jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lalit Yadav
Source: arXiv (Cornell University) (preprint, 2023-11-16)
Field: Materials Science — Materials Chemistry

Atoms as Words: A Novel Approach to Deciphering Material Properties using NLP-inspired Machine Learning on Crystallographic Information Files (CIFs) In condensed matter physics and materials science, predicting material properties necessitates understanding intricate many-body interactions. Conventional methods such as density functional theory (DFT) and molecular dynamics (MD) often resort to simplifying approximations and are computationally expensive. Meanwhile, recent machine learning methods use handcrafted descriptors for material representation which sometimes neglect vital crystallographic information and are often limited to single property prediction or a sub-class of crystal structures. In this study, we pioneer an unsupervised strategy, drawing inspiration from Natural Language Processing (NLP), to harness the underutilized potential of Crystallographic Information Files (CIFs). We conceptualize atoms and atomic positions within a CIF similarly to words in textual content. Using a Word2Vec-inspired technique, we produce atomic embeddings that capture intricate atomic relationships. Our model, CIFSemantics, trained on the extensive Material Project dataset, adeptly predicts 15 distinct material properties from the CIFs. Its performance rivals specialized models, marking a significant step forward in material property predictions. Artificial intelligence ; Property (philosophy) ; Computer science ; Representation (politics) ; Class (philosophy) ; Word2vec ; Machine learning ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Morningstar, Elizabeth
Source: Smith ScholarWorks (Smith College) (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Investigating Weyl Semimetal and Magnetized Phases in Quantum Materials Using Stochastic and Machine Learning Methods Pyrochlore iridates are a class of materials that experience a metal to insulator phase transition in response to temperature or pressure. These materials are of particular interest due to their applications for superconductivity and other areas of physics. This thesis investigates methods of modeling and identifying phase transitions. The statistical nature of phase transitions lends itself well to computational study, however there are limitations such as the sign problem. Due to the complexity of pyrochlore iridates, the Ising model was first created using Monte Carlo methods to explore simple phase transitions dependent on temperature. To categorize these phase transitions, this project used two types of machine learning: supervised and unsupervised. The methods for creating the Ising model were applied to a toy model for pyrochlore iridates. With this toy model, the feasibility of using neural networks to identify phases, which specific focus on the Weyl semimetal phase, for pyrochlore iridates was tested. Ising model ; Pyrochlore ; Weyl semimetal ; Phase transition ; Artificial neural network ; Phase (matter) ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Theoretical and Computational Physics
Authors: Masahiko Okumura | Keita Kobayashi | A. Yamaguchi
Source:  (article, 2023-01-01)
Field: Materials Science — Biomaterials

Super-hydrated kaolinite under high pressure: A machine learning molecular dynamics study Numerical simulation is a practical tool to investigate the physical and chemical properties of clay minerals.Molecular dynamics (MD) is a powerful method based on nanoscale dynamics.Primary MD methods are classical molecular dynamics (CMD) and ab initio molecular dynamics (AIMD).CMD is based on a highly simplified physical model of atoms, i.e., atoms are approximated as point particles of classical mechanics, and the interaction is formulated by simple functions.AIMD employs quantum mechanical calculations for electrons, although nuclei are approximated as point particles.The computational costs of the former and the latter are low and high, respectively.The calculation accuracies using CMD and AIMD are low and high, respectively.Therefore, CMD is mainly used for relatively large-scale simulations for a rough estimation of the physical and chemical properties of clay minerals, and AIMD is used for the simulations of small systems to evaluate these properties accurately.Recently, machine learning molecular dynamics (MLMD) was proposed.This new method realizes low computational cost and high accuracy due to artificial neural networks trained using a vast number of ab initio calculations results to reproduce the potential energy surface of ab initio calculations.Kaolinite is one of the simplest phyllosilicates.Recently, the MLMD simulations of kaolinite atmospheric pressure have succeeded [1].MLMD reproduced the experimental result of phonons of the hydroxy group in the low-frequency region, which CMD and AIMD could not reproduce.Recently, an interesting structural change with respect to pressure has been reported: kaolinite with a thin water layer between the layers, which is called "super-hydrated kaolinite" [2].The thin water layer is an interesting research target of the mineral-fluid interface under extreme conditions.MLMD simulations of superhydrated kaolinite were performed, and the lattice parameters of super-hydrated kaolinite observed by the experiment [2] were accurately reproduced by the simulations.The vibrational properties of the hydroxyl group, the diffusion of water molecules in the interlayers, and other static and dynamic properties will be given at the presentation. Kaolinite ; High pressure ; Molecular dynamics ; Computer science ; Dynamics (music) ; Artificial intelligence ; Materials science ; Chemistry ; Mineralogy ; Engineering physics ; Physics ; Computational chemistry Clay minerals and soil interactions ; Soil and Unsaturated Flow ; Mineral Processing and Grinding
Authors: Jeffrey Hu | David L. Hu | Nihang Fu | Rongzhi Dong
Source: arXiv (Cornell University) (preprint, 2023-08-05)
Field: Materials Science — Materials Chemistry

Improving realistic material property prediction using domain adaptation based machine learning Materials property prediction models are usually evaluated using random splitting of datasets into training and test datasets, which not only leads to over-estimated performance due to inherent redundancy, typically existent in material datasets, but also deviate away from the common practice of materials scientists: they are usually interested in predicting properties for a known subset of related out-of-distribution (OOD) materials rather than a universally distributed samples. Feeding such target material formulas/structures to the machine learning models should improve the prediction performance while most current machine learning (ML) models neglect this information. Here we propose to use domain adaptation (DA) to enhance current ML models for property prediction and evaluate their performance improvements in a set of five realistic application scenarios. Our systematic benchmark studies show that there exist DA models that can significantly improve the OOD test set prediction performance while standard ML models and most of the other DAs cannot improve or even deteriorate the performance. Our benchmark datasets and DA code can be freely accessed at https://github.com/Little-Cheryl/MatDA. Benchmark (surveying) ; Computer science ; Property (philosophy) ; Machine learning ; Redundancy (engineering) ; Domain adaptation ; Artificial intelligence ; Domain (mathematical analysis) ; Set (abstract data type) ; Adaptation (eye) ; Predictive modelling ; Test set ; Code (set theory) ; Data mining Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Domain Adaptation and Few-Shot Learning
Authors: Paul Katzberger | Sereina Riniker
Source: arXiv (Cornell University) (preprint, 2023-02-18)
Field: Materials Science — Materials Chemistry

Implicit Solvent Approach Based on Generalised Born and Transferable Graph Neural Networks for Molecular Dynamics Simulations Molecular dynamics (MD) simulations enable the study of the motion of small and large (bio)molecules and the estimation of their conformational ensembles. The description of the environment (solvent) has thereby a large impact. Implicit solvent representations are efficient but in many cases not accurate enough (especially for polar solvents such as water). More accurate but also computationally more expensive is the explicit treatment of the solvent molecules. Recently, machine learning (ML) has been proposed to bridge the gap and simulate in an implicit manner explicit solvation effects. However, the current approaches rely on prior knowledge of the entire conformational space, limiting their application in practice. Here, we introduce a graph neural network (GNN) based implicit solvent that is capable of describing explicit solvent effects for peptides with different composition than contained in the training set. Solvation ; Molecular dynamics ; Artificial neural network ; Solvent ; Limiting ; Computer science ; Polar ; Solvent models ; Molecule ; Set (abstract data type) ; Graph ; Chemical space ; Implicit solvation ; Space (punctuation) ; Computational chemistry ; Chemistry ; Theoretical computer science ; Chemical physics ; Artificial intelligence ; Organic chemistry ; Physics ; Drug discovery Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: O. V. Sen’ko | N. N. Kiselyova | A. A. Dokukin | Viktor Dudarev | Yu. A. Kuznetsova | L. V. Dmitriev
Source:  (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

PARAMETRIC AND NON-PARAMETRIC MACHINE LEARNING METHODS IN TASKS OF PREDICTING THE PARAMETERS OF THE CRYSTAL LATTICE OF INORGANIC COMPOUNDS The work compares various linear parametric models and ensemble machine learning methods in problems of predicting crystal lattice parameters of inorganic compounds Parametric statistics ; Lattice (music) ; Computer science ; Parametric model ; Crystal (programming language) ; Crystal structure ; Machine learning ; Artificial intelligence ; Algorithm ; Chemistry ; Mathematics ; Physics ; Statistics ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Data Processing Techniques
Authors: Changlu Zhou | Ruihao Yuan | Yatong Zhao | B. C. HUI | Yuli Li | Pingxiang Zhang | Jinshan Li
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Augmented Oxidation Kinetics Model to Predict High-Temperature Steam Corrosion Behavior of Zirconium Alloys Zirconium ; Corrosion ; Zirconium alloy ; Kinetics ; Materials science ; Metallurgy ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; High-Temperature Coating Behaviors
Authors: Jun-Zhe Hao | Shiai Xu | Jiajun Xu | Hai-Lian Cao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predict the Surface Free Energy of Ti/Zr/V Chemical Conversion Coating on Nickel by Machine Learning Approach: Trees vs. Neurons Nickel ; Coating ; Energy (signal processing) ; Materials science ; Chemical engineering ; Metallurgy ; Nanotechnology ; Mathematics ; Statistics ; Engineering Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Corrosion Behavior and Inhibition
Authors: Long Liu | Fusheng Tan | Zan Wu | Yang Wang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Combining Genetic Algorithm and Deep Learning: Optimize the Performance of Chemical Kinetics Mechanism Under High Pressure Mechanism (biology) ; Computer science ; Kinetics ; Algorithm ; Artificial intelligence ; Physics Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Machine Learning in Materials Science
Authors: Su‐Huai Wei | Chuan-Nan Li | Han-Pu Liang | Xie Zhang | Zijing Lin
Source: Research Square (preprint, 2023-01-11)
Field: Materials Science — Materials Chemistry

Graph deep learning accelerated efficient crystal structure search and feature extraction Abstract Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features. Our method is highly accurate and predictive, and extracts structural features from desired structures to guide materials design. As a case study, we apply our new approach to a two-dimensional B-C-N system, which identifies 28 previously undiscovered stable structures out of 82 compositions; our analysis further establishes the structural features that contribute most to energy and bandgap. Compared to conventional approaches, SCCOP is about 10 times faster while maintaining a comparable accuracy. Our new framework is generally applicable to all types of systems for precise and efficient structural search, providing new insights into the relationship between ML-tracted structural features and physical properties. Graph ; Computer science ; Artificial intelligence ; Feature (linguistics) ; Feature extraction ; Deep learning ; Pattern recognition (psychology) ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Binh Duong Nguyen | Pavlo Potapenko | Aytekin Dermici | Kishan Govinda | Bompas, Sébastien | Sandfeld, Stefan
Source: arXiv (Cornell University) (preprint, 2023-09-01)
Field: Materials Science — Materials Chemistry

Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn-Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this. Robustness (evolution) ; Computer science ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Domain (mathematical analysis) ; Relation (database) ; Data mining ; Mathematics Machine Learning in Materials Science
Authors: Jun-Zhe Hao | Shiai Xu | Jiajun Xu | Hai-Lian Cao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predict the Surface Free Energy of Ti/Zr/V Chemical Conversion Coating on Nickel by Machine Learning Approach: Trees vs. Neurons Nickel ; Coating ; Energy (signal processing) ; Materials science ; Metallurgy ; Surface (topology) ; Chemical engineering ; Composite material ; Mathematics ; Engineering ; Statistics Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Corrosion Behavior and Inhibition
Authors: Jun-Zhe Hao | Shiai Xu | Jiajun Xu | Hai-Lian Cao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predict the Surface Free Energy of Ti/Zr/V Chemical Conversion Coating on Nickel by Machine Learning Approach: Trees vs. Neurons Nickel ; Coating ; Materials science ; Energy (signal processing) ; Metallurgy ; Composite material ; Mathematics ; Statistics Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Corrosion Behavior and Inhibition
Authors: Ivan Semenov | Alexandr Petrov
Source: Modern Technologies and Scientific and Technological Progress (article, 2023-05-04)
Field: Chemistry — Analytical Chemistry

PREDICTION OF THE PHYSICO-CHEMICAL PROPERTIES OF HYDROCARBON MIXTURES BY NEURAL NETWORKS The main classical approaches to the calculation of the physicochemical properties of individual hydrocarbons and their multicomponent mixtures are considered, as well as the possibility of their evaluation based on artificial neural networks. The obtained training set allows to estimate the boiling point, average molecular weight and density of hydrocarbon mixtures based on a limited set of initial data: mass concentrations of its components and their normal boiling Boiling point ; Artificial neural network ; Hydrocarbon ; Boiling ; Set (abstract data type) ; Biological system ; Training set ; Hydrocarbon mixtures ; Chemistry ; Thermodynamics ; Computer science ; Organic chemistry ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses
Authors: Wei Zeng | Mengtang Li | Yu-Chian Calvin
Source:  (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

DEGAT: A Graph Neural Network Algorithm Based on Dynamic Attention Mechanism for Molecular Property Prediction Accurately predicting the physicochemical, biophysical, and physiological properties of molecules plays a vital role in drug design and drug discovery, and it is also one of the fundamental tasks in computer-aided drug design(CADD). In recent years, graph neural networks have developed rapidly in the application of molecular property prediction tasks, but existing methods pay less attention to the feature information of edges (chemical bonds), so the obtained molecular representations have certain limitations. We propose a dynamic attention-based message passing neural network (DEGAT), which changes the static attention of GAT to dynamic attention, and describes the importance of different edges by applying weights to the edges. On this basis, we also introduce a feature enhancement module, which effectively highlights the role of nodes with strong information. Our experiments show that on ten molecular property datasets, the performance of our proposed model has been significantly improved compared with the advanced baseline models in recent years, which indicates that the DEGAT model has learned molecular representations that are more suitable for molecular property prediction tasks. Computer science ; Mechanism (biology) ; Property (philosophy) ; Artificial neural network ; Graph ; Algorithm ; Graph theory ; Artificial intelligence ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Qin Li | Nihang Fu | Sadman Sadeed Omee | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

MD-HIT: Machine learning for materials property prediction with dataset redundancy control Materials datasets are usually featured by the existence of many redundant (highly similar) materials due to the tinkering material design practice over the history of materials research. For example, the materials project database has many perovskite cubic structure materials similar to SrTiO$_3$. This sample redundancy within the dataset makes the random splitting of machine learning model evaluation to fail so that the ML models tend to achieve over-estimated predictive performance which is misleading for the materials science community. This issue is well known in the field of bioinformatics for protein function prediction, in which a redundancy reduction procedure (CD-Hit) is always applied to reduce the sample redundancy by ensuring no pair of samples has a sequence similarity greater than a given threshold. This paper surveys the overestimated ML performance in the literature for both composition based and structure based material property prediction. We then propose a material dataset redundancy reduction algorithm called MD-HIT and evaluate it with several composition and structure based distance threshold sfor reducing data set sample redundancy. We show that with this control, the predicted performance tends to better reflect their true prediction capability. Our MD-hit code can be freely accessed at https://github.com/usccolumbia/MD-HIT Redundancy (engineering) ; Computer science ; Machine learning ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Genomics and Phylogenetic Studies
Authors: Yashwant Kurmi | M. Vishwanathan | Zhongliang Zu
Source:  (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Quantitative CEST Molecular MR Imaging Using Deconvolution and Parallel Deep Learning Models &lt;p&gt;Chemical Exchange Saturation Transfer (CEST) imaging is a promising MRI molecular imaging technique that allows for multiple parameter quantification (MPQ) of various molecules. However, the presence of overlapping confounding signals and poor data quality pose challenges in accurately quantifying the CEST effect and the underlying exchange parameters. In this paper, we propose a deconvolution-based preprocessing and a deep learning architecture called Deep Multiple Z-spectrum Quantification (DMUZQ) to address these limitations and enable automatic MPQ in CEST imaging. Our approach begins with a deconvolution kernel that minimizes interference from overlapping pools in CEST Z-spectra. This preprocessing step enhances the features of the solute of interest and improves data quality, enabling more specific molecular information to be extracted.&lt;/p&gt; &lt;p&gt;The DMUZQ model utilizes multiple power level signals and provides a weighted average of the quantified outcome, leading to enhanced accuracy and robustness in parameter quantification. We perform model optimization at both the individual and complete model levels. To estimate the accurate range of solute exchange rate, we introduce explicit regularization using the Lyapunov stability criteria, which considers the functional relationship between solute concentration, relaxation time, and exchange rate. We validate the DMUZQ model through comprehensive analysis and comparisons with existing deep learning models, variants, and a conventional CEST quantification method based on data fitting. Simulated data experiments demonstrate that our DMUZQ method achieves higher accuracy when compared to ground truth. Evaluation on animal tumor models further confirms its superior performance over the conventional fitting approach.&lt;/p&gt; Deconvolution ; Computer science ; Artificial intelligence ; Preprocessor ; Algorithm ; Robustness (evolution) ; Deep learning ; Ground truth ; Pattern recognition (psychology) ; Chemistry Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis