MatCreatioNN: Machine learning-guided computational discovery of photocatalysts for environmental applications

• URL: http://arxiv.org/abs/2607.27295v1
• Date: Wed, 29 Jul 2026 16:03:07 GMT
• Title: MatCreatioNN: Machine learning-guided computational discovery of photocatalysts for environmental applications
• Abstract summary: This work presents an integrated machine-learning framework that couples reinforcement learning-based metal-organic framework (MOF) generation with a multi-stage Crystal Graph Convolutional Neural Network (CGCNN) prediction funnel to identify photocatalysts optimized across multiple electronic and structural features.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rational design of photocatalysts for environmental remediation and CO2 conversion remains limited by the high computational cost and sparse experimental data describing multi-parameter photocatalytic behavior. This work presents an integrated machine-learning framework that couples reinforcement learning-based metal-organic framework (MOF) generation with a multi-stage Crystal Graph Convolutional Neural Network (CGCNN) prediction funnel to identify photocatalysts optimized across multiple electronic and structural features. 120,000 MOF candidates were generated and screened using 13 key descriptors, including band-gap suitability, CO2/H2O selectivity, adsorption energy, and structural stability. The funnel approach reduced computational cost by 4.13-fold while maintaining predictive robustness. Two top candidates, a Cr-based and a Zn-based MOF, exhibited predicted photocatalytic fitness values of 1.70 +/- 0.25 and 1.20 +/- 0.05 fold higher respectively than benchmark materials such as PCN-224(Zr), demonstrating simultaneous improvements in light absorption, redox energetics, and framework durability. Simulated X-ray diffraction patterns confirmed strong structural agreement with experimentally synthesized MOFs, indicating high synthesizability. Post-hoc analysis revealed recurring structural motifs, such as the N262 metal cluster, that correlated strongly with high predicted photocatalytic activity. These results highlight the potential of data-driven methods to accelerate discovery of efficient and durable photocatalysts for environmental and energy-related transformations, providing a foundation for experimental realization and large-scale implementation of computationally designed MOFs.

Related papers

• Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures [66.44589802420899]
  We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport (QT) solver.<n>The framework enables 10,000X speedups over density functional theory to produce the Hamiltonian matrix of devices made of >20,000 atoms.
  arXiv  Detail & Related papers  (2026-08-27T15:33:39Z)
• Catalyst Diffusion Transformer: Generative Inverse Design of Heterogeneous Catalysts [0.18665975431697432]
  Catalyst Diffusion Transformer (CatDiT) is a unified framework for inverse catalyst design.<n>CatDiT generates valid and novel structures ranging from intermetallic alloys to oxide surfaces.
  arXiv  Detail & Related papers  (2026-07-27T11:11:33Z)
• Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems [0.0]
  This work presents an interpretable machine learning framework that integrates SHAP-based feature importance analysis (Explainable AI) with tree-based ensembles to characterize Fe-zeolite and oxide-supported catalysts for the partial oxidation of methane (POM)<n>Despite limited data, the framework decodes complex structure-performance relationships by identifying and ranking thermodynamic, structural, and geometric microdescriptors that influence the electronic band gap and govern macroscale performance metrics such as selectivity, activity, and stability.
  arXiv  Detail & Related papers  (2026-05-09T15:25:30Z)
• OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction [63.318434943975255]
  We introduce OXtal, a large-scale 100M parameter all-atom diffusion model that learns the conditional joint distribution over intramolecular conformations and periodic packing.<n>By leveraging a large dataset of 600K experimentally validated crystal structures, OXtal achieves orders-of-improvement over prior ab initio machine learning CSP methods.<n> OXtal attains over 80% packing similarity rate, demonstrating its ability to model both thermodynamic and kinetic regularities of molecular crystallization.
  arXiv  Detail & Related papers  (2025-12-07T20:46:30Z)
• QuantumChem-200K: A Large-Scale Open Organic Molecular Dataset for Quantum-Chemistry Property Screening and Language Model Benchmarking [1.18832001529231]
  We introduce QuantumChem-200K, a large-scale dataset of over 200,000 organic molecules annotated with eleven quantum-chemical properties.<n>These values are computed using a hybrid workflow that integrates density function theory (DFT), semi-empirical excited-state methods, atomistic quantum solvers, and neural-network predictors.<n>Using QuantumChem-200K, we fine tune the open-source Qwen2.5-32B large language model to create a chemistry AI assistant capable of forward property prediction from SMILES.
  arXiv  Detail & Related papers  (2025-11-23T02:33:06Z)
• Foundation Models for Discovery and Exploration in Chemical Space [57.97784111110166]
  MIST is a family of molecular foundation models trained on large unlabeled datasets.<n>We demonstrate the ability of these models to solve real-world problems across chemical space.
  arXiv  Detail & Related papers  (2025-10-20T17:56:01Z)
• Single photon emission from lithographically-positioned engineered nanodiamonds for cryogenic applications [1.2096924420929192]
  Nitrogen-vacancy centres in nanodiamonds (NDs) provide a promising resource for quantum photonic systems.<n>We present a hybrid materials platform by photolithographically positioning ball-milledally-enriched NDs on broadband metal reflectors.
  arXiv  Detail & Related papers  (2025-08-08T16:11:41Z)
• Photon-mediated interactions and dynamics of coherently driven quantum emitters in complex photonic environments [41.94295877935867]
  Born-Markov master equations have been extensively employed in the description of quantum optical phenomena.<n>We benchmark this modeling approach for the quantum dynamics of the emitter pair against exact calculations based on a macroscopic field quantization formalism.<n>Our analysis reveals four distinct regimes of laser driving and frequency splitting that lead to markedly different levels of accuracy in the effective model.
  arXiv  Detail & Related papers  (2025-08-01T09:38:07Z)
• Spatial super-resolution in nanosensing with blinking emitters [79.16635054977068]
  We propose a method of spatial resolution enhancement in metrology (thermometry, magnetometry, pH estimation, and similar methods) with blinking fluorescent nanosensors.<n>We believe that blinking fluorescent sensing agents being complemented with the developed image analysis technique could be utilized routinely in the life science sector.
  arXiv  Detail & Related papers  (2024-02-27T10:38:05Z)
• First-Principles Screening of Metal-Organic Frameworks for Entangled Photon Pair Generation [0.0]
  nonlinear optical devices based on metal-organic framework (MOF) materials can efficiently generate entangled light via spontaneous down-conversion(SPDC) We screen a database of 114,373 synthesized MOF materials to establish correlations between the structure and chemical composition of MOFs with the brightness and coherence properties of entangled photon pairs. Our work paves the way for the computational design of MOF-based devices for optical quantum technology.
  arXiv  Detail & Related papers  (2023-09-09T12:56:26Z)
• Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction [61.9212585617803]
  Graph neural networks (GNNs) have drawn more and more attention from material scientists. We develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task.
  arXiv  Detail & Related papers  (2022-09-15T13:52:15Z)
• Inverse-designed photon extractors for optically addressable defect qubits [48.7576911714538]
  Inverse-design optimization of photonic devices enables unprecedented flexibility in tailoring critical parameters of a spin-photon interface. Inverse-designed devices will enable realization of scalable arrays of single-photon emitters, rapid characterization of new quantum emitters, sensing and efficient heralded entanglement schemes.
  arXiv  Detail & Related papers  (2020-07-24T04:30:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.