Luckily, some artificial intelligence and machine learning<\/a> (ML) models have grander ambitions than ripping off beloved animators<\/a> and mass-producing essays at an eighth-grade reading level. Take, for instance, a new ML model developed by a team of Emory University scientists. Typically, machine-learning algorithms are used as a tool to help scientists sift through immense amounts of data or optimize experiments, but this particular ML model actually discovered new physics on its own\u2014at least, as it relates to dusty plasma. <\/p>\n Related Story<\/p>\n You\u2019re likely familiar with plasma<\/a>\u2014that fourth state of matter that actually makes up 99.9% of all ordinary matter in the universe<\/a>. Dusty plasma is simply the same mix of ionized gas, but with charged dust particles. This type of plasma can be found throughout both space and terrestrial environments. Wildfires, for example, generate dusty plasmas when charged particles of soot mixed with smoke. In this new study\u2014published in the journal Proceedings of the National Academy of Sciences<\/a> (PNAS)\u2014a team of researchers describes how their trained ML model successfully provided the most detailed description of dusty plasma physics yet, creating precise predictions for non-reciprocal forces.<\/p>\n \u201cOur AI method is not a black box<\/a>: we understand how and why it works,\u201d Justin Burton, a co-author of the study from Emory, said in a press statement<\/a>. \u201cThe framework it provides is also universal. It could potentially be applied to other many-body systems to open new routes to discovery.\u201d<\/p>\n Put simply, non-reciprocal forces (as their name suggests) occur when forces exerted between two particles in a plasma are not the same. The authors describe the phenomenon as two boats impacted by the wake of the other\u2014relative position can impact the particles\u2019<\/a> attractive or repulsive forces. <\/p>\n \u201cIn a dusty plasma, we described how a leading particle attracts the trailing particle, but the trailing particle always repels the leading one,\u201d Ilya Nemenman, another co-author of the study from Emory, said in a press statement. \u201cThis phenomenon was expected by some but now we have a precise approximation for it which didn\u2019t exist previously.\u201d<\/p>\n Related Story<\/p>\n The ML algorithm was also able to correct some theoretical misconceptions about dusty plasma. For example, scientists thought that the charge of the particle was proportional to its size, but the model<\/a> confirms that while a larger particle does contain a larger charge, it isn\u2019t proportional, as it can also be influenced by density and temperature. They also found that the charge between particles isn\u2019t only influenced by the distance between two particles, but also by the particles\u2019 sizes.<\/p>\n