SAM #S-196258

Licensing Opportunity for AmineBind ML Software from Los Alamos National Laboratory

This opportunity is a technology licensing program from Los Alamos National Laboratory (LANL), under the Department of Energy and Triad National Security, LLC. - The offering is for AmineBind ML software, a descriptor-based machine learning toolkit for amine-based carbon capture discovery. - The software parses molecular inputs in SMILES format, computes descriptors for amine sites, and predicts CO2 binding energies using a trained surrogate model. - Patent pending (LA-UR-26-27826), identified as T5090. - Licensing is available under exclusive or non-exclusive agreements for patented and patent-pending inventions and copyrighted software. - No external development or service contracts are requested; this is strictly a technology licensing opportunity. - The OEM is Los Alamos National Laboratory. - The software is at Technology Readiness Level (TRL) 3. - Intended for high-throughput screening of chemical structures for direct air capture and integration with materials pipelines. - Commercial companies with expertise in carbon capture, chemical informatics, or materials discovery may be interested.

Key Details: - Agency: Los Alamos National Laboratory (DOE) - Product: AmineBind ML Software (T5090) - Licensing: Exclusive or non-exclusive - Patent Status: Patent pending - No external services required - Technology Readiness Level: 3

Description

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 binding sites, then uses a descriptor‑based machine learning surrogate model trained on roughly 20,000 electronic‑structure calculations to predict CO2 binding energetics. Inference runs far faster than density functional theory, which enables high‑throughput exploration of millions of candidate chemistries for direct air capture. Predictions at the atomic scale can be combined with mesoscale modeling to feed broader materials pipelines.

Technology Description

AmineBind ML includes a Python‑based toolkit that parses molecular inputs in SMILES format, computes chemically meaningful descriptors for amine sites, and applies a trained model to estimate CO2 binding energies. Training data come from binding energetics computed for ~20,000 molecules, anchoring predictions to first‑principles energetics and supporting generalization across diverse amine chemistries. Model inference achieves orders‑of‑magnitude speed‑ups versus DFT, which enables rapid ranking and down‑selection prior to expensive simulations or synthesis.

This bundle supports screening of millions of structures for direct air capture, delivering candidate materials that balance strong CO2 uptake with manageable regeneration temperatures to minimize operational costs and mitigate sorbent degradation. The atomic‑level predictions can integrate with mesoscale treatments, creating a robust modeling pipeline that links molecular binding energetics to process‑level performance.

Advantages

Rapid screening of large chemical spaces from simple SMILES inputs Orders‑of‑magnitude faster predictions than DFT for CO2 binding energetics Better targeting of materials that balance capture strength and regeneration needs Integration with mesoscale models to support end‑to‑end materials workflows Software package designed for researchers in chemistry and materials science

Market Applications

Direct air capture (materials discovery, sorbent optimization) Specialty chemicals (amine functional design, process modeling) Computational chemistry software (screening tools, model‑based decision support) Environmental services (air capture planning, emissions reduction analysis)

TRL 3

Software information: T5090

U.S. Patent pending

LA-UR-26-27826

LANL Tech Partnerships: Unlock the Innovative Potential

Los Alamos National Laboratory offers a wide range of cutting-edge technologies and capabilities that may provide your company with a competitive edge in the market and unlock the innovative potential that can enhance, refine, and revolutionize your products.

LANL’s licensing program focuses on moving inventions developed by our researchers to commercial innovations. Patented and patent pending inventions and copyrighted software are available to existing and start-up companies through exclusive and non-exclusive licensing agreements. For specific discussions, please contact licensing@lanl.gov.

Note: This is not a call for external services for the development of this technology.

https://www.lanl.gov/engage/collaboration/feynman-center/partner-with-us/licensing-technology

m.lanl.gov/tech-search

View original listing