REVIEW 2 cited by
A Method for Inferring Polymers Based on Linear Regression and Integer Programming
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In this paper, we design a new method for inferring a polymer based on the framework. For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers. We also use linear regression as a building block of constructing a prediction function in the framework. The results of our computational experiments reveal a set of chemical properties on polymers to which a prediction function constructed with linear regression performs well. We also observe that the proposed method can infer polymers with up to 50 non-hydrogen atoms in a monomer form.
Forward citations
Cited by 2 Pith papers
-
Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks
Small 7B-14B parameter language models trained on the new PolyBench dataset for polymer design tasks outperform similar-sized models and compete with large frontier LLMs while improving on external benchmarks.
-
Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks
PolyBench, a 125K-question benchmark with chain-of-thought reasoning, lets small language models achieve competitive polymer design performance.
Discussion (0). Sign in to comment.