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Solvents01:12

Solvents

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A solvent is a substance, most often a liquid, that can dissolve other substances. Here, the substance being dissolved is called a solute. When a solvent and a solute combine, they form a solution - a homogenous mixture of both the solvent and the solute. Water is a universal biological solvent. Its polar structure allows it to dissolve many other polar compounds. The ability of water to dissolve is governed by a balance between water molecules binding to each other and binding to the solute.
A...
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Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
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Updated: Jan 11, 2026

High-Throughput Measurement and Classification of Organic P in Environmental Samples
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High-Throughput Measurement and Classification of Organic P in Environmental Samples

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Machine Learning for Green Solvents: Assessment, Selection and Substitution.

Rohan Datta1, Janhavi Nistane2, Abhishek Sose2

  • 1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|November 17, 2025
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Summary

A new data-driven pipeline assesses solvent sustainability, creating the largest green solvent database. This accelerates the discovery of environmentally friendly solvents that match hazardous ones in performance.

Keywords:
green solventsmachine learning

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Area of Science:

  • Green Chemistry
  • Computational Chemistry
  • Environmental Science

Background:

  • Growing demand for sustainable solvents due to strict environmental regulations.
  • Existing Solvent Selection Guides (SSGs) are limited in scope and data availability.
  • Identifying greener alternatives requires balancing complex criteria like sustainability, cost, and performance.

Purpose of the Study:

  • To develop a data-driven pipeline for assessing solvent sustainability and identifying greener substitutes.
  • To expand the pool of evaluated solvents beyond the limited scope of current guides.
  • To create a comprehensive database of green solvent metrics.

Main Methods:

  • Trained and evaluated Gaussian Process Regression (GPR) and GPT models on the GlaxoSmithKline Solvent Sustainability Guide (GSK SSG).
  • Utilized GPR to predict greenness metrics for 10,189 solvents, creating the GreenSolventDB.
  • Combined predicted metrics with Hansen solubility parameters to find suitable greener alternatives.

Main Results:

  • GPR model slightly outperformed language-based GPT models in predicting solvent greenness.
  • GreenSolventDB was established as the largest public database of green solvent metrics.
  • Predicted alternatives for hazardous solvents like benzene and diethyl ether showed good alignment with known substitutes.

Conclusions:

  • The developed framework effectively quantifies solvent sustainability and identifies viable greener substitutes.
  • This approach significantly accelerates the discovery and adoption of environmentally friendly solvents.
  • The GreenSolventDB provides a valuable resource for researchers and industry.