jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huynh Ngoc Chau | Cu Huy Duc | Nguyen Minh Quang
Source: Tạp chí Khoa học Đại học Công Thương. (article, 2024-08-28)
Field: Chemistry — Organic Chemistry

DISCOVERY OF NOVEL METAL-THIOSEMICARBAZONE COMPLEXES USING IN SILICO MODELS IN ENVIRONMENTAL ANALYSIS Twenty novel metal-thiosemicarbazone complexes were discovered by in silico techniques. The stability constants (logb12) of complexes were also predicted by using the quantitative structure and property relationships (QSPR) models. The models were created using the multiple linear regression (MLR) and artificial neural network (ANN) approaches. The structure characteristics of complexes consist of molecular and quantum properties. The published literature is used to collect the stability constants with experimental parameters. The best model, MLR2-QSPR (k = 4), consisted of molecular descriptors such as S6, Dipole, xv1, and N’4. Statistical metrics such as R2train = 0.913, Q2LOO = 0.903, and SE = 0.408 were used to validate the quality of this MLR-QSPR. The statistical data for the ANN4-QSPR model I(4)-HL(10)-O(1) were also reported: R2train = 0.972, Q2test = 0.975, and R2CV = 0.985. In addition, the work used the results of variables from the QSPR models for developing new thiosemicarbazone ligands and based-ligand complexes. As a result, novel metal-thiosemicarbazone complexes were newly outlined and predicted the stability constants by two developed QSPR models. The results obtained from models can be applied to develop novel chemicals that can be administrated for use in analytical chemistry and environmental evaluation monitoring. In silico ; Semicarbazone ; Metal ; Key (lock) ; Computational biology ; Chemistry ; Computer science ; Environmental chemistry ; Biology ; Stereochemistry ; Biochemistry ; Organic chemistry ; Computer security Inorganic and Organometallic Chemistry
Authors: Gustina Alfa Trisnapradika | Muhamad Akrom
Source: Journal of Multiscale Materials Informatics (article, 2024-04-29)
Field: Chemistry — Inorganic Chemistry

Comparison of Ridge and Kernel Ridge Models in Predicting Thermal Stability of Zn-MOF Catalysts This study investigates machine learning-based quantitative structure-property relationship (QSPR) models for predicting the thermal stability of zinc metal-organic frameworks (Zn-MOF). Utilizing a dataset comprising 151 Zn-MOF compounds with relevant molecular descriptors, ridge (R) and kernel ridge (KR) regression models were developed and evaluated. The results demonstrate that the R model outperforms the KR model in terms of prediction accuracy, with the R model exhibiting exceptional performance (R² = 0.999, RMSE = 0.0022). While achieving high accuracy, opportunities for further improvement exist through hyperparameter optimization and exploration of polynomial functions. This research underscores the potential of ML-based QSPR models in predicting the thermal stability of Zn-MOF compounds and highlights avenues for future investigation to enhance model accuracy and applicability in materials science. Quantitative structure–activity relationship ; Ridge ; Hyperparameter ; Stability (learning theory) ; Kernel (algebra) ; Mean squared error ; Regression ; Computer science ; Mean absolute error ; Machine learning ; Biological system ; Mathematics ; Geology ; Statistics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Thermal and Kinetic Analysis
Authors: Ezequiel López‐Rubio
Source: ChemRxiv (preprint, 2024-11-14)
Field: Chemistry — Physical and Theoretical Chemistry

Instruction set and language for chemical nomenclature Processing of chemical information by computational intelligence methods faces the challenge of the structural complexity of molecular graphs. These graphs are not amenable to being represented in a suitable way for such methods. The most popular representation is the SMILES notation standard. However, it comes with some limitations such as the abundance of non-valid strings and the fact that similar strings often represent very different molecules. In this work, a completely different approach to chemical nomenclature is presented. A reduced instruction set is defined, and the language of all strings that are sequences of such instructions is considered. All strings of this language are valid, i.e., each string represents a molecule. Moreover, slight changes in a string usually correspond to small modifications in the represented molecule. Therefore, this approach is appropriate for its use if state-of-the-art computational intelligence systems for chemical information processing, including deep learning models. Nomenclature ; Set (abstract data type) ; Computer science ; Natural language processing ; Linguistics ; Programming language ; Philosophy ; Biology ; Botany History and advancements in chemistry
Authors: Hyungro Lee | Sayan Ghosh
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

pnnl/emp-gnn Efficient Graph Neural Network for Predicting Molecular Properties software can compare the prediction quality with ab initio DFT results reported by the high-performance state-of-theart NWChem quantum chemistry package [1] through Mean Absolute Error (MAE) obtained by the fitting between DFT and model predictions i.e, MPNN [2]. We also demonstrated the performance benefits by grouping large molecules by atom sizes, and executing GNN models on different types of resources. Since the training times depend on the number of atoms, we demonstrate the impact of distributing the workloads on two GPUs with varying capabilities (e.g., NVIDIA A100 vs. GeForce RTX 2080 Ti) to optimize the efficiency. We are at the precipice of broad adoption of GNNs for molecular property prediction tasks; hence, our work is timely by comparing model prediction against classical approaches with the intent of providing initial screening for specific classes of molecules. Computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Theodoros Anagnostopoulos | Chara Kottara | Ioakeim Spiliopoulos
Source: International Journal of Science and Research Archive (article, 2024-10-24)
Field: Chemistry — Analytical Chemistry

Assessing statistical neural networks potentiality to distinguish PDO Kalamata and Molaoi olive oil varieties Several regional areas in Greece produce high quality olive oil by cultivating certain varieties. Olive oil varieties of Kalamata and Molaoi are of special interest, since they produce extra virgin olive oil. Concretely, Kalamata is a city located in southwestern Greece. Intuitively, Molaoi is a town located in southeastern Greece. Subsequently, both geographic locations are known for their famous olive oil quality. Continually, Protected Designation of Origin (PDO) Kalamata olive oil, established by Council regulation (EC) No 510/2006, is considered an exceptional extra virgin olive oil variety. Specifically, there is a need to distinguish PDO Kalamata olive oil from other olive oil varieties is Greece such as the Molaoi olive oil, since PDO Kalamata olive oil is the main variety exported in the global olive oil market. Distinguishing PDO Kalamata from Molaoi olive oil is possible by incorporating statistical neural networks. Concretely, applying neural network experimentation enables differentiation between variations of certain chemical characteristics observed in certain geographic locations of Greece. In this paper, we use statistical neural networks to distinguish the geographical origin of PDO Kalamata olive oil compared with Molaoi olive oil based on synchronous excitation−emission fluorescence spectroscopy of provided olive oils samples evaluated in the chemical laboratory. Evaluation based on certain experimentation phase and subsequent data visualization of the adopted statistical neural networks are promising for distinguishing the samples of PDO Kalamata olive oil with high values of prediction accuracy. Such ability enables olive oil industry to assess extra virgin olive oil profitable potentiality in global market. Olive oil ; Artificial neural network ; Statistical analysis ; Biology ; Artificial intelligence ; Mathematics ; Computer science ; Statistics ; Food science Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis
Authors: Nuralia Syahida Hashim | Banu Poobalan | Nor Farhani Zakaria | N. Manikandan | Safizan Shaari
Source: AIP conference proceedings (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The extraction of gate oxide thickness on silicon carbide wide-bandgap material using thermal oxidation based regression techniques for high-power applications This research demonstrated that the oxide thickness for SiC materials which are processed in wet and dry oxidation conditions for high voltage applications can be estimated using regression methods, mainly linear and polynomial regression. The regression models are validated using Mean Absolute Percentage Error (MAPE) and R-squared approaches. In addition, this work calculates and presents the estimation of the gate oxide thickness that correlates to gate voltage ranges for high voltage applications. This work is also aimed at investigating the role of relevant species on the gate oxide formation of SiC during conventional (dry and wet) thermal oxidation process conditions. The outcome of the study revealed that the conventional wet oxidation method provided a thicker gate oxide with a prolonged process duration and temperature than the dry oxidation method. From the observation of oxide thicknesses against durations with varying temperatures, it was shown that linear and polynomial regression techniques are suitable to be implied for gate oxide thickness extraction, with better accuracy observed in the polynomial technique. By utilizing the models and formulations generated using regression approaches, this work is expected to aid researchers in the fabrication industry in selecting the desired process conditions for oxide thickness growth, with the exception of real thermal oxidation process conditions. Hence, it ultimately saves the processing time, material, and cost of the power semiconductor device fabrication technology, mainly for high voltage applications. The outcome of this work is also intended to be implemented into semiconductor fabrication tools in line with the development of machine learning technology. Fabrication ; Materials science ; Thermal oxidation ; Silicon carbide ; Gate oxide ; Mean absolute percentage error ; Oxide ; Voltage ; Polynomial regression ; Polynomial ; Threshold voltage ; Linear regression ; Computer science ; Electrical engineering ; Machine learning ; Engineering ; Metallurgy ; Mathematics Ga2O3 and related materials ; Silicon Carbide Semiconductor Technologies ; Semiconductor materials and devices
Authors: Saed Alrawash | Matthew F. Hale | Barry Lennox | Malcolm J. Joyce | Andrew West | Minoru Watanabe | Zhongming Zhang | Michael D. Aspinall
Source: EPJ Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Fuel Debris Location in Fukushima Nuclear Power Plant using Machine Learning Accurate fuel debris location is crucial part of the decommissioning of the Fukushima Nuclear Power plants. Conventional methods face challenges due to extreme radiation and complex structure of the materials involved. In this study, we propose a novel approach utilising neutron detection and machine learning to estimate fuel material location. Geant4 simulations and pythonTM scripts have been used to generate a comprehensive dataset to train a machine learning model using MATLAB’s regression learner. A Gaussian Process Regression model was chosen for training and prediction. The results show excellent prediction performance to estimate the corium thickness effectively and to locate the nuclear fuel material with a mean square error (MSE) of 0.01. By combining the machine learning with nuclear simulation codes, this promises to enhance the nuclear decommissioning efforts to retrieve nuclear fuel debris. Nuclear power plant ; Debris ; Nuclear engineering ; Fukushima Nuclear Accident ; Environmental science ; Nuclear power ; Engineering ; Nuclear physics ; Physics ; Meteorology Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Chonghuan Zhang | Qianghua Lin | Hao Deng | Yaxian Kong | Zhunzhun Yu | Kuangbiao Liao
Source: ChemRxiv (preprint, 2024-08-16)
Field: Materials Science — Materials Chemistry

Intermediate Knowledge Enhanced the Performance of N-Acylation Yield Prediction Model Acylation is an important reaction widely applied in medicinal chemistry. However, yield optimization remains a challenging issue due to the broad conditions space. Recently, accurate condition recommendations via machine learning have emerged as a novel and efficient method to achieve the desired transformations without a trial-and-error process. Nonetheless, accurately predicting yields is challenging due to the complex relationships involved. Herein, we present our strategy to address this problem. Two steps were taken to ensure the quality of the dataset. First, we skillfully selected substrates to ensure diversity and representativeness. Second, experiments were conducted using our in-house high-throughput experimentation (HTE) platform to minimize the influence of human factors. Additionally, we proposed an intermediate knowledge-embedded strategy to enhance the model’s robustness. The performance of the model was first evaluated at three different levels—random split, partial substrate novelty, and full substrate novelty. All model metrics in these cases improved dramatically, achieving an R2 of 0.89, MAE of 6.1%, and RMSE of 8.0%. Moreover, the generalization of our strategy was assessed using external datasets from reported literature. The prediction error for nine reactions among 30 was less than 5%, and the model was able to identify which reaction in a reaction pair with a reactivity cliff had a higher yield. In summary, our research demonstrated the feasibility of achieving accurate yield predictions through the combination of HTE and embedding intermediate knowledge into the model. This approach also has the potential to facilitate other related machine learning tasks. Computer science ; Novelty ; Robustness (evolution) ; Representativeness heuristic ; Embedding ; Machine learning ; Artificial intelligence ; Generalization ; Yield (engineering) ; Mathematics ; Chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Mao Su | Han Xu | Taoyong Cui | Chenyu Tang | D.C. Zhou | Yuqiang Li | Xiang Gao | Xingao Gong | Wanli Ouyang | Shufei Zhang
Source: Research Square (preprint, 2024-08-22)
Field: Materials Science — Materials Chemistry

Evidential Deep Learning for Interatomic Potentials Computer science ; Workflow ; Molecular dynamics ; Set (abstract data type) ; Machine learning ; Computation ; Artificial intelligence ; Sampling (signal processing) ; Uncertainty quantification ; Work (physics) ; Scale (ratio) ; Data mining ; Algorithm ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Ahmet Yıldız | Tarık Küçükdeniz | Merve İlgar | Razium Ali Soomro | Mohamed E. El Sayed | Selcan Karakuş
Source: Journal of Polymers and the Environment (article, 2024-04-16)
Field: Materials Science — Biomaterials

Integrated Data Modeling and Biological Evaluation of PEGylated Konjac Gum-Rosin Pentaerythritol Polymeric Nanocomposites for Enhanced Antimicrobial Performance Abstract The increasing prevalence of antibiotic-resistant pathogens necessitates the development of novel antimicrobial agents. Herein, PEGylated konjac gum-supported rosin pentaerythritol nanocomposites (KG/PEG/RE PNCs) were synthesized using an environmentally friendly sonochemical method, aiming to explore their potential antibacterial and antifungal properties against a range of pathogens, including Candida albicans, Escherichia coli, Pseudomonas aeruginosa, Aspergillus brasiliensis, and Staphylococcus aureus . An elaborate investigation into the rheological properties of these PNCs highlighted the dependence of viscosity on synthesis parameters such as RE concentration, sonication time, and KG/RE blend ratio with the Higiro model validated as a suitable mathematical model for defining the intricate relationship between experimental and resulting viscosity of PNCs. The integration of machine learning (ML), particularly polynomial regression, enabled the modeling of the complex dynamics influencing PNC viscosity, thus advancing comprehension of PNCs behavior in relation to the synthesis parameters. The modeling facilitated precise formulation to predict PNC viscosity with high accuracy, as confirmed by a mean squared error (MSE) of 3.81 and an R 2 of 0.993. Moreover, the PNCs demonstrated broad-spectrum antimicrobial activity, reaching an inhibition plateau during the first week, confirming its efficacy as a versatile antibacterial and antifungal agent. Combining advanced data modeling techniques with biological assessments, this integrated approach represents a step forward in understanding and optimizing polymeric nanostructures. Materials science ; Pentaerythritol ; Rosin ; Antimicrobial ; Nanocomposite ; Viscosity ; Nanotechnology ; Organic chemistry ; Chemistry ; Composite material Nanocomposite Films for Food Packaging ; Polysaccharides Composition and Applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Tianze Zheng | Ailun Wang | Xu Han | Yu Xia | Xingyuan Xu | Jiawei Zhan | Yu Liu | Yang Chen | Zhi Wang | Xiaojie Wu | Sheng Gong | Wen Yan
Source: arXiv (Cornell University) (preprint, 2024-08-23)
Field: Materials Science — Materials Chemistry

Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage A force field is a critical component in molecular dynamics simulations for computational drug discovery. It must achieve high accuracy within the constraints of molecular mechanics' (MM) limited functional forms, which offers high computational efficiency. With the rapid expansion of synthetically accessible chemical space, traditional look-up table approaches face significant challenges. In this study, we address this issue using a modern data-driven approach, developing ByteFF, an Amber-compatible force field for drug-like molecules. To create ByteFF, we generated an expansive and highly diverse molecular dataset at the B3LYP-D3(BJ)/DZVP level of theory. This dataset includes 2.4 million optimized molecular fragment geometries with analytical Hessian matrices, along with 3.2 million torsion profiles. We then trained an edge-augmented, symmetry-preserving molecular graph neural network (GNN) on this dataset, employing a carefully optimized training strategy. Our model predicts all bonded and non-bonded MM force field parameters for drug-like molecules simultaneously across a broad chemical space. ByteFF demonstrates state-of-the-art performance on various benchmark datasets, excelling in predicting relaxed geometries, torsional energy profiles, and conformational energies and forces. Its exceptional accuracy and expansive chemical space coverage make ByteFF a valuable tool for multiple stages of computational drug discovery. Expansive ; Parametrization (atmospheric modeling) ; Classical mechanics ; Molecular mechanics ; Space (punctuation) ; Force field (fiction) ; Physics ; Mathematics ; Mechanics ; Statistical physics ; Theoretical physics ; Molecular dynamics ; Computer science ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Polymer Synthesis and Characterization
Authors: Yixuan Hu | Yumeng Zhang | Bo Wen | Fu‐Zhi Dai
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Metals and Alloys

Grain Boundary Engineering in Nickel-Rich Cathode: A Combination of High-Throughput First-Principle and Interpretable Machine Learning Study Throughput ; Nickel ; Cathode ; Grain boundary ; Materials science ; Boundary (topology) ; Computer science ; Artificial intelligence ; Machine learning ; Nanotechnology ; Metallurgy ; Engineering ; Electrical engineering ; Mathematics ; Microstructure ; Telecommunications Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Chi Zhang | Dmytro Antypov | Matthew J. Rosseinsky | Matthew S. Dyer
Source: ChemRxiv (preprint, 2024-05-21)
Field: Chemistry — Inorganic Chemistry

Accelerating Metal-Organic Framework Discovery via Synthesisability Prediction: The MFD Evaluation Method for One-Class Classification Models Machine learning has found wide application in the materials field, particularly in discovering structure-property relationships. However, its potential in predicting synthetic accessibility of materials remains relatively unexplored due to the lack of negative data. In this study, we employ several one-class classification (OCC) approaches to accelerate the development of novel metal-organic framework materials by predicting their synthesisability. The evaluation of OCC model performance poses challenges, as traditional evaluation metrics are not applicable when dealing with a single type of data. To overcome this limitation, we introduce a quantitative approach, the Maximum Fractional Difference (MFD) method, to assess and compare model performance, as well as determine optimal thresholds for effectively distinguishing between positives and negatives. A DeepSVDD model with superior predictive capability is proposed. By combining assessment of synthetic viability with porosity prediction models, a list of 3,453 unreported combinations is generated characterised by predictions of high synthesisability and large pore size. The MFD methodology proposed in this study is intended to provide an effective complementary assessment method for addressing the inherent challenges in evaluating OCC models. The research process, developed models, and predicted results of this study are aimed at helping prioritisation of materials for synthesis. Class (philosophy) ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Data mining Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science