Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Explainable Artificial Intelligence (XAI) now offers accessible chemical insights. The XpertAI framework uses large language models (LLMs) to translate complex data into understandable natural language explanations for structure-property relationships.
Area Of Science
- Artificial Intelligence
- Chemistry
- Data Science
Background
- Explainable Artificial Intelligence (XAI) methods address the opacity of machine learning models.
- XAI is valuable for elucidating structure-property relationships in chemistry.
- Current XAI tools often require technical expertise, limiting broader accessibility.
Purpose Of The Study
- To develop a framework that makes XAI more accessible to chemists.
- To integrate XAI with large language models (LLMs) for automated data interpretation.
- To generate natural language explanations of chemical data.
Main Methods
- The XpertAI framework was developed, integrating XAI techniques with LLMs.
- The framework accesses scientific literature to inform explanations.
- Five case studies were conducted to evaluate XpertAI's performance.
Main Results
- XpertAI successfully generated accessible, natural language explanations of raw chemical data.
- The framework demonstrated the ability to extract input-output relationships.
- Case studies confirmed the generation of specific, scientific, and interpretable explanations.
Conclusions
- XpertAI bridges the gap between complex XAI methods and chemical data interpretation.
- The framework leverages LLMs and XAI to enhance understanding of structure-property relationships.
- XpertAI offers a user-friendly approach to chemical data analysis using AI.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Skeletal Model
Simpler two-dimensional representations of chemical compounds are accomplished using skeletal models. The illustration shows only the molecular framework or bonds without explicitly showing the atoms. In this representation, many of the carbon atoms...
VSEPR Theory for Determination of Electron Pair Geometries
The following procedure uses VSEPR theory to determine the electron pair geometries and the molecular structures:
Write the Lewis structure of the molecule or polyatomic ion.
Count the number of electron groups (lone pairs and bonds) around the central atom. A single, double, or triple bond counts as one region of electron density.
Identify the electron-pair geometry based on the number of electron groups: linear, trigonal planar,...
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
The carbon atoms also have an unhybridized 2p atomic orbital...