{"id":826674,"date":"2026-07-27T09:44:14","date_gmt":"2026-07-27T09:44:14","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/826674\/"},"modified":"2026-07-27T09:44:14","modified_gmt":"2026-07-27T09:44:14","slug":"ai-in-analytical-chemistry-machine-learning-generative-ai-and-the-future-of-chemical-analysis","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/826674\/","title":{"rendered":"AI in analytical chemistry: machine learning, generative AI and the future of chemical analysis"},"content":{"rendered":"

Article summary<\/p>\n


\nAI is already deeply embedded in analytical chemistry, particularly through machine learning and chemometrics, where it helps scientists analyse large datasets, identify patterns and improve the speed and rigour of spectroscopic and other analytical techniques.
\nGenerative AI is expanding the field\u2019s capabilities, enabling researchers to create synthetic spectra, predict molecular structures and explore new chemical hypotheses, although experts stress that the technology is still developing and has not yet reached its full practical potential.
\nResearchers caution against overreliance on AI outputs, noting that both chatbots and scientific AI models can produce convincing but incorrect results or \u2018hallucinations\u2019, making validation against established physical and chemical principles essential.
\nAI-powered analytical tools are finding real-world applications in areas such as forensic science, cancer diagnostics and scientific translation, while experts generally believe AI will augment rather than replace analytical chemists by shifting their role towards interpretation, quality control and oversight. <\/p>\n

This summary was generated by AI and checked by a human editor<\/p>\n

If you were to ask people to name the branch of science or technology that they expect to impact their lives most in the near future, for good or ill, there\u2019s a very good chance that they will choose artificial intelligence. 2024\u2019s Nobel prize bonanza for AI pioneers \u2013 the chemistry prize for protein structure prediction with AlphaFold, and the physics one for the \u2018foundational discoveries that led to artificial neural networks\u2019 \u2013 occurred a bare two years after the chatbot ChatGPT burst onto the scene, threatening to upend higher education and much else besides. Like all professionals, chemists are rapidly coming to terms with living and working in what is fast becoming an AI world. This is perhaps particularly true for analytical chemists, whose work revolves around identifying and quantifying the composition of substances.<\/p>\n

If you then ask someone what AI means to them, many would describe ChatGPT or a similar chatbot. This, however, is only one small part of the technology. AI is defined more generally as \u2018the capacity of a computer to perform activities that are normally associated with human reasoning\u2019 \u2013 learning, problem-solving, perception, decision-making and the like \u2013 and in that general sense it has been around almost as long as computers themselves.<\/p>\n

And this is not even the first period that has seen explosive growth in the apparent potential of AI; rather, it is the third, and the first two didn\u2019t last. Business analysts use a framework known as the Gartner hype cycle to describe how novel technologies can move from a \u2018peak of inflated expectations\u2019 through the inevitable \u2018trough of disillusionment\u2019 to a final \u2018plateau of productivity\u2019. The first peak in expectations for AI came in the 1950s with the first, very simple neural networks; Isaac Asimov\u2019s short stories about humanoid robots, I, Robot, were published together in 1950. The second coincided with the growth of machine learning techniques in the 1980s, and the third, of course, with generative AI and chatbots today.<\/p>\n

AI\u2019s long road in chemistry<\/p>\n

So, what will the current technology wave look like in its plateau of productivity? Rasmus Bro, who researches machine learning in analytical chemistry at the University of Copenhagen in Denmark, stresses that for him, the plateau is still some way off. \u2018We routinely analyse samples using machine learning techniques that were introduced in the 1980s, and that is all we need,\u2019 he explains. \u2018Generative methodologies will not necessarily help us do what we do much better, but they will broaden the kind of problems that we can solve \u2026 we think it will be a revolutionary change, not an incremental one when it comes, but we\u2019re not there yet,\u2019<\/p>\n

Long before generative AI emerged onto the scene, established forms of AI, particularly machine learning, had revolutionised how scientists approach data analysis. The power of machine learning to classify and discern patterns in large datasets makes analysis simultaneously more rigorous and less time-consuming. Any chemist who uses large quantities of data \u2013 which means almost any chemist today, including many students \u2013 will be using machine learning, whether they realise it or not. In particular, machine learning aids Bro\u2019s discipline of chemometrics, defined as \u2018the science of extracting information from chemical data using statistical and mathematical methods\u2019, and reinforces the value of the spectroscopies and other analytical techniques that provide that data.<\/p>\n

<\/p>\n

We should beware in particular of a \u201dbeautifully correct\u201d answer from AI<\/p>\n

<\/p>\n

Generative AI is, essentially, a particular type of machine learning. The difference between it and other types lies in that word \u2018generative\u2019. Unlike traditional machine learning algorithms, generative algorithms can generate something entirely novel from analysing the patterns stored in their vast datasets. Any generative model is based on a probabilistic data model and can generate new examples based on that probability. Jerome Workman Jr., a former instrument and software development scientist from California, US, says generative models will \u2018augment, simulate, and better characterise spectral data\u2019. This provides the logical link between scientific uses of generative AI and the ubiquitous chatbots: in ChatGPT, for instance, text-based large language models \u2013 in some cases derived from \u2018the whole [accessible] Internet\u2019 \u2013 are equivalent to repositories of scientific data.<\/p>\n

However, using machine learning to generate original content from a prompt or request is neither (quite) as new or as strange as has been suggested. Farooq Wahab, an analytical chemist and research engineering scientist at the University of Texas at Arlington in the US, has tracked down what may be its first mention, from over 30 years ago. \u2018To the best of my knowledge, the first use of the term \u201cgenerative AI\u201d with something like its current meaning was in a talk by a British-American forensic software analyst, Andy Johnson Laird, in a conference paper about AI and intellectual property in 1991,\u2019 he says.<\/p>\n

Wahab takes a firmly critical attitude to the use of generative AI in his own field, explaining his approach using a \u2018clever Hans\u2019 analogy. Clever Hans was a horse that appeared to do simple sums during exhibitions in early 20th century Germany. Later, a psychologist, Oskar Pfungst, showed that Hans was, in fact, responding to involuntary cues from his owner. By analogy, artificial intelligence programs \u2013 including generative ones \u2013 will sometimes give a plausible answer through flawed logic. \u2018We should beware in particular of a \u201dbeautifully correct\u201d answer from AI, because it may still be based on incorrect reasoning,\u2019 he adds.<\/p>\n

Spectroscopy and AI at a crossroads<\/p>\n

In very general terms, a typical experiment in analytical spectroscopy involves passing a beam of radiation through a sample, recording the amount absorbed at each wavelength and generating a spectrum for analysis and interpretation. The mathematical techniques used for interpreting the spectra form an important part of the discipline of chemometrics. At the very simplest level, this might just involve calibrating an instrument by simple regression, but more complex levels of spectral interpretation require machine learning techniques for descriptive and predictive analysis.<\/p>\n

A recent feature in Spectroscopy magazine<\/a> described spectroscopy as \u2018at a crossroads\u2019. The authors list three unrelated trends as contributing to the challenges facing spectroscopists: artificial intelligence (not necessarily limited to generative AI) \u2013 is one, of course, along with automation and miniaturisation. The trend towards using spectroscopy tools outside large-scale laboratory facilities \u2013 in the clinic, perhaps, or in the field for forensic applications \u2013 is a key factor driving miniaturisation.<\/p>\n

<\/p>\n

ChatGPT is still not as good at chemistry as it is at maths<\/p>\n

<\/p>\n

Workman believes that generative AI became particularly compelling for spectroscopists when it was able to offer the possibility of mapping data space itself, not just mapping inputs (the spectroscopic data) to outputs (the predicted analyte concentrations or values). \u2018These [AI-generated] maps or models become important whenever it is difficult, expensive, time-consuming or perhaps impossible to obtain representative samples for calibration,\u2019 he adds. \u2018Importantly, we are able to generate physically plausible synthetic spectra using these techniques.\u2019<\/p>\n

All these techniques still have limitations, however. As many lecturers know, students who use large language models to help with their assignments are often tripped up by incorrect or \u2018hallucinatory\u2019 references: references that even experts in a field will find entirely plausible at first glance but that just don\u2019t exist. \u2018AI-generated spectra, too, may be \u201challucinatory\u201d; that is, plausible but incorrect, particularly if the algorithms have been poorly trained or are used outside the domain they were trained on,\u2019 adds Workman. \u2018But rigorous validation and cross-checking with the physical and chemical principles involved can provide safeguards.\u2019<\/p>\n

And \u2018chatbots\u2019 \u2013 basic text-based generative AI tools \u2013 can also prove helpful in spectroscopic analysis. Wahab has used ChatGPT as a coding assistant and found that it started off as an unreliable one but significantly improved. \u2018ChatGPT is still not as good at chemistry as it is at maths,\u2019 he explains. \u2018I wouldn\u2019t recommend it to a chemist for help with chemistry, but it can be a very useful PhD-level mathematical assistant for chemists without maths backgrounds. Or you could train it [in chemistry] as Omar Yaghi is doing.\u2019 Yaghi, who won a share of the 2025 Nobel prize in chemistry for his work developing flexible and stable metal\u2013organic frameworks, is a \u2018power user\u2019 of generative AI<\/a>.<\/p>\n

Promise, pitfalls and hallucinations<\/p>\n

Without strict ethical safeguards, training chatbots with large bodies of data will come at a price. The AI company Anthropic \u2018skimmed most of the world\u2019s knowledge\u2019 from the Internet to train its widely used chatbot, Claude; an enormous number of in-copyright books were accessed without permission for this, including some of Workman\u2019s spectroscopy textbooks. \u2018Five of my books are included in the list of works for a class action lawsuit suing the company for improper use,\u2019 he explains. \u2018This is such a large suit that each member could only get a small sum, but it\u2019s not the money but the principle that\u2019s important here.\u2019<\/p>\n

\"Schematic<\/p>\n

Xiangliang Zhang and Kehan Guo are computer scientists who work closely with NMR spectroscopists and other chemist colleagues at the University of Notre Dame in the US state of Indiana to formulate spectroscopy-related problems<\/a> that can be studied using machine learning and generative AI. One direction is to predict spectra from molecular structures; another, the more challenging inverse problem, is to generate candidate molecular structures that are consistent with a given spectrum.<\/p>\n

Generative models are useful here because they can rapidly explore a much larger space of possible molecular candidates than would be practical through manual reasoning alone, giving chemists and spectroscopists a broader set of hypotheses to evaluate. These candidates may be represented as Smiles strings and compared with chemical databases such as PubChem<\/a> as one part of the validation process, although the absence of an exact database match does not by itself establish novelty, synthesisability or chemical value. The goal is not to replace chemical expertise, but to help chemists focus their effort on assessing which AI-generated candidates are chemically valid, spectroscopically plausible, and experimentally meaningful.<\/p>\n

AI beyond the laboratory<\/p>\n

Any technology that can separate and characterise complex mixtures of compounds may find uses in forensic science, diagnostics and food safety, for example. Combining the basic techniques with AI can add accuracy, reliability, speed and rigour to already well-established methodologies. In some ways, this technology mimics a well-studied biological system: olfaction, our sense of smell<\/a>. Mammals have evolved, and in the case of dogs near-perfected, a system for distinguishing between substances in the atmosphere from their molecular profile. Volatile organic compounds (VOCs) passing into mammalian noses bind to specific olfactory receptors and generate signals that are recognised as odours. A human nose is theoretically able to recognise about 10,000 different ones, and dogs, with 40 times as many olfactory receptors, can distinguish millions.<\/p>\n

\"Close<\/p>\n

Donatella Puglisi, a physicist based at Linkoping University in Sweden, and her group have developed an artificial olfactory system<\/a>, called an \u2018e-Nose\u2019, in which volatiles bind to a sensor array, generating signals that machine learning algorithms can classify quickly, precisely and from small samples. The e-Nose comprises an array of 32 different gas sensors held consistently at four different temperatures. Electrical signals generated when gas containing VOCs passes over the array are transferred to an advanced machine learning system. \u2018The artificial intelligence in the e-Nose is trained to distinguish one pattern of signals from another, rather as our brains are trained in early childhood to distinguish the smell of cheese from that of strawberry jam,\u2019 explains Puglisi.<\/p>\n

\"Scheme<\/p>\n

The sensitivity and precision of the e-Nose should be of particular value during murder investigations. Detectives need to be able to distinguish between\u00a0tissue from living and dead individuals<\/a> and between human and animal remains, and to estimate the time of death. This system is not yet available for use in the field, but it should offer a significant improvement over two currently used methods. The most appropriate standard analytical technique is gas chromatography\u2013mass spectrometry (GC\u2013MS), but this equipment is too fragile to use at a scene of crime, and the analyses are time-consuming; specially trained dogs have had major successes, but their results cannot be used as physical evidence in court.<\/p>\n

\"Detailed<\/p>\n

Beyond forensic science, the e-Nose is being tested in oncology<\/a>, to distinguish between blood plasma from ovarian cancer patients and healthy controls. Puglisi is collaborating with her colleague, Jens Eriksson, and a company, VOC Diagnostics, to produce a version for clinical use. \u2018We aim to produce a machine that can screen for ovarian cancer in minutes using a simple blood sample, with more accuracy than any other technique,\u2019 she says. The company focuses on ovarian cancer primarily because its founder, Gy\u00f6rgy Horvath, is a gynaecologist who had specialised in this disease for decades, but the same principles could apply to any cancer type. \u2018We would like to build a multi-cancer sensor, but first we need to make sure that different types of cancer \u201csmell\u201d differently,\u2019 she adds.<\/p>\n

Will AI replace analytical chemists?<\/p>\n

Not every application of AI in general, and of generative AI in particular, that benefits chemists is specific to chemistry. It has already proved very useful for translating the scientific literature. English has only been the official language of science for a few decades, and only 40 years ago many chemistry students were required to know some German. Much valuable knowledge is hidden in papers written in other languages, and human translation is time-consuming and expensive. Google Translate and other standard translation software is useful but limited, particularly by an inability to handle mathematical symbolism. \u2018Generative AI can do an excellent job of translating all types of chemistry papers \u2026 if the equations are provided in LaTeX format,\u2019 says Wahab. This type of AI translation is making information in in the old multivolume Beilstein or Gmelin Handbooks of Organic Chemistry, or German quantum chemistry papers, for example, accessible to further generations of chemists.<\/p>\n

But while AI in all forms is coming to play an ever-increasing role in the work of analytical chemists, one big question remains: will the discipline cease to be AI-enabled, and become AI-dominated? In fact, will human analytical chemists still be needed? Perhaps despite his involvement in the class action, Workman is an optimist on the jobs question. He suggests that the answer will be \u2018more, not less\u2019. \u2018The more routine work will be automated,\u2019 he explains. \u2018But the analytical chemist\u2019s role will switch to quality control and interpretation; demand will grow for scientists who understand chemometrics and can work with AI.\u2019 If he is right, it seems that a bright future awaits at least some analytical chemists \u2013 and their robot collaborators.<\/p>\n

Clare Sansom is a science writer based in Cambridge, UK<\/p>\n","protected":false},"excerpt":{"rendered":"Article summary AI is already deeply embedded in analytical chemistry, particularly through machine learning and chemometrics, where…\n","protected":false},"author":2,"featured_media":826675,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[64,63,128],"class_list":["post-826674","post","type-post","status-publish","format-standard","has-post-thumbnail","category-science","tag-au","tag-australia","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/826674","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/comments?post=826674"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/826674\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media\/826675"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media?parent=826674"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/categories?post=826674"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/tags?post=826674"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}