The fields within chemistry as well as materials science are seeing an increase in interest in self-driving labs that go on make use of artificial intelligence- AI as well as automated systems to speed up research and discovery. Researchers happen to be now looking to propose a suite of definitions and performance metrics that will enable themselves, non-experts, as well as future users to better gauge what such new technologies happen to be doing in addition to how each technology would perform vis-Ã -vis other self-driving labs. Their inferences have gone on to be published in Nature Communications.
Self-driving labs hold a tremendous promise when it comes to accelerating the exploration of new molecules, materials, as well as manufacturing processes, by way of applications that range from electronic devices to pharmaceuticals. While technologies happen to be fairly new, some have gone on to show that they lessen the time required so as to identify fresh materials from months or years to even days.
Self-driving labs happen to be garnering immense attention as of now, however, there are immense outstanding questions with regards to these technologies, remarked the corresponding author of a paper on the new metrics and also an associate professor of chemical and biomolecular engineering at North Carolina State University, Milad Abolhasani. He adds that this technology is described as autonomous; however, different research teams happen to be defining autonomous in a varied way. By the same token, varied research teams are reporting distinct aspects of their work differently. This makes it a challenge to go ahead and even compare such technologies to each other, and the comparison is even more important if one wants to be able to comprehend each other and even push the field forward.
According to Milad Abolhasani, what does Self-Driving Lab A go ahead and do really well? How could one use that so as to improve the Self-Driving Lab B’s performance? They happen to be proposing a set of shared definitions along with performance metrics, which they hope will be embraced by everyone working within this space. The final objective will be to enable all to learn from each other as well as even advance such powerful research acceleration technologies.
For instance, one seems to be seeing some issues when it comes to self-driving labs that happen to be related to the performance, precision, as well as robustness of certain autonomous systems, says Abolhasani. This goes on to raise questions pertaining to how useful such technologies can be. If one happens to have standardized metrics as well as the reporting of results, one can identify such challenges and even better gauge how to address them.
At the core of the new proposal happens to be a clear definition of self-driving labs and seven proposed performance metrics, that researchers would go on to include in any published work that’s related to their self-driving labs.
• Degree when it comes to autonomy: how much guidance a system needs from users?
• Operational lifetime: how long can the system function without any intervention from users?
• Throughput: how long it takes the system to run one-single experiment?
• Experimental precision: how reproducible happen to be the results of the system?
• Material use: what is the total amount of materials that happen to be used by a system pertaining to each experiment?
• Parameter space that is accessible: to what extent would the system go on to account for all the variables across each experiment?
• Efficiency concerning optimization
Optimization efficiency happens to be one of the most significant of these metrics, but it is also one of the most intricate as it doesn’t lend itself to an exact definition, says Abolhasani. Essentially, one just wants the researchers to quantitatively assess the performance of the self-driving lab and its experiment-selection algorithm by setting standards against a baseline for instance, through random sampling.
At the end of the day, one thinks having a standardized approach in order to report on self-driving labs will enable to make sure that this field happens to be coming up with trustworthy, reproducible results that go on to make the most of AI programs that go ahead and capitalize on the large, high-quality sets of data that get produced by self-driving labs, says Abolhasani.
