This work combines large-scale computational exploration with accelerated experimental synthesis to uncover underexplored regions of Li/Na-containing metal oxide chemistries relevant to electrochemical energy storage and develops solid-state and wet-chemical synthesis platforms supported by automation, robotics, and AI-guided decision-making.
Abstract
Data-driven materials discovery accelerates the identification of functional compounds but can be hindered by gaps in existing databases and biases introduced when missing phases go unrecognized. To address these limitations, we combine large-scale computational exploration with accelerated experimental synthesis to uncover underexplored regions of Li/Na-containing metal oxide chemistries relevant to electrochemical energy storage. Our computational workflow integrates diverse structural prototypes, isovalent substitution strategies, and existing experimental knowledge to identify new ground-state and metastable compositions, revealing promising cation-rich systems with potential for enhanced Li/Na-ion based energy storage. Complementing this effort, we develop solid-state and wet-chemical synthesis platforms supported by automation, robotics, and AI-guided decision-making. These workflows streamline precursor selection, reaction condition optimization, and navigation of complex chemistries and pathways. The computational insights and accelerated synthesis methods provide a unified framework for expanding inorganic materials databases and enabling the rational discovery of next-generation battery materials.
Accurate generation of intricate 3D molecular structures is a fundamental challenge in computational chemistry. Transition states (TS), the transient structures that dictate reaction kinetics, exemplify this challenge, as their calculation remains a major bottleneck in elucidating reaction mechanisms. While generative AI has shown promise in automating TS generation, existing methods are largely confined to simple systems, struggling with the complex structures like those in transition metal catalysis. Here we show a unified framework for general-purpose TS generation, comprising the UniTS-Lib library of 4,391 high-quality structures spanning 42 elements and diverse chemical transformations, coupled with the UniTS-Gen diffusion model that generates 3D TS configurations from 2D reactant graphs using a custom-designed higher-degree equivariant network. Validation demonstrates UniTS-Gen’s superior accuracy and robust generalization to unseen chemical systems. We show that UniTS-Gen provides reliable initial guesses and accelerates discovery by locating kinetically favored conformations. This work provides a scalable and transferable solution for automating mechanistic studies in organic synthesis and beyond.
Li-Cheng Xu, Junyi An, Weiqi Liu et al.· Nature Communications· 0 citations
We report an end-to-end computational-experimental workflow for the discovery of metal-organic frameworks (MOFs), demonstrated by the computational design and synthesis of two novel Zn-based frameworks, UCHI-1 and UCHI-2, exhibiting enhanced methane uptake and selectivity at low pressure under ambient conditions (298 K, 1 bar). The workflow enables the rational selection and experimental realization of metal-organic frameworks combining data mining, machine-learning driven adsorption prediction, and structure generation, with experimental synthesis and validation within a closed-loop discovery pipeline. Analysis of existing and newly generated MOFs reveals the structure-property relationships governing low-pressure methane adsorption, identifying an optimal pore size and shape, framework densities, linker functionalities, and framework topologies that maximize dispersive C-H/π and van der Waals interactions. Beyond the specific materials identified herein, the results establish this workflow as a scalable and extensible platform for accelerated MOF discovery, with clear routes toward further optimization and automation while demonstrating practical applicability beyond purely theoretical exploration of hypothetical materials.
A. Darù, Jianheng Ling, Xiaoliang Wang et al.· Journal of the American Chem...· 1 citation
The optimization of chemical reactions, both catalytic and noncatalytic, to simultaneously achieve high selectivity, activity, and stability remains a central challenge in homogeneous catalysis and synthetic organic chemistry. Traditional discovery pipelines rely heavily on labor-intensive experimental screening, while current automated computational workflows are often limited by incomplete end-to-end automation, restricted accessibility beyond expert computational chemists, integration of quantum-mechanical surrogate methods, and reliance on single or approximate performance descriptors. A general-purpose, turnkey software platform that extends accessibility beyond command-line execution and removes the need for programming expertise can empower nonexpert scientists to design molecular catalysts and optimize chemical reactions. Here, we present RxnEnumProfiler, a highly automated, GUI-enabled software platform for mechanism-guided molecular catalyst design and reactivity optimization that integrates quantum mechanical methods, semiempirical tight-binding approaches, and reactive machine-learning interatomic potentials into a unified end-to-end workflow for simultaneous multimetric catalyst scoring and parallel exploration of competing reaction pathways. This platform enables rapid, high-throughput evaluation of arbitrary objective performance descriptors and subsequent ranking of candidate compounds via reaction network enumeration and profiling, substantially expanding the accessible chemical design space and accelerating the discovery of high-performing molecular catalysts and reactions. The generality of this approach is demonstrated across three representative homogeneous catalytic systems─organocatalyzed asymmetric hydrogenation, Pd-catalyzed C-O cross-coupling, and ansa-metallocene-catalyzed isotactic propylene polymerization─as well as a noncatalytic transformation relevant to epoxy curing chemistry.
Pavel A. Dub, Thomas F. Hughes, T. Mustard· Journal of Chemical Informat...· 0 citations
Developing reliable synthesis routes for complex materials remains a major bottleneck in accelerating materials discovery. This study establishes a large language model-based framework for predicting and optimizing synthesis conditions directly from the literature data. Key synthesis information, including target compounds, precursors, and processing parameters, was systematically extracted from 4407 open-access solid-state synthesis papers and organized into a structured recipe dataset. Using a retrieval-augmented generation (RAG) approach, the system first retrieves similar recipes from the corpus and then generates a new candidate recipe conditioned on those exemplars. The generated recipes were benchmarked against literature data using quantitative scoring metrics, achieving strong agreement with experimentally reported conditions. To validate the predictive capability, the framework was applied to unreported solid-state electrolyte candidates identified through first-principles screening, and multiple oxy-selenide compounds were successfully synthesized through iterative feedback between the model and experiment. The recipe generator accurately refined synthesis parameters over successive trials, demonstrating its ability to reproduce phase-pure products while minimizing trial-and-error. This approach establishes a data-driven, feedback-optimized route to accelerate synthesis design, offering a generalizable paradigm for integrating language models into experimental materials research.
Dong Won Jeon, Dong Hwi Kim, Taeyang Jeon et al.· Advances in Materials· 0 citations
ABSTRACT Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual compounds rather than on accelerated broad‐range exploration. In this work, we first evaluate the synthesizability and band gaps of 909 hypothetical ternary phosphosulfides by density functional theory. We find 19 previously unknown thermodynamically stable compounds, including the first Si‐ and Ge‐based phosphosulfides. For rapid band gap prediction, we then develop a multi‐fidelity machine learning model to translate semilocal density functional theory band gaps into experimentally calibrated band gaps. Importantly, we extend the accelerated material development workflow to the experimental domain by demonstrating a route to high‐throughput synthesis and characterization of virtually any phosphosulfide material system. The method is based on thin‐film combinatorial libraries and yields over 100 unique compositions in each experiment, enabling us to synthesize four distinct phosphosulfide compounds in only four combinatorial experiments without prior synthesis recipes and without compromising on material quality. Thus, we argue that accelerated materials development workflows combining theory, artificial intelligence, synthesis, and characterization can be viable even for experimentally challenging inorganic materials.
Javier Sanz Rodrigo, Nicholas A. Kryger-Nelson, Lena A. Mittmann et al.· Small· 1 citation
Designing second-sphere microenvironments that promote proton-coupled electron transfer is central to catalysis yet difficult to achieve in porous solids, such as metal–organic frameworks (MOFs). Here, we report an end-to-end workflow that couples literature-guided large-language-model (LLM) reasoning with real-time experimental feedback to propose, test, and refine microenvironment designs in MOF photocatalysts. The system mined and fused three domains (namely, photocatalytic H2 production, hydrogenases and enzyme-mimetic catalysis) and deduced the hypothesis that placing basic, hydrogen-bonding groups near catalytic centers would facilitate water activation and proton transfer. The hypothesis was instantiated by postsynthetic modification of UiO-67, generating 31 Pt@UiO-67-X variants and evaluating them across six closed-loop iterations on an automated platform. The search converged on Pt@UiO-67-30 (8-quinolinecarboxylic acid), which delivered 2.33 mmol g–1 h–1, a ∼36-fold improvement over the parent material; in a larger, optimally illuminated reactor the same catalyst reached 12.48 mmol g–1 h–1 while preserving the library’s rank order. Photoluminescence quenching, enhanced photocurrent, and reduced impedance are consistent with faster charge separation, and first-principles calculations are consistent with reduced proton-transfer barriers via N···H hydrogen-bond networks. These results establish a practical microenvironment-engineering strategy in MOFs and show how LLM-guided knowledge fusion with experiment-in-the-loop reasoning can systematize and accelerate targeted discovery of functional materials.
Yi-Ming Zhao, Tao Song, Lin-Jiang Chen et al.· Journal of the American Chem...· 0 citations
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