatures T f (the temperature at which a chosen order parameter is frozen in the equilibrium liquid). The higher T f is associated with the regularity of smectic layers and lateral packing, while the lower T f with the molecular spacings between and within smectic layers. This indicates that different structural features are frozen on different timescales. The two timescales for ITZ correspond to its two relaxation modes observed by dielectric spectroscopy: the slower δ mode (end-over-end rotation) is associated with the freezing of the regularity of molecular packing and the faster α mode (rotation about the long axis) with the freezing of the spacing between molecules. Finally, our finding suggests a way to selectively control the structural features of glasses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Non-resonant Raman optical activity from phase-space electronic structure theory

In order to model experimental non-resonant Raman optical activity, chemists must compute a host of second-order response tensors (e.g., the electric-dipole–magnetic-dipole polarizability) and their nuclear derivatives along a set of vibrational modes. While these response functions are almost always computed within a Born–Oppenheimer (BO) framework, here we provide a natural interpretation of the electric-dipole–magnetic-dipole polarizability within phase space electronic structure theory, a beyond-BO model whereby the electronic structure depends on nuclear momentum (P) in addition to nuclear position (R). By coupling to nuclear momentum, phase space electronic structure theory is able to capture the asymmetric response of the electronic properties to an external field, in so far as for a vibrating (non-stationary) molecule, $\frac{∂μ}{∂B}$≠$\frac{∂m}{∂F}$, where μ and m are the electrical linear and magnetic dipoles, and F and B are electric and magnetic fields. As an example, for a prototypical methyloxirane molecule, we show that phase space electronic structure theory is able to deliver a reasonably good match with experimental results in a manner that is formally invariant to gauge origin G 0 —provided that one uses a complete basis or, alternatively, gauge invariant atomic orbitals.

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

Strategies for Controlling the Spatial Orientation of Single Molecules Tethered on DNA Origami Templates Physisorbed on Glass Substrates: Intercalation and Stretching

Nanoarchitectural control of matter is crucial for next-generation technologies. DNA origami templates are harnessed to accurately position single molecules; however, direct single molecule evidence is lacking regarding how well DNA origami can control the orientation of such molecules in three-dimensional space, as well as the factors affecting control. Here, we present two strategies for controlling the polar (θ) and in-plane azimuthal (Φ) angular orientations of cyanine Cy5 single molecules tethered on rationally-designed DNA origami templates that are physically adsorbed (physisorbed) on glass substrates. By using dipolar imaging to evaluate Cy5's orientation and super-resolution microscopy, the absolute spatial orientation of Cy5 is calculated relative to the DNA template. The sequence-dependent partial intercalation of Cy5 is discovered and supported theoretically using density functional theory and molecular dynamics simulations, and it is harnessed as our first strategy to achieve θ control for a full revolution with dispersion as small as ±4.5°. In our second strategy, Φ control is achieved by mechanically stretching the Cy5 from its two tethers, being the dispersion ±10.3° for full stretching. These results can in principle be applied to any single molecule, expanding in this way the capabilities of DNA as a functional templating material for single-molecule orientation control. The experimental and modeling insights provided herein will help engineer similar self-assembling molecular systems based on polymers, such as RNA and proteins.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ~40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ~3.7 billion candidate molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

CSGL: chemical synthesis graph learning for molecule representation

Abstract Motivation Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. Results Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. Availability and implementation https://github.com/li-2023/CSGL.

Biochemistry & Molecular Biology↗
DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗