On scientific understanding with artificial intelligence

Abstract

An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of ‘scientific understanding’ from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.

Graph neural networks for materials science and chemistry

“…Finally, more research on the explainability and interpretability of GNNs and machine learning in general will help to better understand underlying correlations and eventually causal relations in large and complex datasets, eventually contributing to scientific understanding and progress [266][267][268] .…”

Formalizing Chemical Theory using the Lean Theorem Prover

“…Machine learning tools designed for text prediction [56,57] can be fine-tuned to "auto-complete" mathematical proofs, given a formal problem statement [34,58], even to the point of generating correct solutions to International Math Olympiad problems [59]. Recently, large language models have demonstrated capabilities in solving chemistry problems [60,61], as well as answering scientific question-and-answer problems invoking quantitative reasoning [62] Formal proofs in science and engineering mathematics may in the future provide useful, high-quality data for artificial intelligences aiming to learn, reason, and discover in science [63,64,65].…”

Machine-Learning Based Prediction of Kinetic Rate Coefficients in Radical Polymerization

“…As such, the tool we describe address pure scientific discovery, as outlined in a recent perspective. 9 In this approach, a deeper understanding for the reason of correlation of parameters is per se not required. However, once correlations are established, this information can be used to infer new scaling laws, eventually leading to a better physical understanding.…”

“…Part of this issue might be that in the classical approach, physical chemists look for scientific understanding rather than for a correlation or pure association. 9 In complex interdependent systems, this can be a difficult endeavour since accurate data is often difficult to find, and actual causations might not be obvious. Association is much simpler to establish though via purely statistical approaches.…”

“…Machine-learning (ML) harnesses this relative simplicity to predict complex behaviour of systems. 9 Hence, the question could be raised if it is possible to correlate complex propagation rate coefficients with fundamental and readily available information of monomers without the attempt to establish exact equations reflecting the underpinning processes. If such an approach was successful the resulting correlation can ideally be used to reach a better theoretical understanding.…”