jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Qin Song | Santiago Miret | Huan Zhang | Bang Liu
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science We propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct), which we then apply to finetune a LLaMa-based language model targeted for materials science (HoneyBee). MatSci-Instruct helps alleviate the scarcity of relevant, high-quality materials science textual data available in the open literature, and HoneyBee is the first billion-parameter language model specialized to materials science. In MatSci-Instruct we improve the trustworthiness of generated data by prompting multiple commercially available large language models for generation with an Instructor module (e.g. Chat-GPT) and verification from an independent Verifier module (e.g. Claude). Using MatSci-Instruct, we construct a dataset of multiple tasks and measure the quality of our dataset along multiple dimensions, including accuracy against known facts, relevance to materials science, as well as completeness and reasonableness of the data. Moreover, we iteratively generate more targeted instructions and instruction-data in a finetuning-evaluation-feedback loop leading to progressively better performance for our finetuned HoneyBee models. Our evaluation on the MatSci-NLP benchmark shows HoneyBee's outperformance of existing language models on materials science tasks and iterative improvement in successive stages of instruction-data refinement. We study the quality of HoneyBee's language modeling through automatic evaluation and analyze case studies to further understand the model's capabilities and limitations. Our code and relevant datasets are publicly available at https://github.com/BangLab-UdeM-Mila/NLP4MatSci-HoneyBee. Computer science ; Benchmark (surveying) ; Construct (python library) ; Trustworthiness ; Language model ; Process (computing) ; Soundness ; Code (set theory) ; Quality (philosophy) ; Artificial intelligence ; Machine learning ; Data science ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Benjamin P. MacLeod | Fraser G. L. Parlane | Curtis P. Berlinguette
Source: MRS Bulletin (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

How to build an effective self-driving laboratory Reuse ; Self driving ; Automation ; Computer science ; Laboratory automation ; Systems engineering ; Risk analysis (engineering) ; Engineering ; Transport engineering ; Mechanical engineering ; Business Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Riley J. Hickman | P.N. Parakh | Austin Cheng | Qianxiang Ai | Joshua Schrier | Matteo Aldeghi | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2023-05-17)
Field: Materials Science — Materials Chemistry

Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation of various algorithms on chemistry and materials science optimization tasks, we previously introduced OLYMPUS (Mach. Learn.: Sci. Technol. 2, 035021, 2021), a Python package providing a consistent and easy-to-use interface to numerous optimization algorithms and benchmark datasets. While the original package was limited to continuous parameters and single objectives, in this work we expand OLYMPUS' capabilities to include mixed (continuous, discrete, and categorical) parameter types and multiple objectives. Several experiment planning algorithms already contained in OLYMPUS are extended to handle categorical and discrete parameter types, and five additional planners are implemented (23 in total). We also provide 23 additional benchmark datasets taken from the chemistry and materials science literature (33 in total), covering a wide range of research areas, from chemical reaction optimization to materials manufacturing. Finally, the visualization capabilities of OLYMPUS are enhanced to allow for easy inspection of the results, and the core functionality of the package is embedded in a Streamlit web application for code-free usage. We demonstrate how OLYMPUS enables researchers to rapidly benchmark different optimization strategies and gain insight into their behavior by focusing on two case studies: the optimization of a Suzuki-Miyaura cross-coupling reaction with categorical reaction conditions, and the multi-objective optimization of redox-active materials. The updated OLYMPUS package provides practitioners with a large suite of tools to efficiently benchmark and analyze experiment planning algorithms on mixed-parameter and multi-objective optimization tasks. Benchmarking ; Benchmark (surveying) ; Computer science ; Python (programming language) ; Categorical variable ; Optimization problem ; Algorithm ; Machine learning ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: S. Hessam M. Mehr | Dario Caramelli | Leroy Cronin
Source: Proceedings of the National Academy of Sciences (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Digitizing chemical discovery with a Bayesian explorer for interpreting reactivity data Interpreting the outcome of chemistry experiments consistently is slow and frequently introduces unwanted hidden bias. This difficulty limits the scale of collectable data and often leads to exclusion of negative results, which severely limits progress in the field. What is needed is a way to standardize the discovery process and accelerate the interpretation of high-dimensional data aided by the expert chemist's intuition. We demonstrate a digital Oracle that interprets chemical reactivity using probability. By carrying out >500 reactions covering a large space and retaining both the positive and negative results, the Oracle was able to rediscover eight historically important reactions including the aldol condensation, Buchwald-Hartwig amination, Heck, Mannich, Sonogashira, Suzuki, Wittig, and Wittig-Horner reactions. This paradigm for decoding reactivity validates and formalizes the expert chemist's experience and intuition, providing a quantitative criterion of discovery scalable to all available experimental data. Chemical space ; Intuition ; Computer science ; Oracle ; Chemistry ; Data science ; Combinatorial chemistry ; Drug discovery ; Epistemology ; Philosophy ; Programming language Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Computational Drug Discovery Methods
Authors: Xiangyu Zhang | Zezhao Xu | Zidi Wang | Huiyu Liu | Yingbo Zhao | Shan Jiang
Source: APL Materials (article, 2023-06-01)
Field: Chemistry — Inorganic Chemistry

High-throughput and machine learning approaches for the discovery of metal organic frameworks Metal-organic frameworks (MOFs) are promising nanoporous materials with diverse applications. Traditional material discovery based on intensive manual experiments has certain limitations on efficiency and effectiveness when faced with nearly infinite material space. The current situation offers an opportunity for high-throughput (HT) and machine learning (ML) approaches, including computational and experimental methods, as they have greatly improved the efficiency of MOF screening and discovery and have the capacity to deal with the enormous growth of data. In this review, we discuss the research progress in HT computation and experiments and their effect on MOF screening and discovery. We also highlight how ML-based approaches and the integration of HT methods with ML algorithms accelerate MOF design. In addition, we provide our insights on the future capability of data-driven techniques for MOF discovery, despite facing some knowledge gaps as an obstacle. Nanoporous ; Throughput ; Metal-organic framework ; Computer science ; Chemical space ; High-throughput screening ; Obstacle ; Computation ; Nanotechnology ; Knowledge extraction ; Key (lock) ; Artificial intelligence ; Biochemical engineering ; Machine learning ; Materials science ; Data science ; Drug discovery ; Engineering ; Bioinformatics ; Wireless Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Eric Schwenker | Weixin Jiang | Trevor Spreadbury | Nicola Ferrier | Oliver Cossairt | Maria K. Y. Chan
Source: Patterns (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy dataset containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data. Computer science ; Annotation ; Natural language processing ; Artificial intelligence ; Information retrieval ; Construct (python library) ; Word (group theory) ; Variety (cybernetics) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Shraddha S. Ambade | Vivek Kumar Gupta | Ritesh Bhole | Pramod Khedekar | Rupesh V. Chikhale
Source: Preprints.org (review, 2023-09-13)
Field: Chemistry — Organic Chemistry

A Review on Five and Six-Membered Heterocyclic Compounds Targeting the Penicillin-Binding Protein 2 (PBP2A) of Methicillin-Resistant &lt;em&gt;Staphylococcus aureus&lt;/em&gt; (MRSA) Staphylococcus aureus is a common pathogen in human. Methicillin resistant Staphylococcus aureus (MRSA) infection poses a big and perplexing difficulty in terms of therapy. The acquisition of the non-native gene PBP2a, which has a decreased tolerance for &amp;beta;-lactam antibiotics, frequently confers resistance. PBP2a has a less attraction for methicillin and it helps bacteria to continue peptidoglycan biosynthesis, and it is cell wall&amp;rsquo;s core component in bacteria. So, even in the presence of methicillin or any other antibiotic, bacteria develop resistance. Due to resistance-causing genes, S. aureus becomes MRSA. The main premise of the resistance mechanism is well understood. The current demand for novel antibiotics is legitimate in the face of therapeutic concerns posed by resistant micro-organisms. The emphasis of this review is on PBP2a scaffolds and the different screening approaches used to find PBP2a inhibitors. Penicillin, Cephalosporins, Pyrazole-Benzimidazole based derivatives, Oxadiazole containing derivatives, non-&amp;beta;-lactam allosteric inhibitors, 4-(3H)-Quinazolinones, Pyrrolylated chalcone, Bis-2-Oxoazetidinyl macrocycle (&amp;beta;-lactam antibiotics with 1, 3-Bridges), Macrocycle-embedded &amp;beta;-lactams as novel inhibitors, Pyridine-Coupled Pyrimidinones, novel Naphthalimide corbelled aminothiazoximes, non-covalent inhibitors, Investigational-&amp;beta;-lactam antibiotics Carbapenem, novel Benzoxazole derivatives, Pyrazolylpyridine analogues, and other miscellaneous classes of scaffolds for PBP2a are also represented as well as with their biological activity is discussed. The penicillin-binding protein is also discussed, which is the crucial target for the cell wall of MRSA. Various aspects of PBP2a, the cell wall of bacteria, peptidoglycans, different crystal structures of PBP2a, synthetic routes for PBP2a inhibitors, and future perspectives of MRSA inhibitors are also enumerated. Penicillin binding proteins ; Microbiology ; Staphylococcus aureus ; Antibiotics ; Penicillin ; Methicillin-resistant Staphylococcus aureus ; Antibiotic resistance ; Chemistry ; Bacteria ; Biology Synthesis and biological activity ; Synthesis of β-Lactam Compounds ; Computational Drug Discovery Methods
Authors: Sofia Arevalo | Markus J. Buehler
Source: MRS Bulletin (article, 2023-10-25)
Field: Materials Science — Biomaterials

Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials Abstract Biological systems generate a wealth of materials, and their design principles inspire and inform scientists from a broad range of fields. Nature often adapts hierarchical multilevel material architectures to achieve a set of properties for specific functions, providing templates for difficult tasks of understanding the intricate interplay between structure–property–function relationships. While these materials tend to be complex and feature intricate functional interactions across scales, molecular-based multiscale modeling, machine learning, and artificial intelligence combined with experimental approaches to synthesize and characterize materials have emerged as powerful tools for analysis, prediction, and design. This article examines materiomic graph-based modeling frameworks for assisting researchers to pursue materials-focused studies in a biological context, and provides an overview of methods that can be applied to bottom-up manufacturing, including a historical perspective of bioinspired materials research. Through the advent of novel modeling architectures and diverse systems from nature, there is potential to develop materials with improved properties. Graphical abstract Computer science ; Context (archaeology) ; Data science ; Function (biology) ; Perspective (graphical) ; Template ; Artificial intelligence ; Management science ; Engineering Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Machine Learning in Materials Science
Authors: Muhammad Uzair | Shamsa Kanwal | M.I. Khan | Wajeehah Shahid | Badriah S. Almutairi | Muhammad Amin | Nawal Ansar | Samiah Shahid | Jeong Ryeol Choi
Source: ACS Omega (article, 2023-10-31)
Field: Chemistry — Inorganic Chemistry

Effect of Mn Doped on Structural, Optical, and Dielectric Properties of BiFe<sub>1–<i>x</i></sub>Mn<sub><i>x</i></sub>O<sub>3</sub> for Efficient Antioxidant Activity Manganese-doped bismuth ferrites were synthesized using the coprecipitation method with the green extract <i>Azadirachta indica</i>. Our incorporation of the transition element, manganese, into bismuth ferrites tackles the challenge of increased leakage current often observed in intrinsic bismuth ferrites. We gained key insights through a comprehensive examination of the structural, dielectric, and optical properties of these materials, utilizing Fourier transform infrared spectroscopy (FTIR), impedance spectroscopy, and UV-visible spectroscopy, respectively. The formation of an octahedral geometry was confirmed using the FTIR technique. UV-visible spectroscopy indicated that 2% Mn doping is optimal, while we obtained a low band gap energy (2.21 eV) and high refractive index (3.010) at this amount of doping. The manufactured materials exhibited the typical ferrite-like dielectric response, that is, the dielectric parameter gradually decreased as the frequency increased and then stayed constant in the high-frequency range. Using the diphenylpicrylhydrazyl (DPPH) free radical assay, we also examined the antioxidant activity of bismuth ferrites. We concluded that among different Mn-doped BiFeMnO<sub>3</sub>-based nanomaterials, the 2 wt % Mn-doped BiFeMnO<sub>3</sub> shows the highest antioxidant activity. This finding substantiates the efficacy of the optimized material with regard to its potent antioxidant activity, positioning it as a promising candidate for potential biomedical applications. Fourier transform infrared spectroscopy ; Bismuth ; Materials science ; Dielectric ; Coprecipitation ; Doping ; Manganese ; Analytical Chemistry (journal) ; Spectroscopy ; Bismuth ferrite ; Nuclear chemistry ; Inorganic chemistry ; Chemistry ; Chemical engineering ; Optoelectronics ; Metallurgy ; Organic chemistry Pigment Synthesis and Properties ; Multiferroics and related materials ; Transition Metal Oxide Nanomaterials
Authors: Junfeng Wang | Guohui Li
Source: Smart Molecules (article, 2023-10-30)
Field: Chemistry — Inorganic Chemistry

Full‐scale modeling of chemical experiments Computational chemistry methods are playing an increasingly pivotal role in chemical experiments. From quantum chemistry simulations to finite element simulations, researchers can always find an appropriate simulation method to elucidate the specific mechanisms at a certain resolution scale. However, in organic or inorganic synthesis, the synthesis mechanisms span multiple spatial and temporal scales of chemical experiments. Furthermore, the intricate nature of these mechanisms renders it impossible for any single simulation method to provide a comprehensive depiction of the entire process. In this perspective, using zeolite and polymer synthesis simulations as examples, we stress the significance of full-scale modeling techniques for chemical experiments and urge the corresponding sophisticated simulation platform. Scale (ratio) ; Computer science ; Quantum chemical ; Process (computing) ; Chemistry ; Molecule ; Physics ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: David F. Nippa | Kenneth Atz | Alex T. Müller | Jens Wolfard | Clemens Isert | Martin Binder | Olivier Scheidegger | David B. Konrad | Uwe Grether | Rainer E. Martin | Gisbert Schneider
Source: Communications Chemistry (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp<sup>3</sup>-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening. Computer science ; Combinatorial chemistry ; Virtual screening ; Alkylation ; In silico ; Chemical database ; Process (computing) ; Throughput ; Chemical space ; Biochemical engineering ; Construct (python library) ; High-throughput screening ; Drug discovery ; Machine learning ; Chemistry ; Organic chemistry ; Engineering ; Biochemistry ; Catalysis Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods ; Computational Drug Discovery Methods
Authors: Lijun Gao | Ying Li | Wensheng Fu | Liming Zhou | Shao‐Ming Fang
Source: Polymers (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

Preparation and Performance of Silicone Rubber Composites Modified by Polyurethane Silicone rubber composites with good comprehensive properties modified with polyurethane were obtained through mixing and vulcanizing methods. Firstly, the polyurethane prepolymer with double bonds was prepared by polytetrahydrofuran glycol (PTMG, Mn = 1000), isophorone diisocyanate (IPDI), and 2-hydroxyethyl methacrylate (HEMA). The prepolymer was then added to the silicone rubber compounds to prepare silicone rubber composites, combining the excellent properties of polyurethane with the silicone rubber materials. The effects of polyurethane content on the mechanical properties, insulation, hydrophobicity, thermal stability, and flame retardancy of composites were studied in detail. The results showed that the silicone rubber composites not only have good hydrophobicity, thermal stability and flame retardant properties, but the addition of polyurethane significantly improves the tensile strength at room and low temperatures and the volume resistivity of the materials. The tensile strength increased by 32.5%, and the volume resistivity nearly doubled. The excellent electrical insulation, high hydrophobicity and good mechanical properties make the silicone rubber composites appropriate for use in the field of polymeric house arresters. Materials science ; Composite material ; Polyurethane ; Prepolymer ; Silicone rubber ; Ultimate tensile strength ; Vulcanization ; Thermal stability ; Silicone ; Isophorone diisocyanate ; Natural rubber ; Chemical engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Flame retardant materials and properties
Authors: Jiamin Huang | Yang Gao | Yanhong Chang | Jiajie Peng | Yadong Yu | Bin Wang
Source: Advanced Science (review, 2023-11-09)
Field: Materials Science — Materials Chemistry

Machine Learning in Bioelectrocatalysis At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction. Computer science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Innovative Microfluidic and Catalytic Techniques Innovation