DeGAT: A Dual-Expert Graph Attention Network for Partial Charge Prediction in Metal–Organic Frameworks

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

Predicting the electronic properties of transition metal complexes (TMCs) from 2D molecular graphs remains challenging; organic-trained property models lack TMC transferability, universal interatomic potentials require 3D coordinates rather than SMILES, and tools providing holistic electronic property prediction with atom-level explainability and calibrated uncertainty remain limited. We present tmGNN-XAI, a multitask relational graph convolutional network that predicts seven quantum-chemical properties of TMCs directly from SMILES strings and produces perturbation-based atom-level attributions for each prediction. The model encodes dative coordination bonds as a dedicated edge type distinct from covalent bonds and is trained on 100,703 complexes from the tmQM data set spanning 30 transition metals. Test-set performance is competitive with a Chemprop D-MPNN baseline, achieving R2 = 0.979 for metal partial charge and R2 = 0.964 and 0.949 for HOMO and LUMO energies. Across all 100,703 complexes, donor atoms (N, O, S, P) appear among the top-five most important atoms in more than 99.8% of complexes for every property, a large-scale data-driven result consistent with ligand field theory. A trust framework combining ensemble agreement with attribution direction separates predictions into four reliability scenarios; confident predictions achieve 1.6 to 2.5 times lower mean absolute error than uncertain ones for five of seven properties. The framework generalizes to cross-level DFT validation, phototherapy candidate screening (area under the ROC curve (AUC) = 0.735), and indirect redox prediction via Koopmans' theorem. An interactive web application makes property predictions, atom-level attributions, and trust labels accessible without programming or DFT expertise. tmGNN-XAI is designed as an explainable, first-tier screening tool for TMC electronic property estimation.

Coupled-cluster molecular properties across the main group that extrapolate beyond training size

Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine application, whereas density-functional theory is affordable yet systematically biased. We resolve this trade-off with a single equivariant network, MEHnet-MG, that predicts an effective one-electron Hamiltonian from one inexpensive B3LYP/def2-SVP calculation and derives a broad suite of properties from it (energy, optical gap, dipole, quadrupole, polarizability, Mulliken atomic charges, and Mayer bond orders) at coupled-cluster accuracy across nine main-group elements, including the under-served phosphorus, sulfur, and chlorine chemistries. The model is trained on a new in-house dataset of multi-property labels computed at the CCSD(T) level for all nine elements. On a held-out test set, it reduces the error of every property by a factor of 3.8 to 230 relative to semi-local, hybrid, and double-hybrid DFT (referenced to composite CCSD(T)/cc-pVTZ; Methods), while adding only ~25 ms wall time per molecule, delivering coupled-cluster-quality predictions at the cost of a single DFT calculation. Critically, deriving every property from a predicted Hamiltonian rather than pooling per-atom features builds the correct size-scaling into the model architecture: on pi-conjugated oligothiophenes it matches finite-field CCSD polarizability and the EOM-CCSD optical gap to ~2% at the largest sizes where those references remain affordable (44 and 37 atoms, where a single CCSD field point already costs ~500x the model's entire inference) and extrapolates the corrected trends to 58-atom chains, a regime where pooling-based architectures fail by construction. Accurate extrapolation is therefore set by the model's inductive bias rather than by the training data.

Meta-learning GNN with MD-informed attention for cross-species prediction of phosphoinositide-dependent kinase-1 (PdK1) inhibitors in termite control

Graph Neural Network Force Fields (GPTFF-mol) for Organic Molecules from Optimization Trajectories (OpenGEM26)

Density functional theory (DFT) serves as a reliable tool for atomistic molecular simulations, while machine learning potentials have become powerful complements to balance accuracy and efficiency. In this work, we release OpenGEM26 (Open Generated Ensemble of Molecules, 2026), a large-scale dataset comprising 200,000 unique molecules and 4.4 million conformations composed of H, C, N, O, S and Cl with up to ten heavy atoms. All calculations are carried out at the {\omega}B97X-D/Def2-SVP and Def2-TZVP levels with dispersion corrections, and complete structural optimization trajectories and abundant non-equilibrium structures are recorded. Statistical analyses confirm that this dataset covers a broader conformational space than QM9 in terms of energy, bond lengths and bond angles. A graph neural network-based potential GPTFF-mol is trained using the new dataset, achieving an energy mean absolute error of 16 meV/molecule, which is equivalent to 0.82meV/atom, and superior force prediction performance compared with ANI-2x. Validated by butane rotation and keto-enol tautomerization tests, the model accurately describes molecular dynamical behaviors and reaction barriers at distorted geometries. This work provides a high-quality resource and robust ML potential for efficient simulations of sulfur- and chlorine-containing organic molecules.

Δ -Machine Learning for the Prediction of Metal Complex Properties.

The discovery and design of novel transition metal complexes for specific applications heavily rely on computational high-throughput screenings to identify promising candidates for experimental validation. However, traditional computational approaches, such as density functional theory, are often too computationally demanding to be applied on a large scale. Machine learning methods offer a promising alternative due to their excellent computational efficiency, but their accuracy and high data requirements remain major challenges for their effective implementation. To address these issues, we herein present an adaptation of the Δ-ML strategy for quantum property prediction of transition metal complexes. We combine GFN2-xTB geometry optimizations and density functional theory single-point calculations in order to obtain low-fidelity approximations and generate featurized graph representations that serve as input to a graph neural network architecture. The high-fidelity targets originate from the tmQMg dataset and include the electronic and dispersion energies, HOMO-LUMO gap and dipole moment at the PBE0-D3BJ/def2-TZVP level as well as the polarizability at the PBE-D3BJ/def2-SVP level. Compared to a conventional benchmark approach, the proposed method consistently achieves higher accuracy in the prediction of high-fidelity targets, while demonstrating improved data efficiency and out-of-domain transferability. We furthermore show, how the use of cheaper low-fidelity methods leads to significant reductions in computational cost at minor losses in predictive performance. Overall, these results highlight the potential of Δ-ML for materials discovery in transition metal chemistry, which requires high predictive accuracy despite often times limited availability of training data.

Data-Driven Exploration of the Polyethylene Catalyst Chemical Space via Machine Learning.

The discovery of highly active polyethylene (PE) catalysts demands a systematic understanding of structure-condition-activity relationships in a vast chemical space. In this Letter, we present a data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation. From a curated data set of 507 catalysts (bis(phenoxyimine) and bis(imino)pyridine ligands, seven metals), a gradient boosting regression (GBR) model achieves a test R2 of 0.91, outperforming convolutional and graph neural networks. SHAP analysis identifies topological (Chi2v), electronic (EState_VSA), and hydrophobic (SlogP_VSA) descriptors as governing activity and reveals a classical volcano-type temperature dependence, fundamentally governed by the Sabatier principle. A virtual library of 665 685 structures, constructed via combinatorial fragment assembly, extends the known chemical space substantially. High-throughput screening, coupled with SCscore filtering, yields 1090 synthetically accessible candidates with predicted activities exceeding 2 × 107 g mol-1 h-1. Substructure analysis uncovers metal-dependent design rules, in which early transition metals favor electron-deficient aromatics while late metals profit from moderately sized alkyls. This work establishes a practical route from experimental data to actionable catalyst designs.