jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kenji Sakurai
Source: X-Ray Spectrometry (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

News Article The materials project -Database and toolbox for materials science So far, numerous databases have been built around the world that summarize a large number of physical and chemical properties, thermodynamical diagrams, crystal structures, and some other useful experimental and calculated data.It is extremely useful for checking and confirming various properties of known materials and, conversely, for discovering promising candidates for functions and properties of interest.Furthermore, one of the remarkable recent trends is employment of deep learning, which is known as the 3rd generation of artificial intelligence, to explore some knowledge and models using already stored big data and some optimized algorithms.The system also includes a platform for quantum mechanical calculation.The Materials Project, established in 2011 by Professor Kristin Persson of Lawrence Berkeley National Laboratory, is an open-access database.Anyone can register as a user and start using it for free of charge.More than 120,000 users have already registered.From the perspective of X-ray experts, the system should be very interesting, because it includes many experimentally collected X-ray absorption spectra, the crystal structure data, and the related quantum mechanical calculation data.The total number of the registered data exceeds 500,000; X-ray spectra and crystal structure data are nearly 20% and 35%, respectively, and others are more theoretical data, such as the density of states, the bond structures and so forth.The Materials Project organizes seminars and annual workshop as well. Toolbox ; Computer science ; Crystal (programming language) ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Lu Xu | Yang Qin
Source: Research Square (preprint, 2022-12-09)
Field: Materials Science — Materials Chemistry

Is theoretical chemistry entering a new era of big data learning and reasoning: deep electron cloud-activity and field-activity relationships Cloud computing ; Field (mathematics) ; Data science ; Big data ; Deep learning ; Computer science ; Chemistry ; Artificial intelligence ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Cools-Ceuppens, Maarten | Dambre, Joni | Verstraelen, Toon
Source: Ghent University Academic Bibliography (Ghent University) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A physically sound basis for polarization in machine-learning force fields Molecular dynamics simulations can make quantitative predictions of complex physical and chemical processes at the nanoscale, if a suitable model for the potential energy surface (PES) is available for the system of interest. In fact, there are multiple requirements for a PES model, before it can be considered suitable. It must be capable of describing all processes of interest, e.g. many force field models preclude chemical reactions, whereas most electronic structure methods can describe them. Also, the accuracy of the PES model must be sufficient to minimize the error on prediction outcomes. Finally, also the computational efficiency is an essential requirement, because long molecular dynamics runs are often needed to converge the phase-space sampling. Machine-learning force fields have undeniably reached unprecedented compromises between these requirements: they can be trained to mimic density functional theory (or even better) training data to arbitrary accuracy, while they are computationally much more efficient than electronic structure methods. Despite their popularity and promise, machine-learning force fields have an important limitation: they are inherently short-ranged. The restriction to short ranges has two origins: short real-space cutoff spheres and unfavorable scaling of the required training data for long-range interactions. The first origin is addressed, at least to some extent, in message-passing networks: through multiple message-passing iterations, information from beyond the cutoff distance is taken into account. The second origin is more fundamental and harder to tackle: the number of configurations of atoms at long distances is vast, making it impossible to generate relevant examples for all possibilities. Whenever long-range interactions matter, a physical model is unavoidable. Many physically motivated models for long-range electrostatics and polarization were developed for normal force fields, and were later combined with machine-learning potentials for short-range interactions. In this lecture, advantages and disadvantages of several approaches will be discussed, and a new framework, called electron machine-learning potential (eMLP), will be introduced. Cutoff ; Force field (fiction) ; Basis (linear algebra) ; Scaling ; Polarization (electrochemistry) ; Field (mathematics) ; Free energy principle ; Molecular dynamics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum many-body systems
Authors: Ujjwal Poudel | Madhu Sudhan Bhusal | Manish Bhurtel | Atish Adhikari | Narayan Prasad Adhikari
Source: Journal of Nepal Physical Society (article, 2022-12-13)
Field: Chemistry — Inorganic Chemistry

Machine Learning in Predicting Lattice Constant of Cubic Perovskite Oxides A sample of 3,115 data of perovskite oxides in the form of ABO3 (A and B being the cations) was taken for this study of the application of machine learning in predicting the lattice constants (a determining factor in material design). The ANN, DT, RF, KNN, and SVM models were used to predict the lattice constants of perovskites because machine learning techniques have been phenomenal in uncovering crystal structures in the field of material research in recent years. These models used properties like ionic radii, formation energy, and band gap as input features. The R2 score was used to assess the regression model’s performance. The Random Forest Regression Model outperforms all other regression models regarding dataset and features. Lattice constant ; Support vector machine ; Lattice (music) ; Ionic radius ; Random forest ; Lattice energy ; Crystal structure ; Machine learning ; Artificial intelligence ; Materials science ; Regression analysis ; Perovskite (structure) ; Regression ; Condensed matter physics ; Computer science ; Mathematics ; Chemistry ; Physics ; Ion ; Statistics ; Optics ; Crystallography ; Diffraction Pigment Synthesis and Properties ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Debjyoti Bhattacharya | Devon C. Kleeblatt | Antonia Statt | Wesley F. Reinhart
Source: arXiv (Cornell University) (preprint, 2022-04-09)
Field: Chemistry — Organic Chemistry

Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in which the local arrangement of chemical moieties can lead to the formation of long-range structure. The dependence of this structure on the sequence necessarily implies that a mapping between the two exists, yet it has been difficult to model so far. Predicting the aggregation behavior of these macromolecules is challenging due to the lack of effective order parameters, a vast design space, inherent variability, and high computational costs associated with currently available simulation techniques. Here, we accurately predict the morphology of aggregates self-assembled from sequence-defined macromolecules using supervised machine learning. We find that regression models with implicit representation learning perform significantly better than those based on engineered features such as $k$-mer counting, and a Recurrent-Neural-Network-based regressor performs the best out of nine model architectures we tested. Furthermore, we demonstrate the high-throughput screening of monomer sequences using the regression model to identify candidates for self-assembly into selected morphologies. Our strategy is shown to successfully identify multiple suitable sequences in every test we performed, so we hope the insights gained here can be extended to other increasingly complex design scenarios in the future, such as the design of sequences under polydispersity and at varying environmental conditions. Sequence (biology) ; Representation (politics) ; Computer science ; Aggregate (composite) ; Artificial neural network ; Macromolecule ; Artificial intelligence ; Machine learning ; Sequence space ; Regression ; Biological system ; Nanotechnology ; Mathematics ; Chemistry ; Biology ; Materials science Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: A. Nolan Wilson | Peter C. St. John
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

PolyID™ (Polymer Inverse Design) [SWR-21-98] PolyID™ provides a framework for building, training, and predicting polymer properties using graph neural networks. The codes leverages Neural Fingerprint (nfp) (See DOE Code ID 22185) for building tensorflow-based message-passing neural networks, and Monomers to Polymers (m2p) (See DOE Code ID 44795) for building polymer structures. To understand and use the pipeline, the following examples have been provided: Building polymer structures: examples/example_generate_polymer_structures.ipynb Checking domain of validity: examples/example_determine_domain-of-validity.ipynb Training a graph neural network: examples/example_generate_and_train_models.ipynb Predicting with the trained model: examples/example_predict_with_trained_models.ipynb Inverse ; Computer science ; Mathematics ; Geometry Photopolymerization techniques and applications ; Material Properties and Applications ; Engineering Applied Research
Authors: Luis M. Antunes | Keith T. Butler | Ricardo Grau‐Crespo
Source: arXiv (Cornell University) (preprint, 2022-12-13)
Field: Materials Science — Materials Chemistry

Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To enable deployment on global scales, new classes of effective thermoelectrics are thus required. $\textit{Ab initio}$ models of transport properties can help in the design of new thermoelectrics, but they are still too computationally expensive to be solely relied upon for high-throughput screening in the vast chemical space of all possible candidates. Here, we use models constructed with modern machine learning techniques to scan very large areas of inorganic materials space for novel thermoelectrics, using composition as an input. We employ an attention-based deep learning model, trained on data derived from $\textit{ab initio}$ calculations, to predict a material's Seebeck coefficient, electrical conductivity, and power factor over a range of temperatures and $\textit{n}$- or $\textit{p}$-type doping levels, with surprisingly good performance given the simplicity of the input, and with significantly lower computational cost. The results of applying the model to a space of known and hypothetical binary and ternary selenides reveal several materials that may represent promising thermoelectrics. Our study establishes a protocol for composition-based prediction of thermoelectric behaviour that can be easily enhanced as more accurate theoretical or experimental databases become available. Thermoelectric materials ; Ternary operation ; Thermoelectric effect ; Computer science ; Ab initio ; Seebeck coefficient ; Electricity ; Engineering physics ; Materials science ; Thermodynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Inorganic Chemistry and Materials
Authors: Shan He
Source: Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &amp; Computational Sciences and Engineering, 8th ed. - MOL2NET: FROM MOLECULES TO NETWORKS (article, 2022-11-03)
Field: Materials Science — Materials Chemistry

Applications of Artificial Intelligence in nanoscience. Machine learning (ML) has gained extensive attention in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields such as chemical, pharmaceutical, biological etc. This powerful tool helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are ML potentials, property prediction, and material discovery. Therefore, In this review different type of application of machine learning in nanotechnology will be discussed. Computer science ; Field (mathematics) ; Artificial intelligence ; Nanotechnology ; Property (philosophy) ; Machine learning ; Data science ; Materials science ; Mathematics Machine Learning in Materials Science ; Mineral Processing and Grinding
Authors: Từ Nguyễn Thiên Phúc | Nazanin Rezajooei | Erin R. Johnson | Christopher N. Rowley
Source: ChemRxiv (preprint, 2022-12-29)
Field: Materials Science — Materials Chemistry

A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion Neural Network Potentials (NNPs) have quickly emerged as powerful computational methods for modeling large chemical systems with the accuracy of quantum mechanical methods but at a much smaller computational cost. To make the training and evaluation of the underlying neural networks practical, these methods commonly cut off interatomic interactions at a modest range (e.g., 5.2 Å), so longer-range interactions like London dispersion are neglected. This limits the accuracy of these models for intermolecular interactions. In this work, we develop a new NNP designed for modeling chemical systems where dispersion is an essential component. This new NNP is extended to treat dispersion interactions rigorously by calculating atomic dispersion coefficients through a second set of NNs, which is trained to reproduce the coefficients from the quantum-mechanically derived exchange-hole dipole moment (XDM) model. The NNP with this dispersion correction predicts intermolecular interactions in very good agreement with the QM data, with a mean absolute error (MAE) of 0.67 kcal/mol and a coefficient of determination (R2) of 0.97. The dispersion components of these intermolecular interactions are predicted in excellent agreement with the QM data, with a mean absolute error (MAE) of 0.01 kcal/mol and an R2 of 1.00. This combined dispersion-corrected NNP, called ANIPBE0-MLXDM, predicts intermolecular interaction energies for complexes from the DES370K test set with an MAE of 0.69 kcal/mol and an R2 of 0.97 relative to high-level ab initio results (CCSD(T)), but with a computational cost that is billions of times smaller. The ANIPBE0-MLXDM method is effective for simulating large-scale dispersion-driven systems, such as molecular liquids and gas adsorption in porous materials, on a single computer workstation. Intermolecular force ; Dispersion (optics) ; Artificial neural network ; Statistical physics ; Range (aeronautics) ; Test set ; Dipole ; Ab initio ; London dispersion force ; Chemistry ; Computational chemistry ; Physics ; Computer science ; Quantum mechanics ; Materials science ; Machine learning ; van der Waals force ; Molecule Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Joseph Thacker | David J. Bray | Patrick B. Warren | R. L. Anderson
Source: ChemRxiv (preprint, 2022-12-19)
Field: Materials Science — Materials Chemistry

Can machine learning predict the phase behavior of surfactants? We explore the prediction of surfactant phase behavior using state-of-the-art machine- learning methods, using a data set for twenty-three non-ionic surfactants modified from Bell, Phil. Trans. R. Soc. A 2016, 374, 20150137. We recover Bell’s observation that most machine learning methods are capable to some degree of filling in missing data in partially-complete phase diagrams. However, strong data bias and a lack of chemical space information generally lead to poor results for de novo phase diagram prediction. Although some machine learning methods perform better than others, these observations are largely robust to the particular choice of algorithm. For the gap-filling challenge we additionally examine how to sample state points effectively in the context of typical experimental protocols, to make the most of machine-learning interpolation. Our results indicate what factors should be considered when preparing for machine- learning prediction of surfactant phase behavior in future studies. Machine learning ; Computer science ; Artificial intelligence ; Context (archaeology) ; Interpolation (computer graphics) ; Phase (matter) ; Set (abstract data type) ; Algorithm ; Chemistry Machine Learning in Materials Science ; Surfactants and Colloidal Systems
Authors: Saghar Fooladimahani | Seyed Ali Razavi | F. Ashrafizadeh
Source: Transactions of the Indian Institute of Metals (article, 2022-12-19)
Field: Materials Science — Materials Chemistry

Prediction of Age Hardening Parameters for Nickel Aluminium Bronze by Artificial Neural Network and Genetic Algorithm Artificial neural network ; Materials science ; Aluminium ; Genetic algorithm ; Alloy ; Hardening (computing) ; Ageing ; Nickel ; Metallurgy ; Aluminium alloy ; Bronze ; Algorithm ; Composite material ; Artificial intelligence ; Computer science ; Machine learning Metallurgy and Material Science ; Aluminum Alloy Microstructure Properties ; Corrosion Behavior and Inhibition
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2022-01-03)
Field: Materials Science — Materials Chemistry

Autonomous experiment finds stable fuel-cell material in minutes A robotic experimenter has discovered a stable bismuth oxide that could be useful in fuel cells ( Sci. Adv. 2021, DOI: 10.1126/sciadv.abg4930 ). The researchers who built it say the achievement demonstrates how autonomous synthesis and analysis equipment powered by machine learning can make sense of complex materials science systems faster than humans and traditional methods can. The researchers, at Cornell University, wanted to quickly discover a way to make room temperature stable δ-Bi 2 O 3 . This phase of bismuth oxide has high oxygen-ion conductivity, which would make it a good solid electrolyte for solid oxide fuel cells, but it is only stable between about 725 °C and 825 °C, which precludes easy use. This class of fuel cells efficiently generates electricity directly from hydrocarbons, but high operating temperatures have held the technology back. The autonomous experimenter comprises a laser that anneals thin-film Bi 2 O 3 to Bismuth ; Oxide ; Fuel cells ; Electrolyte ; Electricity ; Computer science ; Materials science ; Chemical engineering ; Nanotechnology ; Process engineering ; Electrical engineering ; Chemistry ; Engineering ; Electrode ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Advanced Data Processing Techniques ; Machine Learning in Materials Science
Authors: Ziyue Zou | Eric R. Beyerle | Sun-Ting Tsai | Pratyush Tiwary
Source: arXiv (Cornell University) (preprint, 2022-10-10)
Field: Materials Science — Materials Chemistry

Driving and characterizing nucleation of urea and glycine polymorphs in water Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights to the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here we employ the machine learning augmented molecular dynamics framework ``Reweighted Autoencoded Variational Bayes for enhanced sampling (RAVE)". We study two molecular systems, urea and glycine in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth liquid-solid transitions of different polymorphs and from these trajectories calculate the polymorph stability relative to the dissolved liquid state. We further observe that the obtained reaction coordinates and transitions are highly non-classical. Molecular dynamics ; Nucleation ; Chemical physics ; Statistical physics ; Crystal (programming language) ; Chemistry ; Water model ; Biological system ; Physics ; Computer science ; Computational chemistry ; Thermodynamics ; Biology Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Jian Luo
Source: arXiv (Cornell University) (preprint, 2022-09-01)
Field: Materials Science — Materials Chemistry

Computing Grain Boundary 'Phase' Diagrams Grain boundaries (GBs) can be treated as two-dimensional (2-D) interfacial phases (also called 'complexions') that can undergo interfacial phase-like transitions. As bulk phase diagrams and calculation of phase diagram (CALPHAD) methods are a foundation for modern materials science, we propose to extend them to GBs to have equally significant impacts. This perspective article reviews a series of studies to compute the GB counterparts to bulk phase diagrams. First, a phenomenological interfacial thermodynamic model was developed to construct GB lambda diagrams to forecast high-temperature GB disordering and related trends in sintering and other properties for both metallic and ceramic materials. In parallel, an Ising-type lattice statistical thermodynamic model was utilized to construct GB adsorption diagrams, which predicted first-order GB adsorption (a.k.a. segregation) transitions and critical phenomena. These two simplified thermodynamic models emphasize on the GB structural (disordering) and chemical (adsorption) aspects, respectively. Subsequently, hybrid Monte Carlo and molecular dynamics atomistic simulations were used to compute more rigorous and accurate GB 'phase' diagrams. Computed GB diagrams of thermodynamic and structural properties were further extended to include mechanical properties. Moreover, machine learning was combined with atomistic simulations to predict GB properties as functions of four independent compositional variables and temperature in a 5-D space for a given GB in high-entropy alloys or as functions of five GB macroscopic (crystallographic) degrees of freedom (DOFs) plus temperature and composition for a binary alloy in a 7-D space. Relevant other studies are examined. Future perspective and outlook, including two emerging fields of high-entropy grain boundaries (HEGBs) and electrically (or electrochemically) induced GB transitions, are discussed. Phase diagram ; Ising model ; Thermodynamics ; CALPHAD ; Materials science ; Grain boundary ; Statistical physics ; Monte Carlo method ; Ternary operation ; Phase (matter) ; Physics ; Mathematics ; Microstructure ; Computer science Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; High-Temperature Coating Behaviors