The Future of Material Scientists in an Age of Artificial Intelligence

Abstract

Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.

Perspectives on the future of host-microbe biology from the Council on Microbial Sciences of the American Society for Microbiology

“…By supporting the utilization of AI, ASM can contribute to the refinement and expansion of ’omics studies, ushering in a new era of insight and discovery ( 63 , 64 ). The host-microbe community can learn from other fields that are already using AI as a discovery generation method ( 65 , 66 ), including the possible use of AI for diagnosis ( 67 ) or prognosis ( 68 ) of infectious diseases. Finally, ASM can lead discussions to establish a set of parameters for benchmarking the accuracy of computational models for host-microbe interaction studies and for promoting their continued refinement.…”

Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles

“…Over the past few years, artificial intelligence (AI) and data-driven approaches have been deployed throughout science and industry. − For materials science, AI has emerged as a powerful tool offering innovative solutions to long-standing challenges; for example, machine learning algorithms can analyze large material scientific data sets associated with chemical compositions, crystal structures, material processing details, and properties to accelerate the prediction and optimization of novel materials, leading to advancement of applications including energy storage, catalysis, and nanoelectronics. − In contrast, the traditional scientific paradigms exhibit trial-and-error limitations that may result in diminished predictive accuracy in experimental validation. , With the advent of a big language model − represented by ChatGPT and various BERT/GPT derivatives, natural language processing (NLP) has received increasing attention in the field of materials science. The emergence of such large language models has facilitated the exploration of vast scientific literature resources that are previously underutilized, , helping integrate scientific literature information into daily material research studies and thereby promote prediction of new materials. , Nevertheless, the practical implementation of big language models encounters formidable challenges, arising from substantial demands on computational resources, sheer volume of model parameters, significant time costs associated with development and deployment, and considerable expenses incurred in handling the computational complexities .…”