jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ángel Yanguas-Gil | Matthew T. Dearing | Jeffrey W. Elam | Jessica C. Jones | Sungjoon Kim | Adnan Mohammad | Chi Thang Nguyen | Bratin Sengupta
Source: arXiv (Cornell University) (preprint, 2024-12-13)
Field: Materials Science — Materials Chemistry

Benchmarking large language models for materials synthesis: the case of atomic layer deposition In this work we introduce an open-ended question benchmark, ALDbench, to evaluate the performance of large language models (LLMs) in materials synthesis, and in particular in the field of atomic layer deposition, a thin film growth technique used in energy applications and microelectronics. Our benchmark comprises questions with a level of difficulty ranging from graduate level to domain expert current with the state of the art in the field. Human experts reviewed the questions along the criteria of difficulty and specificity, and the model responses along four different criteria: overall quality, specificity, relevance, and accuracy. We ran this benchmark on an instance of OpenAI's GPT-4o. The responses from the model received a composite quality score of 3.7 on a 1 to 5 scale, consistent with a passing grade. However, 36% of the questions received at least one below average score. An in-depth analysis of the responses identified at least five instances of suspected hallucination. Finally, we observed statistically significant correlations between the difficulty of the question and the quality of the response, the difficulty of the question and the relevance of the response, and the specificity of the question and the accuracy of the response as graded by the human experts. This emphasizes the need to evaluate LLMs across multiple criteria beyond difficulty or accuracy. Benchmarking ; Atomic layer deposition ; Layer (electronics) ; Deposition (geology) ; Layer by layer ; Nanotechnology ; Materials science ; Computer science ; Business ; Geology Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Semiconductor materials and devices
Authors: Ben Goertzel
Source: arXiv (Cornell University) (preprint, 2024-12-21)
Field: Materials Science — Materials Chemistry

ActPC-Chem: Discrete Active Predictive Coding for Goal-Guided Algorithmic Chemistry as a Potential Cognitive Kernel for Hyperon &amp; PRIMUS-Based AGI We explore a novel paradigm (labeled ActPC-Chem) for biologically inspired, goal-guided artificial intelligence (AI) centered on a form of Discrete Active Predictive Coding (ActPC) operating within an algorithmic chemistry of rewrite rules. ActPC-Chem is envisioned as a foundational "cognitive kernel" for advanced cognitive architectures, such as the OpenCog Hyperon system, incorporating essential elements of the PRIMUS cognitive architecture. The central thesis is that general-intelligence-capable cognitive structures and dynamics can emerge in a system where both data and models are represented as evolving patterns of metagraph rewrite rules, and where prediction errors, intrinsic and extrinsic rewards, and semantic constraints guide the continual reorganization and refinement of these rules. Using a virtual "robot bug" thought experiment, we illustrate how such a system might self-organize to handle challenging tasks involving delayed and context-dependent rewards, integrating causal rule inference (AIRIS) and probabilistic logical abstraction (PLN) to discover and exploit conceptual patterns and causal constraints. Next, we describe how continuous predictive coding neural networks, which excel at handling noisy sensory data and motor control signals, can be coherently merged with the discrete ActPC substrate. Finally, we outline how these ideas might be extended to create a transformer-like architecture that foregoes traditional backpropagation in favor of rule-based transformations guided by ActPC. This layered architecture, supplemented with AIRIS and PLN, promises structured, multi-modal, and logically consistent next-token predictions and narrative sequences. Coding (social sciences) ; Cognition ; Computer science ; Chemistry ; Cognitive science ; Psychology ; Mathematics ; Neuroscience ; Statistics Machine Learning in Materials Science
Authors: Martin Seifrid | Stanley Lo | Dylan G. Choi | Gary Tom | My Linh Le | Kunyu Li | Rahul Sankar | Hoai‐Thanh Vuong | Hiba Wakidi | Ahra Yi | Ziyue Zhu | Nora Schopp | Aaron Peng | Benjamin R. Luginbuhl | Thuc‐Quyen Nguyen | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2024-03-25)
Field: Materials Science — Materials Chemistry

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices Our study explores the current state of machine learning (ML) as applied to predicting and designing organic photovoltaic (OPV) devices. We outline key considerations for selecting the method of encoding a molecular structure and selecting the algorithm while also emphasizing important aspects of training and rigorously evaluating ML models. This work presents the first dataset of OPV device fabrication data mined from the literature. The top models achieve state-of-the-art predictive performance. In particular, we identify an algorithm that is used less frequently, but may be particularly well suited to similar datasets. However, predictive performance remains modest (R2 ≅ 0.6) overall. An in-depth analysis of the dataset attributes this limitation to challenges relating to the size of the dataset, as well as data quality and sparsity. These aspects are directly tied to difficulties imposed by current reporting and publication practices. Advocating for standardized reporting of OPV device fabrication data reporting in publications emerges as crucial to streamline literature mining and foster ML adoption. This comprehensive investigation emphasizes the critical role of both data quantity and quality, and highlights the need for collective efforts to unlock ML's potential to drive advancements in OPV. Computer science ; Quality (philosophy) ; Photovoltaic system ; Data science ; Key (lock) ; Machine learning ; Work (physics) ; State (computer science) ; Data mining ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Conducting polymers and applications ; Green IT and Sustainability
Authors: Bhaskar Dwivedi | Diksha Bhardwaj | Deepika Choudhary
Source: International Research Journal of Innovations in Engineering and Technology (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Eco-friendly Synthesis and Characterization of CeO2-doped TiO2 Nanoparticles Using Biowaste: Unveiling Potent Antiinflammatory and Antioxidant Properties In this study, CeO2-doped TiO2 nanoparticles were synthesized using an eco-friendly approach, utilizing an aqueous extract from the calyx leaves (outer covering) of Physalis peruviana fruits. The morphology of the greensynthesized nanoparticles was characterized using UV-Vis spectroscopy, FT-IR, SEM, and X-ray diffraction. The anti-inflammatory activity of the biosynthesized NPs was evaluated through a denaturation test. The use of the fruit’s outer calyx, commonly discarded as food waste, presents a sustainable strategy for repurposing food and biological waste in nanotechnology. The NPs exhibited favorable magnetic properties, a well-defined crystalline structure, and notable anti-inflammatory effects, highlighting their potential for therapeutic and medical applications, particularly under green synthesis protocols. Characterization (materials science) ; Environmentally friendly ; Antioxidant ; Nanoparticle ; Doping ; Chemistry ; Chemical engineering ; Environmental chemistry ; Nuclear chemistry ; Nanotechnology ; Materials science ; Organic chemistry ; Biology ; Engineering ; Ecology Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis
Authors: Joonyoung F. Joung | Mun Hong Fong | Jihye Roh | Zhengkai Tu | John Bradshaw | Connor W. Coley
Source: arXiv (Cornell University) (preprint, 2024-03-07)
Field: Materials Science — Materials Chemistry

Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation. Scale (ratio) ; Computer science ; Product (mathematics) ; Artificial intelligence ; Machine learning ; Mathematics ; Cartography ; Geography Machine Learning in Materials Science
Authors: N. Ganesan
Source: Research Square (preprint, 2024-07-23)
Field: Materials Science — Materials Chemistry

Characteristics of Aluminum Oxide Nanoparticles and Their Germicidal Activity on Leather Fabrics Aluminum oxide ; Nanoparticle ; Aluminium ; Oxide ; Materials science ; Metallurgy ; Chemical engineering ; Nanotechnology ; Engineering Quantum Dots Synthesis And Properties ; Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Istie Rahayu | Aqila Alya Nabila Sabarna | Irma Wahyuningtyas | Rohmat Ismail | Esti Prihatini | Wayan Darmawan | Gilang Dwi Laksono | Irsan Alipraja
Source: Jurnal Sylva Lestari (article, 2024-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Effect of Synthetic and Commercial Nano-Magnetite on the Electromagnetic Absorbance Behavior of Magnetic Wood Magnetic wood with good electromagnetic wave absorption properties was prepared by comparing synthetic and commercial nano-magnetite (Fe3O4-NP) as sengon (Falcataria moluccana) wood impregnation solution. The co-precipitation method produced a synthetic nano-magnetite with NH4OH as a weak base precursor. Meanwhile, the commercial one was purchased from a supplier. Three levels of nano-magnetite concentration (1%, 2.5%, and 5%) were dispersed in deionized water. The impregnation process was done by applying a vacuum of 0.5 bar for 120 minutes, followed by a pressure of 1 bar for 120 minutes. The results showed that the commercial nano-magnetite caused more improvements in weight percent gain, density, and hardness than the synthetic nano-magnetic, although they were insignificantly different. There was also a reduction in brightness with the overall color change being categorized into other colors because the color became darker with increasing nano-magnetite concentration in both woods. The absorbance capacity of the synthetic nano-magnetite-treated wood was larger than the commercial nano-magnetite-treated wood. This synthetic nano-magnetite-treated wood had been optimally treated at a 5% concentration, making it suitable for use as electromagnetic wave shielding material because it can absorb almost 100% electromagnetic waves. Keywords: Fe3O4, impregnation, nano-magnetite, sengon wood, shielding materials Magnetite ; Absorbance ; Nano- ; Materials science ; Composite material ; Metallurgy ; Optics ; Physics Electromagnetic wave absorption materials ; Magnetic and Electromagnetic Effects ; Natural Fiber Reinforced Composites
Authors: L. Siddharth | Jianxi Luo
Source:  (preprint, 2024-12-26)
Field: Materials Science — Materials Chemistry

Knowledge of Technological Artefacts: Investigating the Linguistic and Structural Foundations Design and innovation processes primarily generate knowledge upon retrieving and synthesising knowledge of existing artefacts. Understanding the basis of knowledge governing these processes is essential for theoretical and practical advances, especially with the growing inclusion of Large-Language Models (LLMs) and their generative capabilities to support knowledge-intensive tasks. In this research, we analyse a large, stratified sample of patented artefact descriptions spanning the total technology space. Upon representing these descriptions as knowledge graphs, i.e., collections of entities and relationships, we investigate the linguistic and structural foundations through frequency distribution and motif discovery approaches. From the linguistic perspective, we identify the generalisable syntaxes that show how most entities and relationships are constructed at the term level. From the structural perspective, we discover motifs, i.e., statistically dominant 3-node and 4-node subgraph patterns, that show how entities and relationships are combined at a local level in artefact descriptions. Upon examining the subgraphs within these motifs, we understand that artefact descriptions primarily capture the design hierarchy of artefacts. We also find that natural language descriptions do not capture sufficiently precise knowledge at a local level, which can be a limiting factor for relevant innovation research and practice. Moreover, our findings are expected to guide LLMs in generating knowledge pertinent to domain-specific design environments, to inform structuring schemes for future knowledge management systems, and to advance design and innovation theories on knowledge synthesis. Structuring ; Computer science ; Hierarchy ; Knowledge management ; Domain knowledge ; Perspective (graphical) ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Design Education and Practice
Authors: Sheryil Mavilayil
Source: African Journal of Biomedical Research (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Synthesis, nanostructural analysis, and antibacterial properties of nanostructured MgFe2O4 against various bacterial strains. Antibacterial activity ; Nanotechnology ; Materials science ; Bacteria ; Biology ; Genetics Magnetic Properties and Synthesis of Ferrites
Authors: Eman R. Mohammed | Intesar A. Sulleman | Shakir M. Saied | Assim A. Sabah
Source: Bulletin of the Chemical Society of Ethiopia (article, 2024-12-31)
Field: Chemistry — Organic Chemistry

Synthesis of novel complexes derived from methyl or benzyl 4-amino antipyrinyl dithiocarbamtes with divalent Mn(II), Co(II), Ni(II), Zn(II) and Cu(II) Two new ligands, methyl 4-amino antipyrinyl dithiocarbamates (MAC) and benzyl 4-amino antipyrinyl dithiocarbamates (BAC), were synthesized from 4-amino antipyrine and corresponding methyl or benzyl halides with potassium hydroxide in dimethylformamide. Then they were coordinated with five divalent transition metals: M = Mn, Co, Ni, Zn, and Cu(II) to produce ten novel complexes, [M(MAC)2]Cl2 I(a-e) and [M(BAC)2]Cl2 II(a-e), which were characterized by some physical properties and spectral methods, elemental analyses, molar conductance, and magnetic susceptibility, in addition to 1H NMR, FT-IR and UV-Vis. Spectroscopy proved that all metal complexes I(a-d) and II(a-d) have octahedral geometries, except those of copper complexes (Ie) and (IIe), which have square planer geometry (D4h). Following screening for biological activity against Gram-positive bacteria (Streptococcus pyogenes) and Gram-negative bacteria (Pseudomonas aeruginosa), the results of these novel complexes' investigations showed no effect except the complexes of [Zn(BAC)2]Cl2 II(a), [Zn(MAC)2]Cl2, I(a), [Mn(MAC)2]Cl2, I(b) and the ligand (MAC) are given promising data. KEY WORDS: 4-Amino antipyrine, Dithiocarbamates, Dithiocarbamate complexes, Biological activity Bull. Chem. Soc. Ethiop. 2025, 39(3), 447-457. DOI: https://dx.doi.org/10.4314/bcse.v39i3.5 Chemistry ; Divalent ; Medicinal chemistry ; Zinc ; Divalent metal ; Stereochemistry ; Organic chemistry ; Metal Synthesis and Characterization of Heterocyclic Compounds ; Organometallic Compounds Synthesis and Characterization ; Metal complexes synthesis and properties
Authors: Haider J. Al-Karagully | Mohammed Kamil Hadi
Source: African Journal of Biomedical Research (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Synthesis, Characterization, And Anti-Proliferative Activity Study of Novel Derivatives Of N'-(4h-Benzo[B] [1,4] Thiazine-3-Carbonyl) Benzene Sulfone-Hydrazides Against Lung and Breast Cancer Cell Line. A synthesis of new derivatives of N'-(4H-benzo[b][1,4]thiazine-3-carbonyl) benzene sulfonyl-hydrazides. Characterized the final compounds using spectroscopic methods to ensure their synthesis and evaluation of their activity against two cancer cell lines; A504 human lung cancer cell lines and MCF-7 a human breast cancer cell line with estrogen, progesterone, and glucocorticoid receptors (i.e hormone-dependent). The synthesized compounds (S1, S2, S3, and S4) were successfully synthesized with a good yeild%, characterized using FTIR spectra, 1H NMR, and 13C NMR, and the assessment of their anti-proliferative activity against the two cancer cell lines by MTT method. There was an indication that the final compounds were prepared with a good yield using a precise research method. All derivatives promise anticancer activity for both cell lines; their half-maximal inhibitory concentration (IC50 in µM) for compounds (S1-S4) was 10.2, 5.4, 19.9, and 4.1 respectively against the A504 cell line, and 4.5, 5.4, 6, 9.3, and 2.6 respectively against MCF-7 cell line. The inhibition % of the final compounds (S1-S4) was 82.3, 88.3, 70.5, and 82.3 against the A504 cell line; and 75.9, 74.5, 72.7, and 80.4 respectively against the MCF-7 cell line. All final compounds were synthesized in a precise method in good yields, characterized spectroscopically showing successful synthesis, assessment of their anti-proliferative activity showed all of them have good IC50 and Inhibition % against A504 and MCF-7 cell lines; the compound S4 had the most promising one (IC50=4.18 and 2.68µM; inhibition percentage=82.3% and 80.45%, against A504 and MCF-7 respectively), compared with erlotinib and tamoxifen as references authorized anticancer drugs. Thiazine ; Sulfone ; Benzene ; Lung cancer ; Cancer ; Medicine ; Chemistry ; Stereochemistry ; Pharmacology ; Internal medicine ; Medicinal chemistry ; Organic chemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Maksim Kuznetsov | Airat Valiev | Alex Aliper | Daniil Polykovskiy | Elena Tutubalina | Rim Shayakhmetov | Zulfat Miftahutdinov
Source: arXiv (Cornell University) (preprint, 2024-10-11)
Field: Materials Science — Materials Chemistry

nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder Recent advancements have integrated Language Models (LMs) into a drug discovery pipeline. However, existing models mostly work with SMILES and SELFIES chemical string representations, which lack spatial features vital for drug discovery. Additionally, attempts to translate chemical 3D structures into text format encounter issues such as excessive length and insufficient atom connectivity information. To address these issues, we introduce nach0-pc, a model combining domain-specific encoder and textual representation to handle spatial arrangement of atoms effectively. Our approach utilizes a molecular point cloud encoder for concise and order-invariant structure representation. We introduce a novel pre-training scheme for molecular point clouds to distillate the knowledge from spatial molecular structures datasets. After fine-tuning within both single-task and multi-task frameworks, nach0-pc demonstrates performance comparable with other diffusion models in terms of generated samples quality across several established spatial molecular generation tasks. Notably, our model is a multi-task approach, in contrast to diffusion models being limited to single tasks. Additionally, it is capable of processing point cloud-related data, which language models are not capable of handling due to memory limitations. These lead to our model having reduced training and inference time while maintaining on par performance. Encoder ; Task (project management) ; Computer science ; Cloud computing ; Point (geometry) ; Point cloud ; Operating system ; Artificial intelligence ; Engineering ; Mathematics ; Geometry ; Systems engineering Machine Learning in Materials Science ; Topic Modeling
Authors: Azerbaijan State Oil and Industry University
Source:  (article, 2024-10-14)
Field: Chemistry — Physical and Theoretical Chemistry

II INTERNATIONAL SCIENTIFIC-PRACTICAL SYMPOSIUM II International Scientific-Practical Symposium on "Standards for Sustainable Green Future" held at ASOIU On October 14, the II International Scientific-Practical Symposium titled "Standards for Sustainable Green Future," dedicated to the "Green World Solidarity Year" and "World Standards Day" started at Azerbaijan State Oil and Industry University (ASOIU). The symposium, organized by the Department of Instrumentation Engineering, took place in a hybrid format, both online and offline. The opening ceremony began with the playing of the National Anthem of Azerbaijan, followed by a minute of silence in honor of National Leader Heydar Aliyev and the martyrs who gave their lives for the territorial integrity of Azerbaijan. In his opening remarks at the symposium, ASOIU's Acting Rector, Associate Professor Vazeh Asgarov, welcomed the participants and highlighted the significance of President Ilham Aliyev's declaration of 2024 as the "Green World Solidarity Year" in the context of global climate change and the shift towards green energy. He also emphasized the importance of hosting this international scientific symposium at the university, especially ahead of a major event like COP29. Associate Professor Asgarov also spoke about ASOIU’s recent achievements in global university rankings and stressed the vital role of educating young people about sustainable green futures and the implementation of new standards. He reaffirmed the university’s support for all initiatives in this area. Ilham Bayramov, Director General of the Azerbaijan Institute of Standardization, and Khamis Seyranov, Director General of the Azerbaijan Institute of Metrology, discussed the country's efforts related to climate change, the implementation of new standards, and the role of youth in adopting green technologies. They praised the symposium’s work and its contribution to these important topics. Gülhan Arzu Yüzereroğlu, head of the Turkish Standards Institute (TSE) representative office in Azerbaijan, and Darkhan Erezhap, Dean of Satbayev University in Kazakhstan, joined the symposium online, noting the scientific significance of the event and its alignment with modern global challenges. Finally, Professor Lala Bakirova, head of the Department of Instrumentation Engineering, provided detailed information about the symposium. She emphasized its role in the exchange of experiences, the study and application of alternative energy resources, and the development of scientific potential and mutual collaboration. Computer science ; Engineering ethics ; Engineering History and advancements in chemistry ; Chemistry and Chemical Engineering ; Ukraine: War, Education, Health