jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Noah Hoffmann | Jonathan Schmidt | Silvana Botti | Miguel A. L. Marques
Source: arXiv (Cornell University) (preprint, 2023-03-06)
Field: Materials Science — Materials Chemistry

Transfer learning on large datasets for the accurate prediction of material properties Graph neural networks trained on large crystal structure databases are extremely effective in replacing ab initio calculations in the discovery and characterization of materials. However, crystal structure datasets comprising millions of materials exist only for the Perdew-Burke-Ernzerhof (PBE) functional. In this work, we investigate the effectiveness of transfer learning to extend these models to other density functionals. We show that pre-training significantly reduces the size of the dataset required to achieve chemical accuracy and beyond. We also analyze in detail the relationship between the transfer-learning performance and the size of the datasets used for the initial training of the model and transfer learning. We confirm a linear dependence of the error on the size of the datasets on a log-log scale, with a similar slope for both training and the pre-training datasets. This shows that further increasing the size of the pre-training dataset, i.e. performing additional calculations with a low-cost functional, is also effective, through transfer learning, in improving machine-learning predictions with the quality of a more accurate, and possibly computationally more involved functional. Lastly, we compare the efficacy of interproperty and intraproperty transfer learning. Transfer of learning ; Computer science ; Machine learning ; Artificial intelligence ; Graph ; Artificial neural network ; Quality (philosophy) ; Training set ; Transfer (computing) ; Data mining ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jiangcheng Xu | An Su | Panyi Huang | Wenbo Yu | Kui Du | Zhidan Fan | Bin Sun | Zhicheng Zhong | Can Jin | Weike Su
Source: ChemRxiv (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

PhotoCat: An Artificial Intelligence-Driven Synthesis Planning Platform for Photocatalysis Photocatalysis is becoming increasingly important in modern chemistry for efficient multicomponent one-pot synthesis. However, predicting the results of photocatalytic reactions using artificial intelligence remains challenging, mostly due to the insufficient number of photocatalytic reactions and the incomplete information on reaction conditions in existing reaction databases. In this study, we curated the Photocatalysis Database (PhotoCatDB), which consists of 6,523 photocatalytic reactions (of which 6,175 are multicomponent) containing reaction condition information such as photocatalysts, bases or acids, additives, and solvents. Before adding reaction conditions to the training data, the attention-based deep learning model PhotoCat pre-trained on USPTO and fine-tuned on PhotoCatDB had a Top-1 accuracy of 78.16%, which was 77.70% higher than the same model trained only on the USPTO database and 14.53% higher than the model fine-tuned by the photocatalytic reactions from Reaxys. After adding reaction conditions to the training data, the Top-1 accuracy of PhotoCat was further increased to 82.25%. In addition, the interpretability of the model was reflected in its attention weights, which can infer the model’s understanding of photocatalytic chemistry. Furthermore, five previously unreported photocatalytic reactions predicted by PhotoCat were successfully validated by wet-lab experiments, demonstrating the potential of the model in identifying and verifying novel photocatalysis reactions of real-world significance. Photocatalysis ; Interpretability ; Catalysis ; Reaction conditions ; Computer science ; Materials science ; Chemistry ; Nanotechnology ; Biochemical engineering ; Biological system ; Artificial intelligence ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Text and Document Classification Technologies
Authors: Hyunseung Kim | Hae-Yeon Choi | Dongju Kang | Won Bo Lee | Jonggeol Na
Source: arXiv (Cornell University) (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

Materials Discovery with Extreme Properties via Reinforcement Learning-Guided Combinatorial Chemistry The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop reinforcement learning-guided combinatorial chemistry, which is a rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown molecules with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better compounds than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1,315 of all target-hitting molecules and 7,629 of five target-hitting molecules out of 100,000 trials, whereas the probability distribution-learning models failed. Moreover, it has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking molecules and HIV inhibitors. Computer science ; Extrapolation ; Molecule ; Molecular descriptor ; Artificial intelligence ; Chemistry ; Machine learning ; Mathematics ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Jan P. Unsleber
Source: ChemRxiv (preprint, 2023-01-22)
Field: Materials Science — Materials Chemistry

Accelerating Reaction Network Explorations with Automated Reaction Template Extraction and Application The autonomous exploration of chemical reaction networks with first-principles methods generates vast amounts of data. Especially explorations that explore reaction networks autonomously and without tight constraints run the risk of exploring regions of reaction space that are not of interest. Consequently, the required human time for analysis and computer time for data generation can make these explorations unfeasible. Here, we show how an automated extraction of reaction templates can facilitate the transfer of chemical knowledge from existing data. This process significantly accelerates reaction network explorations and improves cost-effectiveness. The reaction templates allow for a simple steering mechanism in autonomous reaction network explorations, which we exemplify with a polymerization reaction. We discuss definitions of reaction templates and their generation based on molecular graphs. Graph matching and sub-graph searches based on molecular graphs and reaction templates may allow data clustering and new analyses of the generated reaction network. Template ; Computer science ; Graph ; Cluster analysis ; Theoretical computer science ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Hadeel A.K Hussien | Zainab A. K. Al-Messri
Source: Iraqi Journal of Science (article, 2023-02-28)
Field: Materials Science — Polymers and Plastics

Synthesis, Characterization and Evaluation of Some Meldrum's Acid Derivatives as Lubricant Additives Aminomethylene Meldrum’s acid derivatives were synthesized by a three-component, one-pot reaction of Meldrum's acid with triethyl orthoformate and different aromatic amines. The prepared compounds were characterized using Fourier transform infrared (FT-IR), nuclear magnetic resonance (1H NMR and 13C NMR) and evaluated as anti-corrosion and anti-rust additives by blending with base lubricating oil, according to the American Society of Testing and Materials (ASTM-D130 and ASTM-D665). The blends of the synthesized compounds with the base lubricating oil showed better anti-rust and anti-corrosion effects than the base oil Blank). Lubricant ; Meldrum's acid ; Corrosion ; Triethyl orthoformate ; Base (topology) ; Fourier transform infrared spectroscopy ; Rust (programming language) ; Chemistry ; Nuclear chemistry ; Base oil ; Organic chemistry ; Materials science ; Chemical engineering ; Composite material ; Catalysis ; Scanning electron microscope ; Mathematics Synthesis and properties of polymers ; Thermal and Kinetic Analysis ; Lubricants and Their Additives
Authors: Wei Wang | Fan Yang | Pan Zhang | Luo Zhi | Fangya Li | Jingjing Jiang | Jianbing Zhang | Wei Li | Aimei Liu | Caiqin Qin
Source: Coatings (article, 2023-04-26)
Field: Materials Science — Materials Chemistry

Preparation of Polyurethane-Modified Silicone Rubber Insulating Coating and Its Application in 10 kV Overhead Bare Wire Wrapping Room-temperature vulcanized rubber is an excellent polymer material with excellent electrical insulation and mechanical properties, which can be used for field insulation of high-voltage bare wire. In this paper, a room-temperature cured coating material was prepared from α,ω-dihydroxy polydimethylsiloxane (PDMS), polyurethane-modified silicone material (PU-Si), and Methyltris(methylethylketoxime) silane (MOS). The study showed that the coating made by adding PDMS (70 g), PU-Si (30 g), CaCO3 (90 g), MOS (7 g), KH792 (5 g), and Mg(OH)2 (30 g) had a surface drying time of 19.1 min, tensile strength of 3.2 Mpa, volume resistivity greater than 4 × 1014 Ω·cm, breakdown voltage greater than 20 kV/mm, and flame retardant performance of the FV-1 level. The comprehensive performance of the insulation material meets the national standard of “Aerial insulated cables for rated voltage of 10 kV” (GB/T14049-2008), while the insulation material has been successfully applied to the overhead line renovation project of the State Grid Hubei Electric Power Company (Wuhan, China). Materials science ; Composite material ; Vulcanization ; Coating ; Polydimethylsiloxane ; Polyurethane ; Silicone rubber ; Silicone ; Ultimate tensile strength ; Natural rubber High voltage insulation and dielectric phenomena ; Surface Modification and Superhydrophobicity ; Polymer composites and self-healing
Authors: Et al. Jagannath Jijaba Kadam
Source: International Journal on Recent and Innovation Trends in Computing and Communication (article, 2023-11-05)
Field: Materials Science — Materials Chemistry

Quantum Machine Learning Technique for Automatic Retrosynthetic Reaction Pathway Search Method Retrosynthetic analysis often involves evaluating many potential candidate reaction pathways and molecules at multiple stages of the reaction, resulting in complex retrosynthesis trees that need to be searched and parsed efficiently. Computational approaches could significantly aid the chemist in solving different aspects of the retrosynthesis problem, such as the graph-theoretic search methodologies for efficient tree traversal to identify feasible reaction pathways, dictionary-based methods to evaluate a large search space of precursors, and chemistry-driven heuristics to eliminate practically infeasible routes. In this research, a new single-step retrosynthesis prediction method of the Retro TRAE SMILES-based translation technique is proposed. Accordingly, quantum computing with tree-tensor network topology is presented to construct an automatic data-driven end-to-end retrosynthetic route planning system (Auto-Syn-Route), which is presented based on the heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The model is trained in an end-to-end and fully data-driven fashion. Unlike previous models translating the SMILES strings of reactants and products, a new way of representing a chemical reaction based on molecular fragments is introduced. It is demonstrated that the new approach yields better prediction results than current state-of-the-art computational methods. The new approach resolves the major drawbacks of existing retrosynthetic methods such as generating invalid SMILES strings. The proposed method is implemented using Python software. The proposed approach predicts highly similar reactant molecules with an accuracy of 68%. In addition, the proposed method yields more robust predictions than existing methods. However, the experiments demonstrate that the proposed scheme significantly improves the success rate of solving the retrosynthetic problem by 97% while maintaining the performance of the quantum tree tensor for predicting valid reactions. Retrosynthetic analysis ; Computer science ; Quantum ; Artificial intelligence ; Machine learning ; Chemistry ; Physics ; Stereochemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Shubhangi D. Shirsat | Manjusha Chakranarayan | Varenyam Achal | Mahendra Rai
Source: Research Square (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

Green synthesis of silver nanoparticles (AgNPs) using Alstonia scholaris extract: Evaluation of their antioxidant, enzyme inhibitory, antimicrobial, and antimutagenic activities through in vitro and in silico studies <title>Abstract</title> <italic>Alstonia scholaris</italic> is used as a traditional medicine for the treatment of various human and livestock diseases as it contains diverse types of biologically active compounds. In the current study silver nanoparticles (AgNPs) were synthesized by an eco-friendly method using <italic>Alstonia scholaris</italic> leaf extract (aqueous and methanolic). The synthesized nanoparticles (NPs) were characterized by UV-visible spectroscopy, Fourier transform infrared (FTIR), x-ray diffraction (XRD), and scanning electron microscopy (SEM). Green synthesized AgNPs were evaluated for their antioxidant, enzyme inhibition, antimicrobial, and antimutagenic potential. The AgNPs exhibited efficient antioxidant activities and considerable enzyme inhibition potential against α-amylase, and butyrylcholinesterase (BChE) enzymes. Meth-AgNPs showed strong antioxidant activity and high enzyme inhibitory effect as compared to Aq. AgNPs. Both AgNPs demonstrated significant antimicrobial activity. Synthesized AgNPs did not show any antimutagenic activity while Meth. AgNPs have demonstrated lower toxicities than previously reported AgNPs. Plant extracts are complex and contain several phytochemicals, which participate in the reduction and capping of AgNPs. Thus, despite the popularity of plant extract-mediated synthesis of AgNPs, the exact mechanism is still unclear. Here, we have used molecular simulation to reveal the interaction of phytochemicals and AgNP. Molecular dynamics simulation trajectory analysis showed that all phytochemicals- AgNP complexes reach an equilibrium within 100 ns. In addition, molecular docking experiments were performed to understand the mechanism of enzyme inhibition. The data from molecular simulation and dynamic support the capping of phytochemicals on the surface of AgNPs and is also responsible for the antioxidant, enzyme inhibition, and lower toxicity of AgNPs. Silver nanoparticle ; Chemistry ; Antioxidant ; Antimicrobial ; Biochemistry ; Organic chemistry ; Nanoparticle ; Nanotechnology ; Materials science Nanoparticles: synthesis and applications ; Moringa oleifera research and applications
Authors: Zixun Lan | Zuo Zeng | Binjie Hong | Zhenfu Liu | Fei Ma
Source: arXiv (Cornell University) (preprint, 2023-01-28)
Field: Materials Science — Materials Chemistry

RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning The reaction center consists of atoms in the product whose local properties are not identical to the corresponding atoms in the reactants. Prior studies on reaction center identification are mainly on semi-templated retrosynthesis methods. Moreover, they are limited to single reaction center identification. However, many reaction centers are comprised of multiple bonds or atoms in reality. We refer to it as the multiple reaction center. This paper presents RCsearcher, a unified framework for single and multiple reaction center identification that combines the advantages of the graph neural network and deep reinforcement learning. The critical insight in this framework is that the single or multiple reaction center must be a node-induced subgraph of the molecular product graph. At each step, it considers choosing one node in the molecular product graph and adding it to the explored node-induced subgraph as an action. Comprehensive experiments demonstrate that RCsearcher consistently outperforms other baselines and can extrapolate the reaction center patterns that have not appeared in the training set. Ablation experiments verify the effectiveness of individual components, including the beam search and one-hop constraint of action space. Retrosynthetic analysis ; Computer science ; Graph ; Node (physics) ; Identification (biology) ; Center (category theory) ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Engineering ; Stereochemistry ; Structural engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Computing and Algorithms
Authors: Latimah Bustillo | Tiago Rodrigues
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A focus on the use of real-world datasets for yield prediction The prediction of reaction yields remains a challenging task for machine learning (ML), given the vast search spaces and absence of robust training data. Wiest, Chawla <i>et al.</i> (https://doi.org/10.1039/D2SC06041H) show that a deep learning algorithm performs well on high-throughput experimentation data but surprisingly poorly on real-world, historical data from a pharmaceutical company. The result suggests that there is considerable room for improvement when coupling ML to electronic laboratory notebook data. Yield (engineering) ; Task (project management) ; Focus (optics) ; Machine learning ; Throughput ; Computer science ; Artificial intelligence ; Deep learning ; Real world data ; Training set ; Data science ; Engineering ; Materials science ; Systems engineering ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jingyi Hou | Zhen Dong
Source: Applied Sciences (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Learning Hierarchical Representations for Explainable Chemical Reaction Prediction This paper aims to propose an explainable and generalized chemical reaction representation method for accelerating the evaluation of the chemical processes in production. To this end, we designed an explainable coarse-fine level representation model that incorporates a small amount of easily available expert knowledge (i.e., coarse-level annotations) into the deep learning method to effectively improve the performances on reaction representation related tasks. We also developed a new probabilistic data augmentation strategy with contrastive learning to improve the generalization of our model. We conducted experiments on the Schneider 50k and the USPTO 1k TPL datasets for chemical reaction classification, as well as the USPTO yield dataset for yield prediction. The experimental results showed that our method outperforms the state of the art by just using a small-scale dataset annotated with both coarse-level and fine-level labels to pretrain the model. Computer science ; Generalization ; Representation (politics) ; Probabilistic logic ; Artificial intelligence ; Yield (engineering) ; Machine learning ; Scale (ratio) ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Tereza Jurczyková | František Kačík | Iveta Čabalová | Kateřina Hájková
Source: Polymers (article, 2023-01-22)
Field: Materials Science — Biomaterials

Evaluation of Selected Cellulose Macromolecular Properties after Its Chemical Treatment Using Size Exclusion Chromatography This work evaluates the effect of using selected inorganic chemicals as the main components of waterborne wood preservative systems on the degradation of the cellulose constituent in wood from model samples. The polymeric properties of cellulose and the homogeneity of the degradation process primarily reflect very well the degree of cellulose deterioration. Whatman papers, as pure cellulose model samples, were impregnated with 10 different 5 wt% solutions of inorganic salts and distilled water and consequently subjected to wet-thermal accelerated aging (T = 85 °C, RH = 65%, for 30 days). The samples were then derivatized to cellulose tricarbanilates (CTCs) through two different procedures (by precipitation in a methanol-water mixture/by evaporation of pyridine from the reaction mixture) and finally analyzed using size exclusion chromatography (SEC). Chemically treated and aged cellulose samples showed different changes in the degree of polymerization (DP) and polydispersity (PD) in terms of untreated non-aged standard caused by different ongoing degradation reactions, such as dehydration, hydrolysis, oxidation, and crosslinking. In general, the lowest degradation rate after treatment by chemicals and after accelerated aging was observed in samples treated by borates, NaCl, and ZnSO<sub>4</sub>·7H<sub>2</sub>O. The greatest depolymerization after treatment and after accelerated aging was caused by sulphates containing NH<sup>4+</sup>, Cu<sup>2+</sup>, and Fe<sup>3+</sup> cations, with aging by NH<sub>4</sub>Cl and (NH<sub>4</sub>)<sub>2</sub>HPO<sub>4</sub>-treated samples also leading to significant depolymerization. The higher DP values are linked to the precipitated method of CTC preparation, though not for chlorides and phosphates. PD is also generally higher in precipitated and aged samples and is heavily influenced by the presence of low molecular weight products. This paper brings new insights regarding the complex evaluation of the polymeric properties of degraded cellulose by considering all important factors affecting the sample and the analysis itself through the use of statistics. From the statistical point of view, the influences of all factors (solution, aging, method) and their interactions (except aging*method) on DP are statistically significant. The influence of the sample processing method used for analysis of the desired results becomes important mainly in practice. This work recommends the evaporation method for more accurate description of more degraded cellulose. Cellulose ; Depolymerization ; Degree of polymerization ; Hydrolysis ; Distilled water ; Dispersity ; Chemistry ; Size-exclusion chromatography ; Polymerization ; Nuclear chemistry ; Chromatography ; Polymer ; Polymer chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Wood Treatment and Properties