jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. D. Podobrazhnykh | T. A. Eremina | Ekaterina I. Marchenko | N. N. Eremin
Source: Moscow University Bulletin Series 4 Geology (article, 2024-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Crystal chemical indicators of oxo-centered tetrahedra establishment in divalent lead minerals A complex crystal-chemical analysis of 1567 oxygen positions in 216 crystal structures of lead- containing minerals belonging to the classes of silicates, phosphates, oxides, oxohalides, and a number of others has been carried out. It was shown that the combined application of the bond valence method and the analysis of the geometric characteristics of Voronoi-Dirichlet polyhedra make it possible to confidently distinguish between oxygen positions related to cation structure blocks and oxygen positions for which a description based on an anion-centered approach is more justified. Numerical indicators for such a separation of oxygen positions are substantiated, which can be used to analyze large arrays of structural data using the machine learning approaches. Tetrahedron ; Divalent ; Lead (geology) ; Mineral ; Crystal (programming language) ; Crystallography ; Chemistry ; Materials science ; Geology ; Metallurgy ; Computer science Crystal Structures and Properties ; X-ray Diffraction in Crystallography ; Inorganic Fluorides and Related Compounds
Authors: Elaheh Kazemi-Khasragh | Carlos Gonzaleza | Maciej Harańczyk
Source: arXiv (Cornell University) (preprint, 2024-01-17)
Field: Materials Science — Materials Chemistry

Toward Diverse Polymer Property Prediction Using Transfer Learning The prediction of mechanical and thermal properties of polymers is a critical aspect for polymer development. Herein, we discuss the use of transfer learning approach to predict multiple properties of linear polymers. The neural network model is initially trained to predict the heat capacity in constant pressure (Cp) of linear polymers. Once, the pre-trained model is transferred to predict four additional properties of polymers: specific heat capacity (Cv), shear modulus, flexural stress strength at yield, and tensile creep compliance. They represent a diverse set of mechanical, thermal, and rheological properties. We demonstrate the effectiveness of the approach by achieving high accuracy in predicting the four additional properties using relatively small datasets of 13 to 18 samples. Also, the performance of the base model is examined using five different loss functions. Our results suggest that the combined loss function had better performance compared to the individual loss functions. Materials science ; Flexural strength ; Rheology ; Polymer ; Ultimate tensile strength ; Heat capacity ; Artificial neural network ; Yield (engineering) ; Shear modulus ; Constant (computer programming) ; Creep ; Composite material ; Computer science ; Thermodynamics ; Artificial intelligence Machine Learning in Materials Science
Authors: Ruichen Liu | Li Wang | Xiangwen Zhang | Guozhu Li
Source: Journal of Computational Chemistry (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Groupy: An Open‐Source Toolkit for Molecular Simulation and Property Calculation In this work, an open-source, versatile, and flexible code named Groupy is present for calculating various molecular properties and preparing input files of molecular simulation software such as Gaussian. This code requires only SMILES as input, but can output many new useful data and files in multiple formats. The output information is clear and easy to read. The tips to the users are very detailed and easy to follow when using. Message passing interface (MPI) parallelization is supported to reduce computing time when the properties of a large number of molecules are calculated. Groupy not only supports the calculation of molecular properties using the traditional group contribution method, but also directly outputs the group-contribution-style molecular fingerprints for machine learning. The code has strong extensibility, which can be used as an external library to build other programs. We hope that Groupy brings great convenience to both computational and experimental chemists in their daily research. The code of Groupy can be freely obtained at https://github.com/47-5/Groupy. Computer science ; Code (set theory) ; Source code ; Extensibility ; Software ; Interface (matter) ; Property (philosophy) ; Open source ; Gaussian ; Programming language ; Computational science ; Theoretical computer science ; Parallel computing ; Chemistry ; Set (abstract data type) Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Catalysis and Oxidation Reactions
Authors: Arpan Choudhury | Debashree Ghosh
Source: ChemRxiv (preprint, 2024-11-27)
Field: Chemistry — Organic Chemistry

Engineering melanin-based meta-material for broad UV-visible absorption Designing materials with high intensity absorption of solar light across wide wavelength range is a primary goal in organic optoelectronics engineering. However, the potential application of most known biomolecules specifically in bio-optoelectronics is limited because they can only absorb light within specific wavelengths. Both experimental and computational approaches have investigated the potential of the skin pigment melanin, in this direction, but progress has been limited due to the complexity of its chemical space. In this work, we design a comprehensive virtual chemical space of melanin and develop machine learning-based approach to predict their complete optical and thermodynamical properties. These predictions help engineering melanin-based sustainable materials for tailored optoelectronic applications. Visible spectrum ; Broad spectrum ; Absorption (acoustics) ; Materials science ; Melanin ; Chemistry ; Optoelectronics ; Composite material ; Biochemistry Polydiacetylene-based materials and applications ; melanin and skin pigmentation
Authors: Priyanka Hari Borude
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2024-10-11)
Field: Chemistry — Spectroscopy

Computational Toxicology Abstract: The discipline of “computational” toxicology has been studied thoroughly and its applications have been implemented in the field to the advantage of many individuals by providing cheap and rapid solutions for chemical screening.The current review focuses on the recent progress, obstacles, and use of computational toxicology with respect to various predicted within toxicity endpoints; carcinogenicity, mutagenicity and reproductive spanning toxicity in males. The combination of these approaches involving quantitative structure-activity relationship (QSAR) alongside machine learning (ML) and molecular docking methods has contributed to the improvement of the accuracy of the predictions. There are clear opportunities in the area of computional toxicity for example reduction in testing on animals and more rapid evaluation of the chemical safety of substances as well as improved drug design and in the recognition of threats. Another research will be focused on the integration of multi-omic data, accurate and interpretable model development, and also extension in applicability to new fields such as nanotoxicology. In this regard, computational toxicology allows factories and companies to accommodate only less toxic or more pharmaceutically active chemicals or drugs. The development of computer-aided drug designing alongside experimental screening practices has greatly enhanced methods used in evaluating the safety of chemicals and developing new drugs Toxicology ; Computer science ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Statistical and Computational Modeling
Authors: Tianfan Jin | Veerupaksh Singla | Hsuan‐Hao Hsu | Brett M. Savoie
Source: ChemRxiv (preprint, 2024-05-28)
Field: Chemistry — Physical and Theoretical Chemistry

Large Property Models: A New Generative Paradigm for Molecules Generative models for the inverse design of molecules with particular properties have been heavily hyped but have yet to demonstrate significant gains over machine learning augmented expert intuition. A major challenge of such models is their limited accuracy in predicting molecules with targeted properties in the data scarce regime, which is the regime typical of the prized outliers that inverse models are hoped to discover. For example, activity data for a drug target or stability data for a material may only number in the tens to hundreds of samples, which is insufficient to learn an accurate and reasonably general property-to-structure inverse mapping from scratch. We’ve hypothesized that the property to structure mapping becomes unique when a sufficient number of properties are supplied to the models during training. This hypothesis has several important corollaries if true. It would imply that data scarce properties can be completely determined by a set of more accessible molecular properties. It would also imply that a generative model trained on multiple properties would exhibit an accuracy phase transition after achieving a sufficient size—a process analogous to what has been observed in the context of large language models. To interrogate these behaviors, we have built the first transformers trained on the property to molecular graph task, which we dub “large property models” (LPMs). A key ingredient is supplementing these models during training with relatively basic but abundant chemical property data. The motivation for the large property model paradigm, the model architectures, and case studies are presented here for review and discussion at the upcoming Faraday Discussion on “Data-driven discovery in the chemical sciences”. Generative grammar ; Property (philosophy) ; Cognitive science ; Computer science ; Artificial intelligence ; Psychology ; Epistemology ; Philosophy History and advancements in chemistry
Authors: Da‐Jiang Liu | Jie Zhang | Long Qi | James W. Evans
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Molecular Simulation Using Transfer-Learned Potentials for the Disordered Nanoscale Structure of Nitrogen-Doped Nanoporous Carbons Nanoporous ; Nanoscopic scale ; Nitrogen ; Materials science ; Doping ; Nanotechnology ; Molecular dynamics ; Chemical physics ; Chemical engineering ; Chemistry ; Optoelectronics ; Computational chemistry ; Engineering ; Organic chemistry Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions
Authors: Zachary T. P. Fried | Brett A. McGuire
Source: The Journal of Physical Chemistry A (article, 2024-09-12)
Field: Chemistry — Spectroscopy

Automated Mixture Analysis via Structural Evaluation The determination of chemical mixture components is vital to a multitude of scientific fields. Oftentimes spectroscopic methods are employed to decipher the composition of these mixtures. However, the sheer density of spectral features present in spectroscopic databases can make unambiguous assignment to individual species challenging. Yet, components of a mixture are commonly chemically related due to environmental processes or shared precursor molecules. Therefore, analysis of the chemical relevance of a molecule is important when determining which species are present in a mixture. In this paper, we combine machine-learning molecular embedding methods with a graph-based ranking system to determine the likelihood of a molecule being present in a mixture based on the other known species and/or chemical priors. By incorporating this metric in a rotational spectroscopy mixture analysis algorithm, we demonstrate that the mixture components can be identified with extremely high accuracy (≥97%) in an efficient manner. Computer science Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Crystallography and molecular interactions
Authors: P. D’Angelo | Francesco Tavani | Alessandro Tofoni | Marco Vandone | Matteo Busato | Luca Braglia | Piero Torelli | Maximillian G. Stanzione | A. Robert Armstrong | Russell E. Morris | Valentina Colombo
Source: Research Square (preprint, 2024-11-15)
Field: Materials Science — Materials Chemistry

A combined soft X-ray and theoretical investigation discloses the water harvesting behaviour of Mg MOF-74 at the crystal surface Materials science ; Crystal (programming language) ; X-ray ; Surface (topology) ; Nanotechnology ; Composite material ; Optics ; Computer science ; Physics ; Mathematics ; Geometry Magnesium Oxide Properties and Applications ; Inorganic Fluorides and Related Compounds ; Multiferroics and related materials
Authors: Yingjie Li | Anthony Agnesina | Yanqing Zhang | Haoxing Ren | Cunxi Yu
Source: arXiv (Cornell University) (preprint, 2024-01-19)
Field: Materials Science — Materials Chemistry

BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation Boolean algebraic manipulation is at the core of logic synthesis in Electronic Design Automation (EDA) design flow. Existing methods struggle to fully exploit optimization opportunities, and often suffer from an explosive search space and limited scalability efficiency. This work presents BoolGebra, a novel attributed graph-learning approach for Boolean algebraic manipulation that aims to improve fundamental logic synthesis. BoolGebra incorporates Graph Neural Networks (GNNs) and takes initial feature embeddings from both structural and functional information as inputs. A fully connected neural network is employed as the predictor for direct optimization result predictions, significantly reducing the search space and efficiently locating the optimization space. The experiments involve training the BoolGebra model w.r.t design-specific and cross-design inferences using the trained model, where BoolGebra demonstrates generalizability for cross-design inference and its potential to scale from small, simple training datasets to large, complex inference datasets. Finally, BoolGebra is integrated with existing synthesis tool ABC to perform end-to-end logic minimization evaluation w.r.t SOTA baselines. Computer science ; Inference ; Scalability ; Theoretical computer science ; Graph ; Generalizability theory ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Ralf Meyer | Daniel B. K. Chu | Heather J. Kulik
Source: Machine Learning Science and Technology (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Many-body expansion based machine learning models for octahedral transition metal complexes Abstract Graph-based machine learning (ML) models for material properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not include any 3D information and are unable to distinguish stereoisomers such as those arising from different orderings of ligands around a metal center in coordination complexes. In this work we present a modification to revised autocorrelation descriptors, a molecular graph featurization method, for predicting spin state dependent properties of octahedral transition metal complexes (TMCs). Inspired by analytical semi-empirical models for TMCs, the new modeling strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. We present the necessary modifications to include this approach in two commonly used ML methods, kernel ridge regression and feed-forward neural networks. On a test set composed of all possible isomers of binary TMCs, the best MBE models achieve mean absolute errors (MAEs) of 2.75 kcal mol −1 on spin-splitting energies and 0.26 eV on frontier orbital energy gaps, a 30%–40% reduction in error compared to models based on our previous approach. We also observe improved generalization to previously unseen ligands where the best-performing models exhibit MAEs of 4.00 kcal mol −1 (i.e. a 0.73 kcal mol −1 reduction) on the spin-splitting energies and 0.53 eV (i.e. a 0.10 eV reduction) on the frontier orbital energy gaps. Because the new approach incorporates insights from electronic structure theory, such as ligand additivity relationships, these models exhibit systematic generalization from homoleptic to heteroleptic complexes, allowing for efficient screening of TMC search spaces. Octahedron ; Transition metal ; Materials science ; Computer science ; Crystallography ; Chemistry ; Crystal structure Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Daniel F. Thomas du Toit | Yuxing Zhou | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2024-08-01)
Field: Materials Science — Materials Chemistry

Hyperparameter Optimization for Atomic Cluster Expansion Potentials Machine-learning-based interatomic potentials enable accurate materials simulations on extended time- and lengthscales. ML potentials based on the Atomic Cluster Expansion (ACE) framework have recently shown promising performance for this purpose. Here, we describe a largely automated computational approach to optimizing hyperparameters for ACE potential models. We extend our openly available Python package, XPOT, to include an interface for ACE fitting, and discuss the optimization of the functional form and complexity of these models based on systematic sweeps across relevant hyperparameters. We showcase the usefulness of the approach for two example systems: the covalent network of silicon and the phase-change material Sb$_{2}$Te$_{3}$. More generally, our work emphasizes the importance of hyperparameter selection in the development of advanced ML potential models. Cluster (spacecraft) ; Hyperparameter ; Cluster expansion ; Coupled cluster ; Computer science ; Physics ; Statistical physics ; Algorithm ; Quantum mechanics Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Jirui Jin | Somayeh Faraji | Bin Liu | Mingjie Liu
Source: ChemRxiv (preprint, 2024-05-13)
Field: Materials Science — Materials Chemistry

Comparative Analysis of Conventional Machine Learning and Graph Neural Network Models for Perovskite Property Prediction Perovskite materials, renowned for their versatility and remarkable properties, pose challenges in discovering optimal candidates due to the vast compositional space. Data-driven machine learning (ML) offers promise in expediting material discovery; however, the trade-off between accuracy and efficiency across different ML models for predicting perovskite properties is not well understood. In this study, we conducted a comprehensive assessment of various ML models for predicting the formation energy (Ef) and bandgap (Eg) of perovskites. We designed a protocol to extract perovskite structures from three databases based on the stoichiometry, octahedral lattice motif, and alignment with established perovskite prototype structures. Benchmarking conventional ML algorithms (CML) against graph neural network (GNN) models across three datasets, we identified the GATGNN model as the top performer, balancing exceptional prediction accuracy and computational efficiency. We further investigated the impact of data size on model performance, emphasizing the need for over 1000 data points for optimal prediction accuracy. Additionally, through SHAP analysis, we provided valuable insights into the interpretation of CML models in predicting Ef and Eg. Our study establishes a standardized benchmark for evaluating various ML models across diverse datasets of perovskite materials, facilitating future applications in materials science, particularly in model selection and the advancement of perovskite materials. Artificial neural network ; Property (philosophy) ; Artificial intelligence ; Graph ; Computer science ; Machine learning ; Theoretical computer science ; Philosophy Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Advancements in Solid Oxide Fuel Cells
Authors: Shilpa S. Selvan | Debabandya Mohapatra | Adinath Kate | Karan Singh | Manoj Kumar Tripathi | Abhijit Kar
Source: Journal of Agricultural Engineering (India) (article, 2024-12-25)
Field: Chemistry — Analytical Chemistry

Development of Machine Learning-based Shelf Life Prediction Models for Pearl Millet (Pennisetum glaucum L.) Grains using Oxidation Kinetics Data By observing oxidation kinetics, this study examined how storage temperature (5°C, 25°C, 45°C) and duration (0 to 120 days) affect the quality of raw and fermented pearl millet grains. The study also compared the accuracy of different machine-learning approaches for predicting shelf life of pearl millet grains. The results showed that the levels of free fatty acid (FFA), acid value (AV), peroxide values (PV) and lipase activity (LA) increased with the temperature and duration of storage, regardless of the treatment. For raw grains, the FFA, AV, PV and LA content varied from 0.89% to 6.23%, 1.3 to 8.84 mg NaOH 100g-1, 5 to 88.33 mEq kg-1 of flour, and 4.34 to 23.47 mg KOH g-1, respectively during 120 days of storage over the storage temperature range under consideration. On the other hand, in the case of fermented grains, the values of FFA, AV, PV and LA content ranged from 0.85% to 4.52%, 1.2 to 6.41 mg NaOH 100g-1, 5 to 51.67 mEq kg-1 of flour, and 4.29 to 12.36 mg KOH g-1, respectively. The FFA, AV, and PV values for raw and fermented grains were used to estimate the shelf life using oxidation kinetics data. The kinetics data fit a pseudo- zero-order reaction model better than a first-order reaction model (R2 =0.8901 to 0.9927). Among all the machine learning techniques, artificial neural network (ANN) was found to be a better predictor with the least error functions and higher accuracy (R2 =0.9847 to 0.9969) as compared with the Gradient Boosting and the Support Vector Machine models. Pennisetum ; Peroxide value ; Shelf life ; Food science ; Fermentation ; Kinetics ; Chemistry ; Mathematics ; Animal science ; Horticulture ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Meat and Animal Product Quality
Authors: Hossein Tahmasbi | Kushal Ramakrishna | Mani Lokamani | Attila Cangi
Source: Physical Review Materials (article, 2024-03-21)
Field: Materials Science — Materials Chemistry

Machine learning-driven structure prediction for iron hydrides We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a versatile machine-learned interatomic potential for iron hydride via a neural network using an iterative training process to explore its energy landscape under different pressures. To evaluate the accuracy and comprehend the intricacies of the PES, we conducted comprehensive crystal structure predictions using our neural network-based potential paired with the minima hopping approach. The predictions spanned pressures ranging from ambient to 100 GPa. Our results reproduce the experimentally verified global minimum structures such as dhcp, hcp, and fcc, corroborating previous findings. Furthermore, our in-depth exploration of the iron hydride PES at different pressures has revealed complex alterations and stacking faults in these phases, leading to the identification of several different low-enthalpy structures. This investigation has not only confirmed the presence of regions of established FeH configurations but has also highlighted the efficacy of using data-driven, extensive structure prediction methods to uncover the multifaceted PES of materials. Materials science ; Metallurgy ; Engineering physics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling