The application of machine learning approaches to meaningful problems in chemistry and materials science is still challenged by the limited availability of data. In order to close this gap, we report the tmQMg* data set, which provides excited state properties for 74k mononuclear transition metal complexes extracted from the Cambridge Structural Database. All properties were computed at the TD-DFT ωB97xd/def2SVP level of theory. The strongest electron excitations in the ultraviolet, visible, and near-infrared ranges are included, together with the wavelengths and intensities of the first 30 excited states.

Further, natural transition orbitals were computed for the strongest excitations in the visible range to determine the nature of the associated charge transfers. By computing the TD-DFT spectra in both gas phase and acetone, we quantified solvatochromic effects, which are also provided with the data set, in terms of both wavelength shifts and intensity changes.

The tmQMg* data set will enable the development of discriminative and generative artificial intelligence models with respect to absorption spectra, charge transfer character, and solvatochromism, enabling novel advances in the field of transition metal photochemistry.

In this paper, we present the results of training machine learning (ML) models for accurate prediction of several key photophysical characteristics (absorption maximum wavelength, molar absorption coefficient, emission maximum wavelength, fluorescence quantum yield and lifetime, singlet oxygen generation quantum yield) for BODIPYs. ML models were trained using experimental data comprising more than 35,000 records for the predicted parameters. Particular emphasis was placed on model interpretability and on accounting for the solvent nature effect on the predicted photophysical parameters. To ensure open data access and a user-friendly interface, all developed models were integrated into the created web tool SpecML (http://specml.isc-ras.ru/).

SpecML allows prediction of photophysical parameters for individual BODIPY molecules and the screening of entire series of BODIPYs. We believe that our created SpecML web tool will become an effective resource for accelerating the rational design of BODIPYs with desired photophysical properties and will be useful for a wide range of researchers in the fields of photonics, organic electronics, and molecular design.

Significant attention has been devoted to developing novel solid-state electrolytes (SSEs) with high ionic conductivity for all-solid-state batteries (ASSBs). However, most studies have primarily focused on compositional substitutions, often overlooking the fundamental role of inherent crystal structures on ion transport.

To address this, we introduce a theoretical crystal structure prediction (CSP) approach based on the machine-learning moment tensor potential (MTP). The proposed approach successfully identifies novel SSE structures and reproduces 12 experimental crystal structures. Using a phase-diagram-guided strategy, CSP is applied to four promising SSE candidates, Li2SiS3, Li2GeS3, Li4SiGeS6, and Li4SiSnS6, to assess their polyhedral connectivity, relative stability, and Li-ion transport properties.

The results reveal that metastable edge-sharing phases exhibit superior Li-ion mobility compared with their stable corner-sharing counterparts. This superior conductivity is attributed to the Li-ion accessible volume, quantified by the packing ratio (fraction of the unit cell volume occupied by nonconductive volume) and by the dynamic distortion of the Li–S4 sublattice, which represents the local environment encountered by migrating Li-ions. The metastable phases feature higher packing efficiency, larger Li–S4 sublattice volume, and greater distortion, all of which contribute to improved Li-ion transport. This study highlights the potential of CSP to design novel SSEs and high-performance ASSBs.

In this work, we curate data sets of hemilabile and nonhemilabile ligands from experimentally characterized structures in the Cambridge Structural Database, analyze trends in observed coordination modes, and introduce four exhaustive and mutually exclusive types of hemilability.

Using these labeled data sets, we train graph neural networks to carry out classification of hemilabile ligands with high accuracy, precision, and recall and develop an ensemble algorithm that predicts primary and alternative chemically plausible coordination modes from SMILES strings in an end-to-end fashion. We demonstrate the utility of our algorithm by generating novel TMCs in predicted coordination modes and calculating the corresponding energy difference due to changes in coordination (i.e., ΔEc) with density functional theory.

Comparing our novel TMCs in multiple poses against an energetic criterion from experimentally observed TMCs confirms the plausibility of our alternative poses. We anticipate that our open-source workflows will accelerate organometallic discovery in experimental and virtual screening campaigns by proposing realistic metal–ligand coordination.

We address this by introducing oMeBench, the first large-scale, expert-curated benchmark for organic mechanism reasoning in organic chemistry. It comprises over 10,000 annotated mechanistic steps with intermediates, type labels, and difficulty ratings.

Furthermore, to evaluate LLM capability more precisely and enable fine-grained scoring, we propose oMeS, a dynamic evaluation framework that combines step-level logic and chemical similarity.

We analyze the performance of state-of-the-art LLMs, and our results show that although current models display promising chemical intuition, they struggle with correct and consistent multi-step reasoning. Notably, we find that using prompting strategy and fine-tuning a specialist model on our proposed dataset increases performance by 50% over the leading closed-source model.

Here, we aim to fill that identified gap and present a structured tutorial for experimentalists to integrate computer vision into high-throughput materials research, providing a detailed roadmap from data collection to model validation.

Specifically, we describe the hardware and software stack required for deploying CV in materials characterization, including image acquisition, annotation strategies, model training, and performance evaluation.

As a case study, we demonstrate the implementation of a CV workflow within a high-throughput materials synthesis and characterization platform to investigate the crystallization of metal–organic frameworks (MOFs). By outlining key challenges and best practices, this tutorial aims to equip chemists and materials scientists with the necessary tools to harness CV for accelerating materials discovery.

A data-driven pipeline is presented for assessing the sustainability of solvents and identifying greener substitutes. Three models are trained and evaluated on the GlaxoSmithKline Solvent Sustainability Guide (GSK SSG) to predict “greenness” metrics: a traditional Gaussian Process Regression (GPR) model, a fine-tuned GPT model (FT GPT), and a GPT model using in-context learning (ICL). It is found that GPR slightly outperforms language-based GPT models and is used to evaluate 10,189 solvents, forming GreenSolventDB–the largest public database of green solvent metrics.

These predictions are combined with Hansen solubility parameter-based metrics to identify greener solvents with solubility behavior similar to hazardous solvents. This approach is validated through case studies on benzene and diethyl ether, with predicted alternatives aligning well with known greener substitutes.

Building on this success, novel alternatives are proposed for the hazardous solvents listed in the GSK SSG. This framework for quantifying solvent sustainability and identifying greener substitutes is expected to significantly accelerate the discovery and adoption of environmentally-friendly solvents.

Assessing small-molecule blood–brain barrier permeability is laborious, yet critical in drug development. Quantitative prediction models are hindered by a lack of high-quality data set.

To address this, we curated PETBD, a novel data set of drug concentrations for 1056 positron emission tomography tracers across 14 organs at 60 min post injection, as well as in vivo metadata. We developed machine learning models to predict the brain-to-blood concentration ratio (log BB), and for the first time, drug concentration in the brain.

Extreme gradient boosting model reached the best performance in predicting Cbrain (R2 = 0.700) and also achieved state-of-the-art log BB prediction (R2 = 0.770). Feature importance analysis was employed to explain the contributions of physicochemical-based features. The model’s superior generalizability was validated against the B3DB benchmark and with unpublished PET tracers.

In this Perspective, we examine current challenges and opportunities for explainable AI (XAI) in molecular design and evaluate the benefits of incorporating domain-specific knowledge into XAI approaches for model refinement, experimental design, and hypothesis testing. In this context, we also discuss the current limitations in evaluating results from chemical language models that are increasingly used in molecular design and drug discovery.

Here we introduce MOSAIC (Multiple Optimized Specialists for AI-assisted Chemical Prediction), a computational framework that enables chemists to harness the collective knowledge of millions of reaction protocols. MOSAIC is built upon the Llama-3.1-8B-instruct architecture, training 2,498 specialized chemical experts within Voronoi-clustered spaces.

This approach delivers reproducible and executable experimental protocols with confidence metrics for complex syntheses. With an overall 71% success rate, experimental validation demonstrates the realizations of over 35 novel compounds, spanning pharmaceuticals, materials, agrochemicals, and cosmetics. Notably, MOSAIC also enables the discovery of new reaction methodologies that are absent from the expert’s training, a cornerstone for advancing chemical synthesis.

Organic synthetic chemistry has undergone a paradigm shift driven by breakthroughs in artificial intelligence (AI). Data-driven methods help accelerate hypothesis evaluation and reduce experimental trial-and-error efforts. However, its practical utility is constrained by the out-of-distribution (OOD) issue, where predictions usually fail when extrapolating to unseen reactions with new catalysts, substrates, or conditions.

Here, we introduce SynAD (synthetic applicability domain), a machine learning framework for assessing the predictive capability of AI models trained with existing data. SynAD combines descriptors with model-adaptive distance metrics to automatically demarcate reliable and unreliable reactions. Validated on the Ullmann Ligand Dataset (ULD, >5000 reactions), SynAD a priori distinguishes predictable chemical space, resulting in a prediction accuracy of R2 = 0.90 (at 12.3% coverage) from a baseline of R2 = −0.21. This capacity to target reliable chemical space is consistently observed across 6 additional datasets. We also enable a SynAD score to quantify reaction class predictability, guiding experimental focus on OOD spaces. By defining model limits, SynAD provides a critical guardrail for chemists to trust AI, allocate resources strategically, and accelerate de novo discovery.

Here, we present a quasi-autonomous AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by SAFE-MolGen, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental data sets to consider the impact of realistic experimental conditions.

Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal–ligand complexes and performs quantum mechanical free energy calculations to directly assess the metal selectivity.

We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe4BTBP.

The discovery of new organic photocatalysts (PCs) for energy transfer (EnT) catalysis remains a significant challenge, largely due to the vast and underexplored chemical space and the delicate balance of the photocatalytic properties. While transition-metal catalysts are effective, their high cost and environmental impact necessitate the development of metal-free alternatives.

In this work, we present a hybrid inverse molecular design strategy that combines global exploration with targeted local optimization to discover highly efficient organic PCs. Our approach leverages a generative model, guided by machine learning predictions and semiempirical simulations, to efficiently navigate chemical space and identify promising molecular scaffolds. We demonstrate the utility of this strategy by rediscovering known PCs and, more importantly, exploring uncharted structural regions, leading to the identification of novel candidates with favorable photophysical properties. A subsequent local exploration stage, using quantum mechanical calculations, allows refinement of the properties as well as control of the synthetic complexity.

The practical applicability of the approach is demonstrated by performing a local exploration of one of the identified scaffolds and successfully synthesizing four candidate PCs. We showcase their catalytic aptitude in three different EnT-mediated reactions, including a challenging aza-photocycloaddition, where one of our designed PCs achieved 90% yield, a performance comparable to a state-of-the-art iridium-based catalyst. This study highlights the power of a data-driven inverse design framework to bridge computational discovery and experimental validation, accelerating the identification of novel PCs and expanding the scope of EnT catalysis.

In this work, we release the IUPAC Digitized pKa Dataset, a digital version of a critically-assessed collection of data compiled up to 1970. The dataset includes metadata such as temperature, measurement method, assessed reliability of data, and chemical identifiers such as SMILES and InChI strings.

The dataset spans 24,222 entries across 10,564 unique molecules, making it the largest FAIR open-source dataset publicly available for aqueous pKa data. Herein, we detail the data digitization and checking process, and assess the informational space spanned by the data.

We introduce SUPERChem, a benchmark of 500 expert-curated reasoning-intensive chemistry problems, covering diverse subfields and provided in both multimodal and text-only formats. Original content and an iterative curation pipeline eliminate flawed items and mitigate data contamination. Each problem is paired with an expert-authored solution path, enabling Reasoning Path Fidelity (RPF) scoring to evaluate reasoning quality beyond final-answer accuracy.

Evaluations against a human baseline of 40.3% accuracy show that even the best-performing model, GPT-5 (High), reaches only 38.5%, followed closely by Gemini 2.5 Pro (37.9%) and DeepSeek-V3.1-Think (37.3%). SUPERChem elicits multi-step, multimodal reasoning, reveals model-dependent effects of visual information, and distinguishes high-fidelity reasoners from heuristic ones. By providing a challenging benchmark and a reliable evaluation framework, SUPERChem aims to facilitate the advancement of LLMs toward expert-level chemical intelligence.

Nickel/photoredox catalysis in cross-coupling reactions offers mild operating conditions for efficient C–C bond formation, expanding synthetic access to pharmaceutically relevant molecules. However, routine implementation of such reactions remains constrained by the intricate reaction mechanism and limited availability of experimental data, which complicate optimization tasks and the development of predictive models. The integration of quantum-mechanical (QM) calculations with machine learning (ML) has proven to be effective for developing predictive models of complex reactions with sparse experimental data.

Here, we present a combined approach that integrates automated density functional theory calculations, ML, and parallel synthesis to develop quantum mechanics-machine learning (QM-ML) models for the nickel metallophotoredox cross-coupling reaction feasibility prediction. Random-Forest classification models are trained to predict the outcome of a given reaction using DFT-computed descriptors from automatically generated 3D structures of catalytic cycle intermediates. We demonstrate the broad applicability of this approach, applying it to a diverse data set encompassing four reaction subtypes, namely, bromide cross-electrophile couplings, chloride cross-electrophile couplings, deoxygenative couplings, and amino radical transfer (ART) couplings, augmented with additional experiments curated by a systematic cheminformatics method to broaden the alkyl halides scope. We show on a blind literature data set that such a QM-ML approach can successfully predict the feasibility of complex reactions from heterogeneous data sets with minimal data requirements and can generalize it to unseen reaction subtypes with a few-shot learning approach, affording a computational model for ART coupling. Together, these capabilities provide a data-efficient solution for rapidly predicting the outcome of cross-coupling reactions and facilitate the adoption of nickel photocatalysis in the MAKE stage of the DMTA cycle.

Understanding how metals coordinate to organic ligands is a precondition for the rational design of metal complexes and catalysts. Whereas certain types of ligands are capable of just one easy-to-predict coordination modality, others may present tens and sometimes even hundreds of coordination options (mono-, bi-, or polydentate), and predicting the correct one may be a challenge even to seasoned chemists.

The current paper describes a “hybrid” computational approach in which a Machine Learning, ML, algorithm learns to predict complex coordination patterns using knowledge-based “rules” derived from the Cambridge Structural Database, CSD. This model is applicable to a broad scope of ligands (including hemilabile and haptic ones as well as those with denticity > 6) and different metals at different oxidation states. The algorithm's code is disclosed and can be readily deployed in RDKit via our RDMetallics python-wrapper. It is also deployed as a publicly accessible web portal for demonstration and use.