arXiv Machine Learning
Aug 12
Inverse Design of Inorganic Compounds with Generative AI
arXiv:2604. 11827v2 Announce Type: replace-cross Abstract: Machine learning is revolutionizing chemistry.
Data-knowledge dual-driven intelligent framework for full-chain, experiment-efficient synthesis of 2D dendrites
arXiv:2603. 16959v2 Announce Type: replace-cross Abstract: Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism deciphering.
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arXiv Machine Learning
Jun 2
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.
arXiv AI
Jun 9
A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design
arXiv:2601. 02424v2 Announce Type: replace-cross Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties.
arXiv AI
Jul 10
Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses
arXiv:2607. 08003v1 Announce Type: cross Abstract: Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computationally intensive screening.
arXiv AI
Jun 16
Sustainable Materials Discovery in the Era of Artificial Intelligence
arXiv:2601. 21527v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening.
arXiv AI
Jun 8
Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
arXiv:2606. 05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator.
arXiv Machine Learning
Jun 17
Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining
arXiv:2606. 17445v1 Announce Type: new Abstract: Inverse design of heterogeneous catalysts remains challenging because catalyst surfaces exhibit substantial structural complexity with coupled surface-adsorbate interactions across a vast chemical space that is difficult to explore efficiently through conventional screening alone.
arXiv Machine Learning
Jun 2
Benchmark Dataset for Catalysis on 2D MXenes
arXiv:2606. 00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials.
arXiv AI
Jul 21
Chemical filters for ultra-high-throughput materials screening and generation
arXiv:2607. 17910v1 Announce Type: cross Abstract: Generative artificial intelligence is rapidly transforming materials design by enabling de novo exploration of immense chemical spaces.
arXiv AI
Jun 2
Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
arXiv:2606. 00315v1 Announce Type: new Abstract: Modern generative machine learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning of these materials remains difficult due to the complexity of the associated physical processes and limited availability of computational tools.
arXiv AI
1d ago
RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
arXiv:2608. 16111v1 Announce Type: cross Abstract: Retrosynthesis is a cornerstone of drug discovery and organic synthesis.
arXiv AI
1d ago
Empowering Polymeric Materials Discovery by Artificial Intelligence
arXiv:2606. 20753v2 Announce Type: replace-cross Abstract: Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing.