Abstract:The realization of automated chemical experiments by robots unveiled the prelude of artificial intelligent laboratory. Several AI-based systems or robots with specific chemical skills have been demonstrated, but conducting all-round scientific research remains challenging. Here, we present an all-round AI-Chemist equipped with scientific mind that is capable of performing all essential steps required for chemical research. Based on a service platform, the AI-Chemist is able to automatically read the literature… Show more
“…In another case, the initialization step can also be done by an AI model automatically reading and generalizing the existing human knowledge. 15 Figure 2 The general workflow of an intelligent laboratory. The definition of the system is done manually, whereas the process of experiment planning and conducting is fully autonomous.…”
Section: Fundamentals Of An Intelligent Laboratorymentioning
confidence: 99%
“…It allows integration of existing knowledge and suggests more promising hypotheses for experiments. 15 , 43 …”
Section: Fundamentals Of An Intelligent Laboratorymentioning
confidence: 99%
“…In 2022, Zhu et al 15 developed AI-Chemist, an AI chemistry laboratory that implements the whole process of scientific research autonomously. For an exploration task, a large number of papers were first read and summarized by AI, and scientific hypotheses were generated.…”
Section: Application Scenarios Of An Intelligent Laboratorymentioning
confidence: 99%
“…In another case, the initialization step can also be done by an AI model automatically reading and generalizing the existing human knowledge. 15 The emergence of this research paradigm was not happening overnight, but was gradually transformed. Specifically, an intelligent laboratory often integrates the following components that will be discussed next:…”
Section: Fundamentals Of An Intelligent Laboratorymentioning
The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.
Graphical Abstract
“…In another case, the initialization step can also be done by an AI model automatically reading and generalizing the existing human knowledge. 15 Figure 2 The general workflow of an intelligent laboratory. The definition of the system is done manually, whereas the process of experiment planning and conducting is fully autonomous.…”
Section: Fundamentals Of An Intelligent Laboratorymentioning
confidence: 99%
“…It allows integration of existing knowledge and suggests more promising hypotheses for experiments. 15 , 43 …”
Section: Fundamentals Of An Intelligent Laboratorymentioning
confidence: 99%
“…In 2022, Zhu et al 15 developed AI-Chemist, an AI chemistry laboratory that implements the whole process of scientific research autonomously. For an exploration task, a large number of papers were first read and summarized by AI, and scientific hypotheses were generated.…”
Section: Application Scenarios Of An Intelligent Laboratorymentioning
confidence: 99%
“…In another case, the initialization step can also be done by an AI model automatically reading and generalizing the existing human knowledge. 15 The emergence of this research paradigm was not happening overnight, but was gradually transformed. Specifically, an intelligent laboratory often integrates the following components that will be discussed next:…”
Section: Fundamentals Of An Intelligent Laboratorymentioning
The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.
Graphical Abstract
The new material industry is the foundation of technological change in many related fields, and also the forerunner of the development of new energy, aerospace, electronic information and other high-tech industries. Traditional means cannot meet the development needs of modern society because of disadvantages such as high cost, low efficiency and long commercial cycle. In recent years, with the application of big data combined with artificial intelligence in a deeper degree, data-driven machine learning has made great progress in the design, screening and performance prediction of new materials, which has greatly promoted the development and application of new materials. In this review, the basic process of machine learning, the algorithms commonly used in materials science and the relevant materials database are summarized. This review focuses on the application of machine learning in different functions, as well as the performance prediction in the fields of catalyst materials, lithium-ion batteries, semiconductor materials and alloy materials, presenting the latest progress in materials development. Finally, machine learning in the application of new materials are analyzed and prospected.
Soft matter with diverse functionalities that are easily designable has fascinated tremendous research interests in the past several decades. Nevertheless, the inherent confluence of time and length scale ubiquitous in soft matter immensely complicates the elucidation of the structure–property relationship and thereby severely impedes the function exploration of soft materials. Recently, the emergent machine learning (ML) techniques open new paradigms in property prediction and molecular design of functional materials, due to their extraordinarily distinguished performance in the aspect of trend identity and pattern extraction from data, and objective optimization by accelerating the guided search in high‐dimensional spaces. This review exclusively focuses on the current state‐of‐the‐art progress in the development of ML techniques applied in the realms of soft matter, ranging from coarse‐grained simulations to theoretical prediction on the structural formation and macroscopic properties, as well as the optimization and algorithm‐aided design in experiments. Finally, an outlook on the challenges and opportunities for this rapidly evolving field is discussed.
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