Artificial Intelligence-Driven Design of Ternary Alloy Electrodeposits: Deciphering the Interplay Between Plating Mechanisms, Process Parameters, and Coating Properties

In binary alloy electrodeposition from aqueous solutions, three fundamental deposition modes are universally recognized: normal, anomalous, and induced regimes. These describe how the relative deposition rates of two metals (A and B) compare with their relative concentrations and inherent nobility (standard reduction potentials). In anomalous co-deposition, the less noble metal deposits preferentially, contrary to what reduction potentials predict. In induced co-deposition, one metal cannot deposit at all under the given conditions unless the other metal is present. Atypical co-deposition regimes present a major obstacle to the synthesis of multicomponent alloys with tailored properties, as current data and tools are insufficient for precise composition design. Electrodeposition mechanisms are rather complex, and the effect of each process parameter on the final composition depends on how it interacts with the others. This interdependence highlights the need for design strategies that explicitly account for these correlations to avoid the so-called trial-and-error in the lab, hence saving time and resources. Industrially relevant ternary alloy candidates will be selected for study from both experimental and computational perspectives. Additionally, the feasibility of predicting the phase diagram for previously unreported ternary alloys will be investigated.

To address these challenges, a combination of artificial intelligence (AI) techniques will be employed to uncover complex, non-linear relationships between plating parameters, deposition mechanisms, and resulting alloy compositions. Initially, supervised learning models such as random forests, gradient boosting, and neural networks will be used to predict composition outcomes based on both literature scrapping and in-house laboratory experiments for data augmentation. To explore previously untested conditions and enable inverse design, generative models such as variational autoencoders or transformers will be applied to propose new sets of plating parameters that are likely to produce target compositions or properties. Additionally, feature importance and sensitivity analyses will be conducted to quantify the interplay between process parameters and deposition mechanisms for suggesting plating conditions that simultaneously optimize composition accuracy, microstructural uniformity, and functional properties. This AI-guided approach will create a closed-loop design workflow, enabling predictive, data-driven exploration of ternary alloy electrodeposition beyond conventional trial-and-error experimentation.

Candidates should have:

• Bachelor / Master in Nanoscience & Nanotechnology, Materials Science, Physics or related fields; extra coursework in data science or artificial intelligence is a plus.
• Analytical thinking & problem-solving, ability to interpret experimental data critically.
• Scientific writing and communication skills in English.
• Teamwork and interdisciplinary mindset (materials science, physical chemistry, energy research).
• Knowledge of Physical Chemistry is desirable.
• Knowledge in Python programming language or PyTorch / Tensorflow certification.
• Preliminary exposure to machine/deep learning, statistical modelling or generative AI.

The AI Lab, part of HP’s renowned Technology and Innovation Office, leads strategic research and development in Generative AI to bring state-of-the-art capabilities into HP’s new form factors portfolio. With teams located in Barcelona (Spain), Palo Alto (USA), and Bangalore (India), the Barcelona group focuses on operationalizing the full lifecycle of Generative AI models and developing energy efficient on device solutions that address top user needs while enabling new, transformative user experiences. Our research spans natural language processing, computer vision, and voice-based AI systems, with a strong emphasis on model fine-tuning, quantization (compression / distillation), and evaluation of large language, vision, and audio models.

The Gnm3 group focuses part of its research on the design, synthesis, and characterization of advanced materials with tailored properties for cutting-edge engineering applications. We use electrochemical methods to produce advanced material architectures with high surface-area-to-volume-ratios and compositions with reduced amounts of noble metals. These materials are tested as electrocatalysts for the hydrogen evolution reaction (HER) and, more recently, implemented in proton exchange membrane fuel cells. Sustainability and energy efficiency are guiding principles of our work, shaping both the development of materials and their envisioned applications.