The Artificial Intelligence Explanatory Trade-Off on the Logic of Discovery in Chemistry

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Ferraz-Caetano, J (Author) FCUP

Journal

Title: Philosophies

Vol. 8

Final page: 17

ISSN: 2409-9287

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00Y-3CQ

DOI: 10.3390/philosophies8020017

Abstract (EN): Explanation is a foundational goal in the exact sciences. Besides the contemporary considerations on 'description', 'classification', and 'prediction', we often see these terms in thriving applications of artificial intelligence (AI) in chemistry hypothesis generation. Going beyond describing 'things in the world', these applications can make accurate numerical property calculations from theoretical or topological descriptors. This association makes an interesting case for a logic of discovery in chemistry: are these induction-led ventures showing a shift in how chemists can problematize research questions? In this article, I present a fresh perspective on the current context of discovery in chemistry. I argue how data-driven statistical predictions in chemistry can be explained as a quasi-logical process for generating chemical theories, beyond the classic examples of organic and theoretical chemistry. Through my position on formal models of scientific explanation, I demonstrate how the dawn of AI can provide novel insights into the explanatory power of scientific endeavors.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15