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Book ; Online: Agent-based Learning of Materials Datasets from Scientific Literature

Ansari, Mehrad / Moosavi, Seyed Mohamad

2023

Abstract: Advancements in machine learning and artificial intelligence are transforming materials discovery. Yet, the availability of structured experimental data remains a bottleneck. The vast corpus of scientific literature presents a valuable and rich resource ... ...

Abstract	Advancements in machine learning and artificial intelligence are transforming materials discovery. Yet, the availability of structured experimental data remains a bottleneck. The vast corpus of scientific literature presents a valuable and rich resource of such data. However, manual dataset creation from these resources is challenging due to issues in maintaining quality and consistency, scalability limitations, and the risk of human error and bias. Therefore, in this work, we develop a chemist AI agent, powered by large language models (LLMs), to overcome these challenges by autonomously creating structured datasets from natural language text, ranging from sentences and paragraphs to extensive scientific research articles. Our chemist AI agent, Eunomia, can plan and execute actions by leveraging the existing knowledge from decades of scientific research articles, scientists, the Internet and other tools altogether. We benchmark the performance of our approach in three different information extraction tasks with various levels of complexity, including solid-state impurity doping, metal-organic framework (MOF) chemical formula, and property relations. Our results demonstrate that our zero-shot agent, with the appropriate tools, is capable of attaining performance that is either superior or comparable to the state-of-the-art fine-tuned materials information extraction methods. This approach simplifies compilation of machine learning-ready datasets for various materials discovery applications, and significantly ease the accessibility of advanced natural language processing tools for novice users in natural language. The methodology in this work is developed as an open-source software on https://github.com/AI4ChemS/Eunomia.
Keywords	Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2023-12-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Improvement of Mouse Preantral Follicle Survival and Development following Co-Culture with Ovarian Parenchyma Cell Suspension.

Najafi Salehi, Javad / Eimani, Hussein / Shahverdi, Abdolhossein / Totonchi, Mehdi / Fathi, Rouhollah / Moosavi, Seyed Akbar / Taher Mofrad, Seyed Mohamad Javad / Tahaei, Leila Sadat

International journal of fertility & sterility

2024 Volume 18, Issue 2, Page(s) 153–161

Abstract: Background: The parallel and continued improvements in both infertility treatment and the management of malignancy cases have brought to the forefront the potential for fertility preservation. Using ovarian follicular resources can effectively improve ... ...

Abstract	Background: The parallel and continued improvements in both infertility treatment and the management of malignancy cases have brought to the forefront the potential for fertility preservation. Using ovarian follicular resources can effectively improve reproductive capacity and prevent infertility. The primary aim of this research was to try to generate an appropriate in vivo environment for the growth of the mouse follicles. Hence, the possible effects of the ovarian parenchyma cell suspension were explored on the growth and maturation of preantral follicles in vitro. Materials and methods: In this experimental study, ovarian parenchymal cells were mechanically dissociated from preantral follicles of 12-14 days-old NMRI mice and then divided into 5 experimental groups (G1: Control, G2: Fresh follicle with fresh parenchyma cell suspension, G3: Vitrified-warmed follicle with fresh parenchyma cell suspension, G4: Fresh follicle with frozen-thawed parenchyma cell suspension, and G5: Vitrified-warmed follicle with frozenthawed parenchyma cell suspension). The diameter of the follicles and immature oocytes, viability, antrum formation, resumption of meiosis, in vitro fertilization (IVF), and Gdf9, Bmp6, and Bmp15 gene expression were examined on different periods. Results: The diameter of the follicles and the oocytes on days 4 and 8, as well as the survival rate of the follicles up to day 12, were significantly higher in G2 and G4 compared to the Ctrl group (G1: 73.66%, G2:87.99%, G3: 82.70%, G4: 94.37%, and G5: 78.59%). Expression of growth marker genes for G3, and G5 groups was significantly higher than other groups, which indicated the protective effects of parenchyma cell suspension on follicles damaged by vitrification solutions. Conclusion: The growth, survival, and maturation of preantral follicles could be enhanced by co-culturing them with ovarian parenchyma cells. Further studies are needed to optimize the conditions for a successful parenchyma cell suspension-induced in vitro maturation (IVM) to occur in infertility clinics.
Language	English
Publishing date	2024-02-02
Publishing country	Iran
Document type	Journal Article
ZDB-ID	2570865-X
ISSN	2008-0778 ; 2008-076X
ISSN (online)	2008-0778
ISSN	2008-076X
DOI	10.22074/ijfs.2023.1990372.1439
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture.

Moubarak, Elias / Moosavi, Seyed Mohamad / Charalambous, Charithea / Garcia, Susana / Smit, Berend

Industrial & engineering chemistry research

2023 Volume 62, Issue 26, Page(s) 10252–10265

Abstract: To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms predicted from ... ...

Abstract	To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal-organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO
Language	English
Publishing date	2023-06-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	1484436-9
ISSN	1520-5045 ; 0888-5885
ISSN (online)	1520-5045
ISSN	0888-5885
DOI	10.1021/acs.iecr.3c01358
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Organizational Climate of the COVID-19 Intensive Care Units: A Qualitative Content Analysis Study.

Khorasani, Parvaneh / Ebrahimi, Amrollah / Andalib, Sima / Ahmadi, Mahnaz / Moosavi, Seyed Mohamad Hosein

Journal of caring sciences

2023 Volume 12, Issue 3, Page(s) 174–180

Abstract: Introduction: To manage the psychological consequences of providing services in the COVID-19 intensive care units (ICUs), it is necessary to identify the experience of nurses from the organizational climate. The current study was conducted to explain ... ...

Abstract	Introduction: To manage the psychological consequences of providing services in the COVID-19 intensive care units (ICUs), it is necessary to identify the experience of nurses from the organizational climate. The current study was conducted to explain the nurses' experience of the organizational climate of the COVID-19 ICUs. Methods: This qualitative study was conducted in three teaching hospitals affiliated to Isfahan University of Medical Sciences. 17 individual and semi-structured interviews with 12 nurses working in three selected COVID-19 centers were included in the data analysis. The participants were selected by purposive sampling and interviewed in one or more sessions at a suitable time and place. Interviews lasted for 45 to 90 minutes and continued with conventional content analysis until data saturation. Data analysis was done using conventional content analysis of Graham and Leideman model. Guba and Lincoln criteria (including validity, transferability, consistency, and reliability) were used to ensure reliability and accuracy. Results: The results of data analysis were classified into 82 primary concept codes and 10 sub-categories in the form of 3 categories: "positive climate of attachment and professional commitment", "emotional resonance in the work environment" and "supportive environment of the organization". Conclusion: This study led to the identification of nurses' experiences of the organizational climate during the COVID-19 which provides appropriate information to nursing managers to create a favorable organizational climate and increase the quality of work-life of nurses.
Language	English
Publishing date	2023-08-07
Publishing country	Iran
Document type	Journal Article
ZDB-ID	2667363-0
ISSN	2251-9920
ISSN	2251-9920
DOI	10.34172/jcs.2023.31909
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Design of New Inorganic Crystals with the Desired Composition Using Deep Learning.

Han, Seunghee / Lee, Jaewan / Han, Sehui / Moosavi, Seyed Mohamad / Kim, Jihan / Park, Changyoung

Journal of chemical information and modeling

2023 Volume 63, Issue 18, Page(s) 5755–5763

Abstract: New solid-state materials have been discovered using various approaches from atom substitution in density functional theory (DFT) to generative models in machine learning. Recently, generative models have shown promising performance in finding new ... ...

Abstract	New solid-state materials have been discovered using various approaches from atom substitution in density functional theory (DFT) to generative models in machine learning. Recently, generative models have shown promising performance in finding new materials. Crystal generation with deep learning has been applied in various methods to discover new crystals. However, most generative models can only be applied to materials with specific elements or generate structures with random compositions. In this work, we developed a model that can generate crystals with desired compositions based on a crystal diffusion variational autoencoder. We generated crystal structures for 14 compositions of three types of materials in different applications. The generated structures were further stabilized using DFT calculations. We found the most stable structures in the existing database for all but one composition, even though eight compositions among them were not in the data set trained in a crystal diffusion variational autoencoder. This substantiates the prospect of the generation of an extensive range of compositions. Finally, 205 unique new crystal materials with energy above hull <100 meV/atom were generated. Moreover, we compared the average formation energy of the crystals generated from five compositions, two of which were hypothetical, with that of traditional methods like atom substitution and a generative model. The generated structures had lower formation energy than those of other models, except for one composition. These results demonstrate that our approach can be applied stably in various fields to design stable inorganic materials based on machine learning.
MeSH term(s)	Deep Learning ; Databases, Factual ; Density Functional Theory ; Diffusion ; Machine Learning
Language	English
Publishing date	2023-09-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.3c00935
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Article ; Online: Organizational Climate of the COVID-19 Intensive Care Units

Parvaneh Khorasani / Amrollah Ebrahimi / Sima Andalib / Mahnaz Ahmadi / Seyed Mohamad Hosein Moosavi

Journal of Caring Sciences, Vol 12, Iss 3, Pp 174-

A Qualitative Content Analysis Study

2023 Volume 180

Abstract	Introduction: To manage the psychological consequences of providing services in the COVID-19 intensive care units (ICUs), it is necessary to identify the experience of nurses from the organizational climate. The current study was conducted to explain the nurses’ experience of the organizational climate of the COVID-19 ICUs. Methods: This qualitative study was conducted in three teaching hospitals affiliated to Isfahan University of Medical Sciences. 17 individual and semi-structured interviews with 12 nurses working in three selected COVID-19 centers were included in the data analysis. The participants were selected by purposive sampling and interviewed in one or more sessions at a suitable time and place. Interviews lasted for 45 to 90 minutes and continued with conventional content analysis until data saturation. Data analysis was done using conventional content analysis of Graham and Leideman model. Guba and Lincoln criteria (including validity, transferability, consistency, and reliability) were used to ensure reliability and accuracy. Results: The results of data analysis were classified into 82 primary concept codes and 10 sub-categories in the form of 3 categories: "positive climate of attachment and professional commitment", "emotional resonance in the work environment" and "supportive environment of the organization". Conclusion: This study led to the identification of nurses’ experiences of the organizational climate during the COVID-19 which provides appropriate information to nursing managers to create a favorable organizational climate and increase the quality of work-life of nurses.
Keywords	nurses ; intensive care units ; covid-19 ; organizational climate ; qualitative research ; Medicine (General) ; R5-920 ; General works ; R5-130.5
Subject code	650
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	Tabriz University of Medical Sciences
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Role of Machine Learning in the Understanding and Design of Materials.

Moosavi, Seyed Mohamad / Jablonka, Kevin Maik / Smit, Berend

Journal of the American Chemical Society

2020

Abstract: Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective ... ...

Abstract	Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.
Language	English
Publishing date	2020-11-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	3155-0
ISSN	1520-5126 ; 0002-7863
ISSN (online)	1520-5126
ISSN	0002-7863
DOI	10.1021/jacs.0c09105
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworks.

Jablonka, Kevin Maik / Ongari, Daniele / Moosavi, Seyed Mohamad / Smit, Berend

Nature chemistry

2021 Volume 13, Issue 8, Page(s) 771–777

Abstract: Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can ...

Abstract	Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can fail to describe oxidation states in extended crystalline systems such as metal-organic frameworks. Here we propose the use of a machine-learning model, trained on assignments by chemists encoded in the chemical names in the Cambridge Structural Database, to automatically assign oxidation states to the metal ions in metal-organic frameworks. In our approach, only the immediate local environment around a metal centre is considered. We show that the strategy is robust to experimental uncertainties such as incorrect protonation, unbound solvents or changes in bond length. This method gives good accuracy and we show that it can be used to detect incorrect assignments in the Cambridge Structural Database, illustrating how collective knowledge can be captured by machine learning and converted into a useful tool.
Language	English
Publishing date	2021-07-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2464596-5
ISSN	1755-4349 ; 1755-4330
ISSN (online)	1755-4349
ISSN	1755-4330
DOI	10.1038/s41557-021-00717-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF.

Domingues, Nency P / Moosavi, Seyed Mohamad / Talirz, Leopold / Jablonka, Kevin Maik / Ireland, Christopher P / Ebrahim, Fatmah Mish / Smit, Berend

Communications chemistry

2022 Volume 5, Issue 1, Page(s) 170

Abstract: The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of ...

Abstract	The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al
Language	English
Publishing date	2022-12-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2929562-2
ISSN	2399-3669 ; 2399-3669
ISSN (online)	2399-3669
ISSN	2399-3669
DOI	10.1038/s42004-022-00785-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Effects of Modafinil on Sleep Pattern during Methamphetamine Withdrawal: A Double-blind Randomized Controlled Trial.

Moosavi, Seyed Mohamad / Yazdani-Charati, Jamshid / Amini, Fatemeh

Addiction & health

2019 Volume 11, Issue 3, Page(s) 165–172

Abstract: Background: Methamphetamine (MA) abuse is a serious and costly public health problem worldwide; It also commonly affects the sleep quality. The present study was carried out aiming to evaluate the effectiveness of modafinil versus placebo on sleep ... ...

Abstract	Background: Methamphetamine (MA) abuse is a serious and costly public health problem worldwide; It also commonly affects the sleep quality. The present study was carried out aiming to evaluate the effectiveness of modafinil versus placebo on sleep pattern in MA withdrawal during an eight-week period. Methods: In a double-blind randomized controlled study, a total of 80 patients with a confirmed diagnosis MA withdrawal were treated with modafinil (200 mg/day). Pittsburgh Sleep Quality Index (PSQI) and Epworth sleepiness scale (ESS) were used to assess sleep pattern in the 1 Findings: The mean age of the people in the intervention and placebo groups was 32.92 ± 2.06 and 34.08 ± 2.13 years, respectively. The mean scores of ESS decreased from 16.15 ± 4.50 to 9.15 ± 3.34 after the intervention in the modafinil group (P < 0.001), with no significant reduction in the placebo group (P = 0.990). The mean scores of PSQI decreased from 13.88 ± 3.40 to 9.92 ± 3.10 after the intervention in the modafinil group (P < 0.001), however there was no significant reduction in the placebo group (P = 0.980). The value of the Eta effect size of the PSQI and ESS questionnaires was 0.52 and 0.72, respectively. Modafinil was superior to placebo in improving the PSQI and ESS scales in the 56 Conclusion: Modafinil improves the sleep quality in patients with MA withdrawal.
Language	English
Publishing date	2019-12-03
Publishing country	Iran
Document type	Journal Article
ZDB-ID	2574430-6
ISSN	2008-8469 ; 2008-4633
ISSN (online)	2008-8469
ISSN	2008-4633
DOI	10.22122/ahj.v11i3.219
Database	MEDical Literature Analysis and Retrieval System OnLINE

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