Role of artificial intelligence in chemistry

Choudhary, Neeru; Bharti, Ruchi; Sharma, Renu

doi:10.1016/j.matpr.2021.09.428

Cited by 14 publications

(7 citation statements)

References 27 publications

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“…Using chatbot's artificial intelligence chemistry scenario fosters students' conceptual understanding of the atomic structure and chemical bonding (Giam en Thanh, 2022). The use of AI stimulates students' conceptual understanding of chemical bonding by predicting the molecular structure of substances (Choudhary, Bharti en Sharma, 2022).…”

Section: Discussionmentioning

confidence: 99%

Exploring Secondary School Students’ Interest and Mastery of Atomic Structure and Chemical Bonding through ChatGPT

Iyamuremye,

Ndihokubwayo

2024

EJAIML

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This research explored the impact of using ChatGPT, a chatbot-assisted artificial intelligence (AI), on students’ interest and conceptual understanding of atomic structure and chemical bonding. A total of 92 senior and two secondary students, including 61 male and 31 female students from Rwanda, were randomly selected to participate in the study. The study used a sequential explanatory research design to collect and analyze data. A pre and post-achievement test was used to collect quantitative before and after using ChatGPT. On the other hand, focus group discussion was used to collect qualitative data to explore students’ interest in using chatbots. Repeated measure ANOVA was used to analyze quantitative data from achievement tests, while thematic analysis was used to analyze qualitative data from focus group discussions. The results revealed that the generated response from the use of chatbot-AI is an effective method to supplement other active methods of teaching and learning atomic structure and chemical bonding. In this regard, the students’ performance increased by 16.6% after the use of chatbot-AI. However, the study did not find a statistically significant difference between male and female students after using ChatGPT. Thematic analysis revealed three overarching themes. In this line, the use of ChatGPT stimulates students’ engagement and interactivity, curiosity and motivation, and enhanced learning experience. Educators and policymakers were recommended to integrate this technology into the curriculum to supplement other active teaching and learning pedagogies.

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Section: Discussionmentioning

confidence: 99%

Exploring Secondary School Students’ Interest and Mastery of Atomic Structure and Chemical Bonding through ChatGPT

Iyamuremye,

Ndihokubwayo

2024

EJAIML

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“…In 1956, John McCarthy coined the term “artificial intelligence” (AI), marking the inception of a computer science subfield centered on machine learning (ML) and an aspiration to emulate human intelligence . AI-driven algorithms have since permeated diverse disciplines, , with notable advancements in sectors such as image understanding, pattern recognition, autonomous driving, automatic programming, big data, , robotics, and human-machine collaboration .…”

Section: Introductionmentioning

confidence: 99%

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

Zhang,

Yao,

Zhao

et al. 2024

Ind. Eng. Chem. Res.

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Wastewater treatment, especially the efficient degradation of contaminants such as m-cresol, remains a pivotal challenge. This study investigates the application of artificial neural networks (ANN) in predicting total organic carbon (TOC) removal rates from m-cresol-contaminated wastewater by using the ultraviolet (UV)-Fenton oxidation process. Six key variables, namely, Fe 2+ dosage, H 2 O 2 dosage, catalyst quantity, reaction time, pH, and substrate concentration, were employed as inputs to the ANN model. Leveraging this multivariable input and a comprehensive data set, the ANN model projected a maximum TOC removal rate of 87.12%, validated by an efficiency of 86.26% achieved through experiments under the derived optimal conditions: Fe 2+ dosage at 16.09 mg/L, H 2 O 2 dosage at 1.40 mg/L, catalyst quantity at 0.11 g/L, reaction time of 29.80 min, initial pH of 3.66, and substrate concentration of 50 mg/L. Comparative analysis with other machine learning algorithms further revealed that the ANN model notably outperformed linear regression, support vector regression, and random forest in terms of precision. This work paves the way for resource-optimized experimental designs, fostering real-time wastewater monitoring and refining advanced oxidation process proficiency in industrial applications.

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“…Research and development of numerous scientific disciplines, including Chemistry, , have recently undergone a major paradigm shift, motivated by the emerging application of Artificial Intelligence (AI) tools . In the particular case of chemical sciences, different AI fields, such as Machine Learning (ML) or Deep Learning (DL), now play an absolutely pivotal role owing to their ability to efficiently solve complex problems in feasible time scales.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors

Gallegos,

Isamura,

Popelier

et al. 2024

J. Chem. Inf. Model.

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Condensing the many physical variables defining a chemical system into a fixed-size array poses a significant challenge in the development of chemical Machine Learning (ML). Atom Centered Symmetry Functions (ACSFs) offer an intuitive featurization approach by means of a tedious and labor-intensive selection of tunable parameters. In this work, we implement an unsupervised ML strategy relying on a Gaussian Mixture Model (GMM) to automatically optimize the ACSF parameters. GMMs effortlessly decompose the vastness of the chemical and conformational spaces into well-defined radial and angular clusters, which are then used to build tailor-made ACSFs. The unsupervised exploration of the space has demonstrated general applicability across a diverse range of systems, spanning from various unimolecular landscapes to heterogeneous databases. The impact of the sampling technique and temperature on space exploration is also addressed, highlighting the particularly advantageous role of hightemperature Molecular Dynamics (MD) simulations. The reliability of the resulting features is assessed through the estimation of the atomic charges of a prototypical capped amino acid and a heterogeneous collection of CHON molecules. The automatically constructed ACSFs serve as high-quality descriptors, consistently yielding typical prediction errors below 0.010 electrons bound for the reported atomic charges. Altering the spatial distribution of the functions with respect to the cluster highlights the critical role of symmetry rupture in achieving significantly improved features. More specifically, using two separate functions to describe the lower and upper tails of the cluster results in the best performing models with errors as low as 0.006 electrons. Finally, the effectiveness of finely tuned features was checked across different architectures, unveiling the superior performance of Gaussian Process (GP) models over Feed Forward Neural Networks (FFNNs), particularly in low-data regimes, with nearly a 2-fold increase in prediction quality. Altogether, this approach paves the way toward an easier construction of local chemical descriptors, while providing valuable insights into how radial and angular spaces should be mapped. Finally, this work opens the possibility of encoding many-body information beyond angular terms into upcoming ML features.

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Role of artificial intelligence in chemistry

Cited by 14 publications

References 27 publications

Exploring Secondary School Students’ Interest and Mastery of Atomic Structure and Chemical Bonding through ChatGPT

Exploring Secondary School Students’ Interest and Mastery of Atomic Structure and Chemical Bonding through ChatGPT

Optimizing the UV-Fenton Degradation of m-Cresol Wastewater: An Experimental and Artificial Intelligence Modeling Approach

An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors

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