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Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging

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Recent advances in the SISSO method and their implementation in the SISSO++ code.

Thomas A R Purcell¹, Matthias Scheffler¹, Luca M Ghiringhelli^1,2

¹The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195 Berlin, Germany.

The Journal of Chemical Physics

|September 18, 2023

Summary

Advancements in the SISSO++ algorithm enhance explainable artificial intelligence (AI) for materials discovery. New features improve descriptor generation and solver efficiency, accelerating the development of accurate AI models.

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Area of Science:

Materials Science
Computational Chemistry
Artificial Intelligence

Background:

Explainable artificial intelligence (AI) models accelerate materials discovery.
Symbolic regression offers analytical descriptions of material properties.
The Sure-Independence Screening and Sparsifying Operator (SISSO) is a deterministic method for feature selection.

Purpose of the Study:

Introduce advancements in the SISSO algorithm, implemented as SISSO++.
Enhance feature creation and descriptor generation capabilities.
Improve the reliability and efficiency of AI models for materials science.

Main Methods:

Developed SISSO++ with C++ and Python bindings.
Introduced a new mathematical expression representation for feature creation.
Incorporated controlled nonlinear optimization into feature engineering.
Refined regression and classification solver algorithms.

Main Results:

SISSO++ offers a new representation for mathematical expressions.
Expanded descriptor range through nonlinear optimization.
Significantly increased reliability and efficiency of SISSO.
Demonstrated potential impact of new features.

Conclusions:

SISSO++ represents a significant advancement in explainable AI for materials discovery.
The enhanced algorithm provides more robust and efficient tools for developing accurate AI models.
Future work may involve integrating "grammar" rules for feature creation.