Opportunity timeline

PostedSep 1, 2026
DeadlineMar 2, 2027

In plain English

Los Alamos National Laboratory is offering licensing opportunities for AmineBind ML, a Python-based software toolkit and machine-learning model that predicts CO2 binding energies for amine-based carbon-capture materials. The tool screens millions of chemical structures much faster than density functional theory and is intended for direct-air-capture, specialty-chemical, computational-chemistry, and environmental applications. The technology is at TRL 3, covered by a U.S. patent application, and is available through exclusive or non-exclusive licensing; this is not a request for outside development services.

About this opportunity

A descriptor?based software and model for amine-based carbon capture discovery Organizations that design sorbents for removing CO2 from air gain a fast, chemistry?aware way to rank candidates and focus resources on the most promising structures. AmineBind ML, a trained surrogate model, packaged with user?friendly software, predicts CO2 binding energies for amine active sites from simple molecular inputs. Teams can screen vast chemical spaces in minutes, align material choices with target regeneration temperatures and reduce trial?and?error in lab campaigns. Overview Developed by Los Alamos National Laboratory, the software ingests a chemical structure as a SMILES string, identifies amine…

Time remaining

Closes Mar 2, 2027

168Days
:
14Hours
:
59Min
:
59Sec

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Ref No.

BS-0AEAF4