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Source 5:  ChatGPT,  The transformative influence of generative AI on science and healthcare - Journal of Hepatology (Varghese, et. all, 2023) 

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Source 5:  ChatGPT,  The transformative influence of generative AI on science and healthcare - Journal of Hepatology (Varghese, et. all, 2023) 

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In processes as complicated as creating a research paper, generative AI can be helpful in replicating research results and focusing less on tedious tasks such as the verbiage of the publication.

Highlights that LLMs can be used in research writing as it is able to reduce the time taken on tasks such as formatting, making the research more easily replicable and allows the researcher to take more time on producing the best results possible in comparison to focusing on the semantics in the writing.

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How can a generative artificial intelligence model be leveraged to create synthetic lab data for an UV-Vis Spectroscopy experiment?

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AI in STEM related fields is the fastest growing facet in technology

--> 23% increase by 2030 in AI related careers in STEM (McKinley 2023)

AI is a way of processing information that depends on large datasets (Wen et al 2022). AI models are able to take in massive amounts of data to make predictions or create results, something chemical experimentation would highly benefit from

GANS are generative adversarial networks, a type of machine learning that both creates and tests synthetic data creation to make the most accurate results (Feuerriegel et al 2023)

Usage of AI in chemistry can aid in making numerous data collection methods more efficient, but is widely looked over.

For example, in spectroscopy, AI models can help identify compounds used and the functional groups it belongs to.

Applications of AI in chemistry would make a lot of basic experimentation more efficient. The ability to run simulations over and over again would also create an abundance of results which would make our overall result a lot more accurate.

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Source 1: Generative AI (Feuerriegel et. all 2023) 

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Source 1: Generative AI (Feuerriegel et. all 2023) 

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Humans can work collaboratively with AI: It isn’t something that humans should particularly be scared about.

Hybrid intelligence is the combination of human intelligence and AI to complete tasks in a more efficient way.

Hybrid learning is where AI is used collaboratively with human intelligence. With the creation of an AI model, it is thus important to incorporate elements of human intelligence within it (Feuerriegel et. all 2023).

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Source 6: Inspired, but not mimicking: a conversation between artificial intelligence and human intelligence - PMC (Zhao, 2022) 

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Source 6: Inspired, but not mimicking: a conversation between artificial intelligence and human intelligence - PMC (Zhao, 2022) 

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Human intelligence has empathy, emotion, and creativity behind it. Although it can be argued that AI has a certain degree of creativity with its ability to produce so many outputs, there is more thought behind human intelligence

→AI is modeled after the human brain, many models use “neural networks” mimicking the connections made between neurons of a brain.

→ “there is extensive feedback modulation in human visual information processing. The current deep networks are primarily based upon feedforward signals, although there are efforts to incorporate feedback processing into the network.

AI’s failure to use contextual information/prior knowledge in a feedback loop is a setback that CNNs work to improve, something human brains are able to do easily.

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Source 7: Full article: The science of artificial intelligence and its critics (Collins, 2021) 

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Source 7: Full article: The science of artificial intelligence and its critics (Collins, 2021) 

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AI is increasingly becoming important in many fields of science. This source emphasizes how important it is for the usage of artificial intelligence should constantly be critiqued as it is a field that thrives upon criticism.

The effectiveness of AI relies on its ability to constantly learn from itself.

A combination of AI models may work in a more human-like manner than an AI model alone. By utilizing AI models and conversing them between each other, it becomes clear that they are able to learn from each other and build upon each other in a very similar way to humans and their collaborative nature.

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Source 3: Full article: Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory  (Kuntz & Wilson, 2022)

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Source 3: Full article: Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory  (Kuntz & Wilson, 2022)

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AI in chemistry can be seen trough a partial charge descriptor, where an atom-path-descriptor is used to calculate atomic reactions given an inputted path length.

AI is thus able to make these paths easier and faster to see the results of than if you were to experiment with each path length.

--> Amon Machine Learning experiment, where a target molecule ChemML is a program with machine  learning algorithms tailored to chemistry

With AI, calculations being done by computers regarding chemical reactions/characteristics, are done a lot faster and with a lot more data backing it. AI is able to constantly learn from training data in a way that no human can possibly accomplish, which is why it makes it so imperative that it is leveraged for completing chemistry experiments

AI is being increasingly used in the chemistry field, prompting for more human-AI integration in chemical informatics.

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Source 10: Artificial Intelligence in Chemistry: Current Trends and Future Directions | Journal of Chemical Information and Modeling (Baum, et. all, 2021)

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Source 10: Artificial Intelligence in Chemistry: Current Trends and Future Directions | Journal of Chemical Information and Modeling (Baum, et. all, 2021)

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Journal and patent publications have experienced significant growth in the integration of AI within analytical chemistry/biochemistry

The accessibility of open-source AI and machine learning fundamentals such as PyTorch (a python extension) has aided in the growing field of AI in chemistry.

Many chemists are coming from data-literate backgrounds, making it easier for them to learn the processes of AI. AI in chemistry is proven to automate laboratory practices and make them less effort than before.

The need for chemists to be data-literate is rising, and it is important for them to know how to implement artificial intelligence into their laboratory practices to make them more efficient and more automated.

Chemists can use AI’s ability to optimize reaction conditions, and finding synthetic routes to complex target molecules.

Due to the nature of computing power and technological innovation moving at an increasingly faster pace, it is important for chemists to learn how to utilize AI in their work.

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AI's practical applications in cheminformatics

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AI's practical applications in cheminformatics

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Source 2: AI in basic chemistry (Chandrakant & Prabhakarrao, 2023) 

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Source 2: AI in basic chemistry (Chandrakant & Prabhakarrao, 2023) 

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AI in chemistry can be used in reaction optimization, reaction design, predicting reaction outcomes, designing new reactions, error analysis, stoichiometry problems, mole-to-mole conversions, reaction calculations, and limiting reagent identification. All of these are basic chemistry topics that can be made more efficient with the use of AI.

In mole-to-mole conversion, AI is able to instantly complete these conversions, a lot faster than a human can and a lot more accurately. There are less factors that could lead to human error, which is where a lot of experimental results can go wrong.

AI uses big datasets consistent with chemicals and analyzes them to which will work best for that specific chemical reaction.

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Source 9: Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data - Houhou - 2021 - Analytical Science Advances - Wiley Online Library (Houhou & Bocklitz, 2021) 

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Source 9: Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data - Houhou - 2021 - Analytical Science Advances - Wiley Online Library (Houhou & Bocklitz, 2021) 

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Deep learning methods were applied to mass spectrometry data through the development of QPMASS. For gas chromatography-mass spectrometry data, this model was able to implement parallel computing to process large scale datasets and reduce false positive/negative errors to less than 5 percent.

It is very important to preprocess data for chemical data to be useful in the usage of artificial intelligence algorithms. Artifacts, or distortions to the data, can set back results and make them less reliable.

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Source 15: Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems (Keith, et. all, 2021)

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Source 15: Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems (Keith, et. all, 2021)

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The accuracy of AI models depends on the representation of the data inputs. With different representations comes different accuracies in creating high-quality computations. The datasets that CompChem feeds on are well-controlled, and are used to achieve chemical accuracy. Even with the introduction of CompChem

AI data generation process isn’t a simple, straightforward process. Instead, there are many steps to its procedures that include a lot of failure within the algorithm.

This is described as the feedback loop. It also stresses the importance of datasets that are updating constantly, and one who correctly aligns to the purpose of the AI model.

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Source 12: Artificial Intelligence Generative Tools and Conceptual Knowledge in Problem Solving in Chemistry (Daher, et. all 2023)

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Source 12: Artificial Intelligence Generative Tools and Conceptual Knowledge in Problem Solving in Chemistry (Daher, et. all 2023)

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This article took concepts from a fundamental chemistry course, Material Science, which covered topics such as particles, ions, chemical bonds, periodic trends, chemical properties, etc. Questions were fed to chatGPT and the answers were collected.

The study revealed that chatGPT sometimes had struggles in identifying chemical rules and the nature of some compounds. It had problems with problem-solving because of arithmetic errors. It was unable to apply chemical concepts to an everyday context. ChatGPT was also unable to translate images (graphical representations)

ChatGPT is not able to process complex chemical structures due to the representation they are in. It was unable to correctly identify the data in the first place, making the predictive accuracy and solutions less accurate.

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Source 14: A comprehensive survey and analysis of generative models in machine learning (Gourisaria, et. all, 2020)

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Source 14: A comprehensive survey and analysis of generative models in machine learning (Gourisaria, et. all, 2020)

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Generative adversarial networks are able to achieve high accuracy in creating visual data.

Ex. a CNN (discriminative model) only spots a few stark differences between cats and dogs, whereas DCGAN can generate new photos of cats and dog by analyzing ALL components of their pictures (called "features"), is able to use what the CNN may have skipped over

→ Generative models as able to learn correlations within datasets of images in comparison to discriminative models that are simply used to label data that is given to the model. It also gives an example of how this difference can be seen.Generative models are able to use patterns from the overall distribution of data to create more data.

Discriminative models have largely been used in problem solving as many use classifier-based solutions, without considering generative models and their usefulness in this field. Generative models are able to select features and help with classification and model accuracy.

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Source 11: Artificial intelligence: machine learning for chemical sciences (Karthikeyan & Priyakumar, 2022) 

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Source 11: Artificial intelligence: machine learning for chemical sciences (Karthikeyan & Priyakumar, 2022) 

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This article discusses how to transform physical and chemical information using encoding techniques which turn molecules into representations of molecular descriptors or feature vectors. These molecular descriptors can be classified as 0D, 1D, 2D, 3D, or 4D descriptors. For example, 2D descriptors contain information on the size and electron distribution of the molecules.

It is first turned into a 2D matrix, and then is used to display 3D information like atomic coordinates/bond angles. Linear notations such as SMILES and InChI are used to transform the chemical’s 3D structure into its symbolic representation, used in the AI to find patterns.

Predictive accuracy is also increased with this preliminary step, as this data representation is the only way the machine learning model can truly take in.

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Source 13 : Survey on Synthetic Data Generation, Evaluation Methods and GANs (Figueira & Vaz, 2022) 

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Source 13 : Survey on Synthetic Data Generation, Evaluation Methods and GANs (Figueira & Vaz, 2022) 

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“The discriminator outputs a probability (continuous value between 0 and 1) that indicates whether a given observation came from the original data (0 means that the discriminator is 100% certain that the given example was synthesized, while 1 means the exact opposite)”

--> Shows how the discriminator and generator parts of the GAN model work to classify probabilities present within a dataset. It explains how his data is represented within the algorithm (1s and 0s)

Identifies the principle (binary cross entropy) that is used as the loss function in the generator. A loss function stops training in the generator if it is not able to “catch up” to the discriminator. It is a complicated equation that can be implemented into the code without much mathematical difficulty as it is just built into the code, but it is important to know what type of loss function is being used and why.

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AI is being increasingly used in the chemistry field, prompting for more human-AI integration in chemical research

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AI is being increasingly used in the chemistry field, prompting for more human-AI integration in chemical research

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Due to AI's ability to utilize LARGE datasets, it is able to perform many chemical experiments with high accuracy

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Due to AI's ability to utilize LARGE datasets, it is able to perform many chemical experiments with high accuracy

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It is important to find a balance within the data preprocessing step to make sure the model creates the best accuracy

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It is important to find a balance within the data preprocessing step to make sure the model creates the best accuracy

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Computational Chem is a form of generative AI that uses machine learning to create highly-accurate generated outputs based on chemical data

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Computational Chem is a form of generative AI that uses machine learning to create highly-accurate generated outputs based on chemical data

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The unique abilities of humans can be leveraged to complement the purposes of AI.

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The unique abilities of humans can be leveraged to complement the purposes of AI.

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AI as a field trying to strive for more human-like structures

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AI as a field trying to strive for more human-like structures

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This demonstrates the increased need for humans themselves to interact with AI, including chemists 

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This demonstrates the increased need for humans themselves to interact with AI, including chemists 

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By creating an AI  with generative properties for visual data, better performance in chemical computations will be achieved

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By creating an AI  with generative properties for visual data, better performance in chemical computations will be achieved

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Generative models are able to create realistic data samples in a way discriminative models cannot, due to their ability to analyze more data types

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Generative models are able to create realistic data samples in a way discriminative models cannot, due to their ability to analyze more data types

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With a GAN’s generative properties, and more ways of interpreting data, it’s important to know how to interpret its outputs and stop it from producing faulty ones.

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With a GAN’s generative properties, and more ways of interpreting data, it’s important to know how to interpret its outputs and stop it from producing faulty ones.