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Challenges and Limitations of ChatGPT and Artificial Intelligence for Scientific Research: A Perspective from Organic Materials
Challenges and Limitations of ChatGPT and Artificial Intelligence for Scientific Research: A...
Communication Challenges and Limitations of ChatGPT and Artificial Intelligence for Scientific Research: A Perspective from Organic Materials Hao-Wen Cheng Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA; email@example.com Abstract: Artiﬁcial Intelligence (AI) has emerged as a transformative technology in the scientiﬁc community with the potential to accelerate and enhance research in various ﬁelds. ChatGPT, a popular language model, is one such AI-based system that is increasingly being discussed and being adapted in scientiﬁc research. However, as with any technology, there are challenges and limitations that need to be addressed. This paper focuses on the challenges and limitations that ChatGPT faces in the domain of organic materials research. This paper will take organic materials as examples in the use of ChatGPT. Overall, this paper aims to provide insights into the challenges and limitations of researchers working in the ﬁeld of organic materials. Keywords: artiﬁcial intelligence; ChatGPT; organic materials 1. Introduction In late 2022, OpenAI, an artiﬁcial intelligence (AI) company, introduced an interactive chatbot named ChatGPT which attracts signiﬁcant attention from both economic and academic ﬁelds. ChatGPT quickly reached 1 million users within just ﬁve days , setting a new benchmark for speedy user acquisition. To provide perspective, ChatGPT’s growth rate was 15 times faster than TikTok, the fastest-growing social media platform. Moreover, ChatGPT continued to set an unparalleled record for growth, reaching 100 million users within two months of its launch . The concept of whether machines can think was Citation: Cheng, H.-W. Challenges proposed by A.M. Turing, in 1950, demonstrated a famous test to determine whether and Limitations of ChatGPT and humans can distinguish between conversations with humans and machines . ChatGPT Artiﬁcial Intelligence for Scientiﬁc easily passes the Turing test, indicating that the era of AI has indeed arrived. Research: A Perspective from OpenAI’s ﬁrst large language model (LLM), called GPT-1, was launched in 2017 Organic Materials. AI 2023, 4, followed by subsequent versions of GPT-2, GPT-3, and the widely-discussed ChatGPT. In 401–405. https://doi.org/10.3390/ scientiﬁc society, ChatGPT is recently acclaimed for its ability to assist in summarizing ai4020021 research papers, provide general experimental procedures, and compare experiment results, Academic Editor: José Machado thus providing a more efﬁcient approach than internet surﬁng [4–7]. It is widely believed that LLMs, such as ChatGPT, will make disruptive changes, becoming indispensable Received: 13 March 2023 Revised: 13 April 2023 assistants for professional use. Accepted: 28 April 2023 The future impacts of ChatGPT on scientiﬁc writing and publishing have been quickly Published: 4 May 2023 realized and discussed by many researchers [4,6,7]. However, how this open-accessible AI tool can be utilized to facilitate scientiﬁc research remains unexplored. This article will utilize ChatGPT as an LLM model to demonstrate how this technique can assist in scientiﬁc research, using organic materials as an example due to their recent attention in the ﬁeld Copyright: © 2023 by the author. of ﬂexible and wearable electronics [8–12]. Additionally, I will examine the limitations of Licensee MDPI, Basel, Switzerland. ChatGPT for research purposes and these challenges will be discussed into three categories. This article is an open access article distributed under the terms and 2. Results conditions of the Creative Commons Starting a new research project can be a challenging task, especially when delving into Attribution (CC BY) license (https:// unfamiliar areas for beginners. It often requires a substantial amount of academic literature creativecommons.org/licenses/by/ to come up with ideas which can be a time-consuming process. However, ChatGPT offers 4.0/). AI 2023, 4, 401–405. https://doi.org/10.3390/ai4020021 https://www.mdpi.com/journal/ai AI 2023, 4 402 a convenient solution by providing quick background and directions within seconds. For instance, by asking ChatGPT to “Summarize the recent organic light emitting diode development in polymers” or “Give me ﬁve top cited papers on stretchable polymer semiconductors for organic transistors”, the researcher can quickly obtain knowledge through the summaries from ChatGPT. The other example is asking “List the most popular research directions for organic solar cells” which yields a range of potential directions, including materials development, device optimization, tandem and hybrid devices, large area and ﬂexible devices, and stability and durability (see Supporting Information S1). One of the signiﬁcant advantages of ChatGPT is that it generates information based on the user ’s input, making it more relevant to their needs. To delve deeper into the materials aspect of organic solar cells, I requested more speciﬁc details from ChatGPT. Here, the issue comes up. While the inorganic material “perovskite” was suggested, it is essential to note that this material is not included in organic solar cells. This indicates ChatGPT lacks the capability to give us useful information. For a more comprehensive response, please refer to Supporting Information S2. In my pursuit to leverage the capabilities of the AI-powered chatbot to augment our research potential, I have identiﬁed three potential limitations in its responses. These in- clude (i) computational calculation constraints, (ii) the potential for inaccurate or false facts, and (iii) inadequacies in inferential capability. It is crucial to acknowledge these limitations to ensure that the chatbot’s responses are interpreted and applied with appropriate caution and context. The ﬁrst issue is the computational capability of ChatGPT. For example, I asked “The material provided an NIR absorption spectrum with an onset of 945 nm, what is the optical bandgap?”. Here is what ChatGPT replied: “To calculate the optical bandgap, we can use the following formula: Eg = hc/, where Eg is the bandgap energy, h is Planck’s constant, c is the speed of light, and is the wavelength corresponding to the onset of the absorption spectrum. In this case, the onset wavelength is 945 nm. Plugging this value into the formula, we get: 34 8 9 Eg = (6.626 10 J s) (2.998 10 m/s)/(945 10 m), Eg = 2.08 eV. Therefore, the optical bandgap of the material is approximately 2.08 eV.” It has become evident that ChatGPT may occasionally provide incorrect responses, such as when it miscalculates units, leading to an inaccurate answer. For example, when asked “What is the value of 99,999,999 + 0.9”, the chatbot’s response was “The result of 99,999,999 + 0.9 is 100,000,000.9” instead of “99,999,999.9”. This issue has been previously investigated by a group at Oxford . To address this concern, it is recommended to double check and clarify queries to ensure accurate responses. A possible approach is to request a recalculation and provide additional details to guide ChatGPT’s response. For instance, by typing “I think there might be an error. Can you please recalculate the bandgap with these parameters?”. “I apologize for the mistake in my previous response. Based on the information given, if the onset of the near-infrared (NIR) absorption spectrum is near 945 nm, then the optical bandgap can be calculated as follows: 1 eV = 1240/ (in nm). Therefore, the energy corresponding to an onset of 945 nm is E = 1240/945 = 1.31 eV. AI 2023, 4 403 So, the optical bandgap is 1.31 eV.” What amazed me is that ChatGPT admitted the fault and successfully replied with the correct answer. Second, it is noteworthy that ChatGPT failed to response to false facts. For instance, I asked the question “Why polymers mobility is higher than small molecules mobility?”. In common cases, the mobility of small molecule single crystals is generally higher than polymers. However, ChatGPT did not correct the question but generated responses based on false facts and combined the facts into paragraphs. See full response in Supporting Information S3. Thus, when asking these kinds of questions, users should be careful. Furthermore, it is essential to note that ChatGPT may lack inferential capabilities in certain scenarios. As evident from the example provided where the scenario that is “Now you have three donor materials for organic solar cells to match with the same acceptor. The highest occupied molecular orbital (HOMO) energy levels of A, B, and C are 5 eV, 5.1 eV, and 5.2 eV. The lowest unoccupied molecular orbital (LUMO) energy level of the acceptor is 4 eV. Which combination of materials has the highest and lowest open-circuit voltage (V )?” Here is the response. oc “Assuming that the V is determined only by the energy levels of the donor and oc acceptor materials, the V of a solar cell can be calculated by the difference between the oc HOMO of the donor and the LUMO of the acceptor. For the given materials, the V can be calculated as follows: oc A: V = ( 5 eV) ( 4 eV) = 1 eV oc B: V = ( 5.1 eV) ( 4 eV) = 1.1 eV oc C: V = ( 5.2 eV) ( 4 eV) = 1.2 eV oc Therefore, the combination of materials with the highest V is Material A, and the oc combination with the lowest V is Material C”. oc Apparently, the V will not have negative values and the answers were wrong. oc Therefore, it is advisable to refrain from asking ChatGPT inferential questions as it may lead to incorrect answers being generated. Lastly, the use of ChatGPT in organic materials research raises various ethical consid- erations that need to be addressed. One potential issue is the risk of biases in generated content due to the data biases that users provide. The use of AI tools, such as ChatGPT, could also have implications for the scientiﬁc community, potentially shifting the research focus away from important areas. Privacy and security concerns arise when processing sensitive or unpublished data, and researchers must ensure that appropriate measures are in place to prevent unauthorized access or misuse of data. The generated content may be subject to intellectual property laws, creating legal challenges and the risk of copyright infringement. Accountability is another important factor that needs to be considered in the use of AI tools, such as ChatGPT, in scientiﬁc research, and researchers must be trans- parent about their use and accountable for the research outcomes, including any potential inaccuracies or biases in the generated content. 3. Conclusions To conclude, ChatGPT is able to provide assistance in many new research areas, and we are still at the very early stage of exploring its application scope. I have no doubt that AI tools will become a game-changing player in all ﬁelds, including our scientiﬁc society. However, there are many challenges in terms of research purposes. So far ChatGPT is limited by computational constraints, the ability to point out inaccurate information or false faces, and inferential capability for scientiﬁc research. All those limitations may lead to misunderstandings or misinterpretation. To address these challenges, researchers are immersing themselves to improve the accuracy and reliability of the model. Emerging technologies and advancements in AI have had a signiﬁcant impact on the development and performance of language models, such as ChatGPT. For example, new AI techniques, such as deep learning and neural networks, have enabled the creation of AI 2023, 4 404 more sophisticated and accurate language models that can process vast amounts of data and learn from it. In addition, the rise of big data and cloud computing has made it possible to train and run language models at scale, opening up new possibilities for applications, such as chatbots, virtual assistants, and language translation. Moreover, the development of pre- trained language models, such as GPT-3, has reduced the amount of data and computing resources required to train new models, making it easier for researchers and developers to create their own language models. For example, collaboration with linguists can help to reﬁne language models by incor- porating a better understanding of the nuances of human language and communication. Collaboration with computer scientists can lead to the development of more efﬁcient and effective algorithms for training and running language models. Collaborations with psy- chologists and neuroscientists can help to reﬁne language models by providing insights into how the human brain processes language. Overall, the impact of emerging technologies and advancements in AI on the de- velopment and performance of language models, such as ChatGPT is signiﬁcant, and the potential for interdisciplinary collaboration is vast. By leveraging these advances and collaborating across disciplines, we can continue to drive innovation in the ﬁeld of language modeling and unlock new possibilities for applications in a wide range of in- dustries and contexts. I strongly believe that good use of the fast-growing LLM models, such as ChatGPT, can beneﬁt scientiﬁc research as a revolutionary tool and can boost technology development. Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/ai4020021/s1, The supplementary covers the ChatGPT records being discussed in the article, including “List the most popular research directions for organic solar cells”, “Give me more details on the materials aspect”, and “Why polymers mobility is higher than small molecules mobility”. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All responses were collected from chat.openai.com. Acknowledgments: I would like to express my appreciation to Xiaozhou Ji for engaging in valuable discussions with me. Conﬂicts of Interest: The author declares no conﬂict of interest. References 1. ChatGPT Sprints to One Million Users. Available online: https://www.statista.com/chart/29174/time-to-one-million-users/ (accessed on 10 March 2023). 2. ChatGPT Reaches 100 Million Users Two Months after Launch. Available online: https://www.theguardian.com/technology/20 23/feb/02/chatgpt-100-million-users-open-ai-fastest-growing-app (accessed on 10 March 2023). 3. Turing, A.M. Computing Machinery and Intelligence. Mind 1950, 236, 433–460. [CrossRef] 4. Thorp, H.H. ChatGPT Is Fun, but Not an Author. Science 2023, 379, 313. [CrossRef] [PubMed] 5. Kirmani, A.R. Artiﬁcial Intelligence-Enabled Science Poetry. ACS Energy Lett. 2022, 8, 574–576. [CrossRef] 6. Grimaldi, G.; Ehrler, B. AI et al.: Machines Are About to Change Scientiﬁc Publishing Forever. ACS Energy Lett. 2023, 8, 878–880. [CrossRef] 7. Buriak, J.M.; Akinwande, D.; Artzi, N.; Jeffrey Brinker, C.; Burrows, C.; Chan, W.C.W.; Chen, C.; Chen, X.; Chhowalla, M.; Chi, L.; et al. Best Practices for Using AI When Writing Scientiﬁc Manuscripts. ACS Nano 2023, 17, 4091–4093. [CrossRef] 8. Cheng, H.-W.; Zhao, Y.; Yang, Y. Toward High-Performance Semitransparent Organic Photovoltaics with Narrow-Bandgap Donors and Non-Fullerene Acceptors. Adv. Energy Mater. 2021, 12, 2102908. [CrossRef] 9. Cheng, H.-W.; Zhang, S.; Michalek, L.; Ji, X.; Luo, S.; Cooper, C.B.; Gong, H.; Nikzad, S.; Chiong, J.A.; Wu, Y.; et al. Realizing Intrinsically Stretchable Semiconducting Polymer Films by Nontoxic Additives. ACS Mater. Lett. 2022, 4, 2328–2336. [CrossRef] 10. Huang, H.-C.; Lin, Y.-C.; Chen, C.-H.; Wei, K.-H.; Su, Y.-W.; Chen, P.-T. Density Functional Theory Study of Donor–Acceptor Conjugated Polymers with Substituent Effect. J. Polym. Res. 2021, 28, 427. [CrossRef] AI 2023, 4 405 11. Su, Y.-W.; Huang, Y.-S.; Huang, H.-C.; Chen, P.-T. Optoelectronic Properties of a Benzodithiophene-Based Organic Photovoltaic. ECS J. Solid State Sci. Technol. 2021, 10, 075003. [CrossRef] 12. Cheng, H.-W.; Juan, C.-Y.; Mohapatra, A.; Chen, C.-H.; Lin, Y.-C.; Chang, B.; Cheng, P.; Wang, H.-C.; Chu, C.W.; Yang, Y.; et al. High-Performance Organic Photovoltaics Incorporating an Active Layer with a Few Nanometer-Thick Third-Component Layer on a Binary Blend Layer. Nano Lett. 2021, 21, 2207–2215. [CrossRef] [PubMed] 13. Frieder, S.; Pinchetti, L.; Grifﬁths, R.; Salvatori, T.; Lukasiewicz, T.; Petersen, P.C.; Chevalier, A.; Berner, J. Mathematical Capabilities of ChatGPT. arXiv 2023, arXiv:2301.13867. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Multidisciplinary Digital Publishing Institute
Challenges and Limitations of ChatGPT and Artificial Intelligence for Scientific Research: A Perspective from Organic Materials
, Volume 4 (2) –
May 4, 2023
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