arXiv Machine Learning
Jun 9
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
arXiv:2208. 00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets.
SFILES 2.0: An extended text-based flowsheet representation
arXiv:2208. 00778v2 Announce Type: replace-cross Abstract: SFILES are a text-based notation for chemical process flowsheets.
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arXiv AI
Jul 3
ChemGraph-XANES: An Agentic Framework for XANES Simulation and Curation
arXiv:2604. 16205v2 Announce Type: replace-cross Abstract: Computational X-ray absorption near-edge structure (XANES) is widely used to interpret local coordination environments, oxidation states, and electronic structure in chemically complex systems.
Hugging Face Trending Papers
Aug 5
CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications. CheMLFlow targets a common bottleneck in scientific machine learning development, where researchers often need to assemble data acquisition, curation, representation, model training, validation, screening, interpretation, and reporting into a reproducible pipeline, even when their primary research contribution concerns only one stage.
arXiv AI
Aug 6
CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
arXiv:2608. 04942v1 Announce Type: cross Abstract: CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications.
arXiv AI
Jun 9
Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence
arXiv:2412. 00508v2 Announce Type: replace-cross Abstract: Control structure design is an important but tedious step in P&ID development.
arXiv AI
Jun 9
Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs
arXiv:2502. 18493v2 Announce Type: replace-cross Abstract: A piping and instrumentation diagram (P&ID) is a central reference document in chemical process engineering.
arXiv AI
Jun 9
Toward autocorrection of chemical process flowsheets using large language models
arXiv:2312. 02873v2 Announce Type: replace-cross Abstract: The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process flows and equipment configurations.
arXiv AI
Jun 30
A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method
arXiv:2602. 02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure.
arXiv Machine Learning
Aug 3
Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies
arXiv:2607. 29389v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications.
arXiv AI
Aug 13
Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models
arXiv:2608. 11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity.
arXiv AI
Sep 23
ChemVTS-Bench: Evaluating Visual-Textual-Symbolic Reasoning of Multimodal Large Language Models in Chemistry
ChemVTS-Bench is a domain-authentic benchmark that evaluates Visual‑Textual‑Symbolic reasoning in multimodal large language models for chemistry. It presents diverse chemical problems—organic molecules, inorganic materials, and 3D crystal structures—in three input modes: visual-only, visual‑text hybrid, and SMILES-based symbolic. The benchmark includes an automated agent workflow for inference, answer verification, and failure diagnosis, and shows that visual-only inputs and structural chemistry remain challenging for current models.
arXiv Machine Learning
Aug 3
MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation
arXiv:2607. 29479v1 Announce Type: new Abstract: Text-to-molecule generation is typically formulated as a one-shot sequence generation problem, where a model directly maps target descriptions to molecular representations.