A new study published in the journal Artificial Intelligence & Environment addresses this dual challenge with a multitask deep learning framework that simultaneously predicts how strongly a candidate material adsorbs sulfur gases and how effectively it senses them. The work was carried out on transition-metal phthalocyanines, a versatile family of planar organic molecules whose electronic behavior can be systematically tuned by swapping the metal atom anchored at the center of the macrocycle. By coupling artificial intelligence with density functional theory calculations, the team built a computational pipeline capable of screening gas-material combinations far more rapidly than would be possible through quantum-chemical simulation alone, offering a faster route to multifunctional materials for environmental monitoring and purification.

The researchers constructed their dataset by examining 28 transition-metal phthalocyanine materials against four sulfur-based gases: hydrogen sulfide (H₂S), sulfur monoxide (SO), sulfur dioxide (SO₂) and sulfur trioxide (SO₃). This systematic combination produced 78 distinct gas-material adsorption systems, each characterized through DFT calculations that yield two key quantities. The first is adsorption energy, a thermodynamic measure of how strongly a gas molecule clings to the material surface. The second is sensing response, which captures how much the interaction alters the electronic or conductive properties of the material, and therefore how visible the gas would be to an actual sensor device built from it.

One of the central insights motivating the study is that gas sensing and gas adsorption are closely connected but not governed by exactly the same underlying physics. A material that binds a gas too weakly may fail to capture it; one that binds too strongly may trap the molecule irreversibly, poisoning the sensor and preventing recovery. According to Xiliang Yan, a corresponding author of the study, the goal was to build a model that could learn what the two processes have in common while preserving the information unique to each. Multitask learning is well suited to this kind of problem because it forces the network to internalize shared physicochemical structure across related prediction problems, which often improves generalization when training data are limited.

Architecturally, the framework departs from conventional machine-learning workflows that predict one material property at a time. Instead of training isolated single-task regressors or classifiers, the multitask model learns a common internal representation of the physicochemical features and then routes that representation into separate prediction branches, one dedicated to adsorption energy and one to sensing response. This design allows the model to explicitly evaluate the trade-off at the heart of multifunctional material design: the balance between how tightly a gas binds to a material and how strongly that binding event perturbs the material’s electrical behavior. Candidates can thus be ranked not only for raw performance on either metric but for the practically important combination of both.

The performance gains on an independent test set were substantial. For the classification task, distinguishing materials that produce high versus low sensing responses, the multitask model achieved an accuracy and F1 score of 0.83 along with a recall of 0.88. By comparison, several conventional single-task machine-learning models tested on the same problem reached accuracies of only 0.44 to 0.63, a margin that underscores how much information the shared representation recovers from a small dataset. For the regression task of predicting adsorption energies, the model attained a test-set R² of 0.86, with a root mean square error of 0.46 electron volts and a mean absolute error of 0.35 electron volts. Those error bars are meaningful in the context of DFT-based screening, where candidate ranking depends on relative rather than absolute precision.

Beyond aggregate metrics, the framework pinpointed specific candidate materials with distinct functional profiles. Iron-centered phthalocyanine, abbreviated Fe/Pc, emerged as a strong sensitivity candidate for sulfur monoxide, and Fe/Pc together with chromium-centered Cr/Pc also displayed promising responses to hydrogen sulfide. Other members of the transition-metal phthalocyanine family exhibited particularly strong adsorption of SO and SO₂, suggesting a complementary role as gas-removal materials rather than sensors. Because the metal center is synthetically tunable, these computational assignments translate directly into design guidance: choosing the central atom steers the material toward detection, capture, or a useful blend of the two.

Importantly, the team designed the model to be interpretable rather than treating it as an opaque prediction engine. Feature analysis revealed that the atomic radius and electronic properties of the transition-metal center, the electronic structure of the surrounding phthalocyanine framework, and the intrinsic properties of the gas molecule all contribute measurably to both sensing and adsorption behavior. Yan noted that the model does more than identify promising candidates; it also helps reveal which physical and electronic characteristics control adsorption and sensing, providing guidance for the rational design of new materials. In practice, this means each prediction comes with an explanation, allowing chemists to reason about why a given metal center performs well and to extrapolate those lessons to materials outside the original training set.

The authors are candid about the limitations of the current work. The model was trained on a relatively small dataset, an unavoidable constraint given the computational cost of generating high-quality DFT labels for dozens of gas-material systems. Expanding the framework to additional metals, a broader catalog of gases and more diverse adsorption configurations could substantially improve its ability to screen previously unexplored materials and reduce uncertainty at the edges of chemical space. Still, the study demonstrates a general template: interpretable multitask learning can connect adsorption thermodynamics with gas-sensing behavior in a single computational strategy, pointing toward a new generation of multifunctional materials that both detect and remove hazardous gases before they reach lungs, factories or the atmosphere.

Subject of Research: DFT-driven multitask deep learning for predicting sulfur gas sensing and adsorption by transition-metal phthalocyanines

Article Title: Deep learning helps scientists design materials that can both detect and capture toxic sulfur gases

Article References: Deep learning helps scientists design materials that can both detect and capture toxic sulfur gases. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: deep learning, gas sensing, hydrogen sulfide, sulfur dioxide, phthalocyanines, density functional theory, adsorption energy, multitask learning, environmental monitoring, gas capture, interpretable AI, materials discovery

Researchers Yibo Peng and Caixia Deng, affiliated with Ningbo University and the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences, set out to understand why a so-called senary high-entropy alloy containing platinum, ruthenium, nickel, cobalt, iron, and molybdenum shows tolerance to CO during the alkaline hydrogen oxidation reaction. This reaction is the anode-side half of an anion-exchange membrane fuel cell, a technology prized for its potential to use cheaper, non-precious components than conventional proton-exchange devices. The catch is that hydrogen oxidation proceeds sluggishly in alkaline conditions, and platinum-based anodes are acutely vulnerable to poisoning by even trace amounts of carbon monoxide that latch onto active sites and refuse to let go.

High-entropy alloys have emerged as a compelling answer. Unlike traditional alloys with one dominant metal and minor additives, these materials mix five or more elements in roughly equal proportions, producing a crystalline surface where the identity of every atom’s neighbors is essentially random. That randomness means the surface is not a single, uniform catalyst but a vast landscape of distinct local environments, each potentially binding hydrogen or CO differently. The trouble for theorists is obvious: there are astronomically many such environments, and calculating the adsorption energy of a molecule on each one with conventional density functional theory would be computationally prohibitive.

Peng and Deng tackled this challenge with what they call a compositional ensemble framework, a pipeline that fuses geometric machine-learning descriptors, intelligent sampling, high-throughput quantum calculations, and a state-of-the-art graph neural network. First, they used smooth overlap of atomic positions, or SOAP, descriptors to encode each surface site’s local chemical neighborhood in a form a machine can compare. Then, farthest point sampling allowed them to select a diverse, representative subset of configurations from that enormous space, ensuring the training data spanned the full variety of local environments rather than clustering around a few common motifs.

On that curated training set, the team ran high-throughput density functional theory calculations to obtain accurate adsorption energies, and used the results to train EquiformerV2, an equivariant transformer architecture designed to respect the rotational and translational symmetries of three-dimensional atomic systems. The payoff was striking: the trained model predicts CO adsorption energies with a mean absolute error of just 0.090 electron volts. With that level of accuracy in hand, the researchers could do something previously impractical, namely statistically evaluate CO adsorption across 120,000 distinct surface sites of the PtRuNiCoFeMo alloy, building a distribution rather than a handful of anecdotal data points.

The results reveal a subtle and somewhat counterintuitive picture. Compared with the flat platinum (111) surface, the canonical benchmark in this field, the high-entropy alloy does not uniformly weaken CO adsorption across its surface. Instead, the distribution of CO binding energies becomes continuously broadened, stretching from sites that bind CO far more weakly than platinum to sites that grip it even more tightly. The overall CO adsorption distribution remains dominated by intermediate-to-strong binding configurations, particularly at bridge and hollow geometries where the molecule can bond to multiple surface atoms simultaneously. In other words, the average alloy surface is, by and large, still a welcoming host for CO.

Yet buried within that distribution lies the alloy’s real advantage. When the researchers performed a comparative two-dimensional analysis of hydrogen and CO adsorption energies, screening each site against the platinum-referenced criterion of accessible hydrogen binding combined with relatively weakened CO binding, they identified 9,479 sites that met both conditions. That corresponds to roughly 7.9 percent of the examined ensemble. These sites, the authors emphasize, should be interpreted as a minority but statistically resolvable reservoir of local motifs where hydrogen chemistry and CO poisoning are effectively decoupled, rather than as evidence that the entire alloy surface outperforms platinum in CO tolerance. It is a reservoir effect: the catalyst as a whole retains enough clean, hydrogen-friendly real estate to keep working even as other regions succumb to adsorbed CO.

Perhaps the most actionable finding concerns what those favorable sites look like at the atomic scale. The team found that the H/CO-favorable motifs are mainly associated with platinum atoms sitting at top-site positions whose nearest-neighbor shells are enriched in molybdenum and platinum. This suggests that nearby molybdenum-platinum coordination is a prominent local environment for balancing hydrogen accessibility against reduced CO affinity. The result dovetails with decades of experimental observations that molybdenum-containing platinum catalysts, from early PtMo alloys to modern MoOx-Pt composites, exhibit exceptional CO tolerance, often attributed to molybdenum’s oxophilicity and its electronic modifying influence on adjacent platinum atoms. The new work reframes that intuition in statistical terms, pinpointing the specific coordination motif worth engineering.

The methodological significance of the study may prove as durable as its catalytic insights. By demonstrating that SOAP descriptors, farthest point sampling, DFT training data, and an equivariant transformer can be chained into a reliable surrogate model for adsorption energies on chemically disordered surfaces, the authors offer the electrocatalysis community a blueprint for interrogating other high-entropy systems, from oxygen reduction catalysts to CO2 conversion electrodes. The statistical framing itself is a corrective: rather than asking whether a high-entropy alloy binds a poison more weakly than a pure metal on average, designers should ask how large the subpopulation of protective local motifs is, and whether synthetic strategies can enlarge it.

For the fuel cell industry, the implications are tantalizing though still computational. Anion-exchange membrane fuel cells need anodes that combine fast alkaline hydrogen oxidation kinetics with robustness against fuel impurities, and a surface in which nearly eight percent of sites are naturally H/CO-decoupled represents a meaningful margin of tolerance. The study also suggests a concrete design lever: tuning synthesis and annealing conditions to promote molybdenum-enriched neighborhoods around surface platinum atoms could, in principle, expand the favorable reservoir further. As hydrogen energy infrastructure scales up globally, turning statistical portraits of disorder like this one into practical catalyst recipes may become one of the field’s central pursuits, bridging the gap between atomic-scale randomness and real-world device durability.

Subject of Research: Statistical machine-learning analysis of CO tolerance in a PtRuNiCoFeMo high-entropy alloy catalyst for alkaline hydrogen oxidation in fuel cells

Article Title: Statistical origin of CO tolerance during alkaline hydrogen oxidation on a PtRuNiCoFeMo high-entropy alloy

Article References: Peng, Y., & Deng, C. (2026). Statistical origin of CO tolerance during alkaline hydrogen oxidation on a PtRuNiCoFeMo high-entropy alloy. Ionics. https://doi.org/10.1007/s11581-026-07511-1

Image Credits: AI Generated

DOI: 10.1007/s11581-026-07511-1

Keywords: high-entropy alloy, CO tolerance, hydrogen oxidation reaction, anion-exchange membrane fuel cell, PtRuNiCoFeMo, adsorption energy, density functional theory, machine learning, graph neural network, EquiformerV2, electrocatalysis, molybdenum-platinum coordination