Combining artificial intelligence and robotic system in chemical product/process design

Product design for formulations is an active and challenging area of research. The new challenges of a fast-paced market, products of increasing complexity, and practical translation of sustainability paradigms require re-examination the existing theoretical frameworks to include the advantages from business and research digitalization. This thesis is based on the hypotheses that (i) new products with desired properties can be discovered by using a robotic platform combined with an intelligent optimization algorithm, and (ii) we can the connect data-driven optimisation with physico-chemical knowledge generation, which will result in a suitable model for translation of product discovery to production, thus impacting on the process development steps towards industrial applications. This thesis focuses on two complex physicochemical systems as case studies, namely the oil-in-water shampoo system and sunscreen products. Firstly, I report the coupling of a machine-learning classification algorithm with the Thompson-Sampling Efficient Multi-Optimization (TSEMO) for the simultaneous optimization of continuous and discrete outputs. The methodology was successfully applied to the design of a formulated liquid product of commercial interest for which no physical models are available. Experiments were carried out in a semi-automated fashion using robotic platforms triggered by the machine-learning algorithms. The proposed closed-loop optimization framework allowed to find suitable recipes meeting the customer-defined criteria within 15 working days, outperforming human intuition in the target performance of the formulations. The framework was then extended to co-optimization of both formulation and process conditions and ingredients selection. Secondly, I report the methods for the identification of new physical knowledge in a complex system where a prior knowledge is insufficient. The application of feature engineering methods in sun cream protection prediction was discussed. It was found that the concentration of UVA and UVB filters are key features, together with product viscosity, which match with the experts’ domain knowledge in sun cream product design. It was also found that through the combination of feature engineering and machine learning, high-fidelity model could be constructed. Furthermore, a modified mixed-integer nonlinear programming (MINLP) formulation for symbolic regression method was proposed for identification of physical models from noisy experimental data. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variables.The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions. The work of this thesis shows that machine learning methods, together with automated experimental system, can speed-up the R&D process of formulated product design as well as gain new physical knowledge of the complex systems

Giant Gating Tunability of Optical Refractive Index in Transition Metal Dichalcogenide Monolayers

We report that the refractive index of transition metal dichacolgenide (TMDC) monolayers, such as MoS2, WS2, and WSe2, can be substantially tuned by > 60% in the imaginary part and > 20% in the real part around exciton resonances using CMOS-compatible electrical gating. This giant tunablility is rooted in the dominance of excitonic effects in the refractive index of the monolayers and the strong susceptibility of the excitons to the influence of injected charge carriers. The tunability mainly results from the effects of injected charge carriers to broaden the spectral width of excitonic interband transitions and to facilitate the interconversion of neutral and charged excitons. The other effects of the injected charge carriers, such as renormalizing bandgap and changing exciton binding energy, only play negligible roles. We also demonstrate that the atomically thin monolayers, when combined with photonic structures, can enable the efficiencies of optical absorption (reflection) tuned from 40% (60%) to 80% (20%) due to the giant tunability of refractive index. This work may pave the way towards the development of field-effect photonics in which the optical functionality can be controlled with CMOS circuits

In silico rationalisation of selectivity and reactivity in Pd-catalysed C-H activation reactions.

A computational approach has been developed to automatically generate and analyse the structures of the intermediates of palladium-catalysed carbon-hydrogen (C-H) activation reactions as well as to predict the final products. Implemented as a high-performance computing cluster tool, it has been shown to correctly choose the mechanism and rationalise regioselectivity of chosen examples from open literature reports. The developed methodology is capable of predicting reactivity of various substrates by differentiation between two major mechanisms - proton abstraction and electrophilic aromatic substitution. An attempt has been made to predict new C-H activation reactions. This methodology can also be used for the automated reaction planning, as well as a starting point for microkinetic modelling.EPSRC grant EP/K009494/1 (MK and SL) and the National Research Foundation, Prime Minister’s Office, Singapore under its CREATE programme, project “Cambridge Centre for Carbon Reduction in Chemical Technology” (LC and AL)

A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models

While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations. Challenges arise when these models grapple with understanding multi-hop relations in complex questions or lack the necessary knowledge for a comprehensive response. To address this issue, we introduce the "Decompose-and-Query" framework (D&Q). This framework guides the model to think and utilize external knowledge similar to ReAct, while also restricting its thinking to reliable information, effectively mitigating the risk of hallucinations. Experiments confirm the effectiveness of D&Q: On our ChitChatQA dataset, D&Q does not lose to ChatGPT in 67% of cases; on the HotPotQA question-only setting, D&Q achieved an F1 score of 59.6%. Our code is available at https://github.com/alkaidpku/DQ-ToolQA

Gas Sensitivity of In0.3Ga0.7As Surface QDs Coupled to Multilayer Buried QDs

AbstractA detailed analysis of the electrical response of In0.3Ga0.7As surface quantum dots (SQDs) coupled to 5-layer buried quantum dots (BQDs) is carried out as a function of ethanol and acetone concentration while temperature-dependent photoluminescence (PL) spectra are also analyzed. The coupling structure is grown by solid source molecular beam epitaxy. Carrier transport from BQDs to SQDs is confirmed by the temperature-dependent PL spectra. The importance of the surface states for the sensing application is once more highlighted. The results show that not only the exposure to the target gas but also the illumination affect the electrical response of the coupling sample strongly. In the ethanol atmosphere and under the illumination, the sheet resistance of the coupling structure decays by 50% while it remains nearly constant for the reference structure with only the 5-layer BQDs but not the SQDs. The strong dependence of the electrical response on the gas concentration makes SQDs very suitable for the development of integrated micrometer-sized gas sensor devices