jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Masato Sumita | Kei Terayama | Ryo Tamura | Koji Tsuda
Source: Journal of Chemical Information and Modeling (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its observable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc.) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC computation. Computation ; Black box ; Python (programming language) ; Computer science ; Observable ; Process (computing) ; Computational science ; Quantum ; Speedup ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Physics ; Parallel computing ; Programming language ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Hobyung Chae | Sukho Seo | Yong Chan Jung | E‐Wen Huang | Jayant Jain | Jun Hyun Han | Soo Yeol Lee
Source: Metallurgical and Materials Transactions A (article, 2022-05-04)
Field: Materials Science — Metals and Alloys

Evaluation of Supercritical Carbon Dioxide Corrosion by High Temperature Oxidation Experiments and Machine Learning Models Austenite ; Materials science ; Corrosion ; Alloy ; Martensite ; Random forest ; Linear regression ; Metallurgy ; Linear correlation ; Decision tree ; Machine learning ; Computer science ; Microstructure ; Mathematics ; Statistics Hydrogen embrittlement and corrosion behaviors in metals ; High-Temperature Coating Behaviors ; Corrosion Behavior and Inhibition
Authors: Zaira Gadzhimagomedova | D. M. Pashkov | D. Yu. Kirsanova | Sergey Soldatov | Maria A. Butakova | А. В. Чернов | А. В. Солдатов
Source: Nanobiotechnology Reports (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Nanostructured Materials The current level of development of artificial intelligence (AI) technologies makes it possible to solve many complex problems just as well as a human does. Importance advances in AI are especially noticeable in machine learning, the methods and algorithms of which are successfully adapted and actively used to solve a wide range of problems, including those in the field of nanotechnology. In modern fields of nanotechnology, it is important to speed up the process of searching for the optimal synthesis parameters when creating new unique nanomaterials. The variety of approaches and techniques in both machine learning and nanotechnology makes it necessary to systematically review current data on the use of AI for solving problems in nanomaterials science at both the stage of computer design and of the chemical synthesis and diagnostics of the resulting nanomaterials. Particular attention is paid to the use of machine-learning technologies for studying the thermal and dynamic properties of nanofluids, the processes of sorption of nanocomposites, the catalytic activity of nanoparticles, and the toxicity of nanoparticles and for solving the problems of nanosensorics, as well as for processing the experimental data obtained during the diagnostics of various characteristics of nanomaterials. Nanotechnology ; Nanomaterials ; Computer science ; Process (computing) ; Variety (cybernetics) ; Field (mathematics) ; Artificial intelligence ; Materials science ; Mathematics Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Electric Power Systems and Control
Authors: Yafei Wang | Bonita Goh | Michael Moorehead | Jason Hattrick‐Simpers | Adrien Couet
Source: Analytical Chemistry (review, 2022-11-24)
Field: Materials Science — Metals and Alloys

High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments Electrochemistry has been used for decades to study materials' degradation in situ in corrosive environments, whether it is in room-temperature chemically aggressive solutions containing halide ions or in high-temperature oxidizing media such as pressurized water, liquid metals, or molten salts. Thus, following the recent surge in high-throughput techniques in materials science, it seems quite natural that high-throughput electrochemistry is being considered to study materials' degradation in extreme environments, with the objective to reduce corrosion resistant alloy development time by orders of magnitude and identify complex degradation mechanisms. However, while there has been considerable interest in the development of high-throughput methods for accelerating the discovery of corrosion resistant materials in different environments, these extreme environments propose formidable and exciting challenges for high-throughput electrochemical instrumentation, characterization, and data analysis. It is the objective of this paper to highlight those challenges, to present relatively new efforts to tackle them, and to develop research perspectives on the future of this exciting field. This Perspective is articulated around four main interconnected topics, which must be conjointly considered to enable corrosion resistant alloy design using high-throughput electrochemical methods: (1) high-throughput processing methods to develop material libraries, (2) high-throughput electrochemical methods for corrosion testing and evaluation, (3) high-throughput machine-learning augmented electrochemical data analysis, and (4) high-throughput autonomous electrochemistry representing the future of accelerated electrochemistry research. Chemistry ; Degradation (telecommunications) ; Electrochemistry ; Throughput ; Nanotechnology ; Environmental chemistry ; Electrode ; Telecommunications Hydrogen embrittlement and corrosion behaviors in metals ; Electrochemical Analysis and Applications ; Machine Learning in Materials Science
Authors: Eric Warnemunde Vertina | N. Aaron Deskins | Emily Sutherland | Oren Mangoubi
Source:  (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Predicting MXene Properties via Machine Learning MXenes are a recently discovered class of 2-D materials which possess a diverse set of electrical, chemical, and physical properties, and have a wide range of applications, including batteries, photovoltaics, and chemical sensors. The properties of a given MXene are determined by its chemical composition, and there are likely an infinite number of possible MXenes. Unfortunately, each MXene is costly and time-consuming to synthesize, and there is a need for machine learning (ML) models which can accurately predict MXene properties and guide synthesis of MXenes with desirable properties. To address this issue, we created interpretable ML models that accurately predict the following MXene properties which have not been previously predicted with ML: Work Function, Fermi Level, Heat of Formation, Density of States at Fermi Level (Density of States), and whether a MXene is magnetic. Our model predicts these properties for novel MXenes which have yet to be synthesized in the lab, and does so using only standard elemental information of the constituent atoms of a given MXene material as input. To train our model, we used experimental data from MXenes synthesized in the lab in previous works and data computed using Density Functional Theory (DFT). To create our model, we first applied Sparse Principal Components Analysis (SPCA) to reduce model dimension while preserving the interpretability of features. Then, Random Forest and XGBoost models were created to predict the specified MXene target properties and to output a feature importance score for input features. XGBoost models had the lowest root-mean-squared-error (RMSE) for each target property, with RMSE values as follows: Work Function, 0.308 J; Heat of Formation, 0.128 eV/atom; Fermi Level, 0.46 eV; Density of States, 1.984 eV <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> . MXenes ; Interpretability ; Random forest ; Computer science ; Machine learning ; Artificial intelligence ; Mean squared error ; Feature (linguistics) ; Biological system ; Materials science ; Mathematics ; Nanotechnology ; Statistics MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: B. Latha | Sheena Christabel Pravin | J. Saranya | E. Manikandan
Source: Computational Toxicology (article, 2022-08-20)
Field: Materials Science — Materials Chemistry

Ensemble super learner based genotoxicity prediction of multi-walled carbon nanotubes Genotoxicity ; Carbon nanotube ; Computer science ; Nanotechnology ; Artificial intelligence ; Computational biology ; Chemistry ; Materials science ; Biology ; Organic chemistry Carbon Nanotubes in Composites ; Nanoparticles: synthesis and applications
Authors: Eleonora Ricci | George Giannakopoulos | Vangelis Karkaletsis | Doros N. Theodorou | Niki Vergadou
Source:  (preprint, 2022-09-07)
Field: Materials Science — Materials Chemistry

Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution. Machine learning techniques have been recently proposed to learn CG particle interactions, i.e. develop CG force fields. Graph representations of molecules and supervised training of a graph convolutional neural network architecture are used to learn the potential of mean force through a force matching scheme. In this work, the force acting on each CG particle is correlated to a learned representation of its local environment that goes under the name of SchNet, constructed via continuous filter convolutions. We explore the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned. Granularity ; Computer science ; Hyperparameter ; Graph ; Representation (politics) ; Artificial intelligence ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Sékou-Oumar Kaba | Siamak Ravanbakhsh
Source: arXiv (Cornell University) (preprint, 2022-11-15)
Field: Materials Science — Materials Chemistry

Equivariant Networks for Crystal Structures Supervised learning with deep models has tremendous potential for applications in materials science. Recently, graph neural networks have been used in this context, drawing direct inspiration from models for molecules. However, materials are typically much more structured than molecules, which is a feature that these models do not leverage. In this work, we introduce a class of models that are equivariant with respect to crystalline symmetry groups. We do this by defining a generalization of the message passing operations that can be used with more general permutation groups, or that can alternatively be seen as defining an expressive convolution operation on the crystal graph. Empirically, these models achieve competitive results with state-of-the-art on property prediction tasks. Leverage (statistics) ; Equivariant map ; Computer science ; Generalization ; Theoretical computer science ; Graph ; Permutation (music) ; Pooling ; Context (archaeology) ; Artificial intelligence ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Rodayna Hmede | Frédéric Chapelle | Yuri Lapusta
Source: Comptes Rendus Mécanique (article, 2022-04-28)
Field: Materials Science — Materials Chemistry

Modeling the butterfly behavior of SMA actuators using neural networks Shape memory alloy (SMA) actuators are an important application of smart materials for robotics. However, the nonlinear behavior of SMA leads to difficulties in real-time simulations using numerical methods. Artificial Intelligence can be used to bypass this problem. In this paper, we study several neural networks (NNs) to model the superelastic or pseudo-elasticity effect (SEE) as well as the shape memory effect (SME) used in SMA. Focusing on antagonistic actuating, we first model a single wire to train the best NN with the proper characteristics that fit the behavior of SEE. Then, we model the SME of two linear antagonistic SMA wires used as an actuator. In both systems, single and antagonistic wires, we train the networks to obtain the stress–strain diagrams representing the behavior. The network type and training algorithm are key factors and are evaluated depending on the RMSE values. As a result, we find that the long short-term memory NN, used with a regression layer on standardized data sets, models the butterfly-shaped behavior of the actuator system with less RMSE value. SMA* ; Shape-memory alloy ; Actuator ; Artificial neural network ; Nonlinear system ; Computer science ; Artificial intelligence ; Smart material ; Control theory (sociology) ; Algorithm ; Materials science ; Control (management) Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Soft Robotics and Applications
Authors: Yiyan Song | Xu‐Cai Wu | Shu-Zong Li | Qingde Sun | Wei‐Bing Zhang
Source: AIP Advances (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

A simple descriptor for magnetic classification of 2D MXene materials Classification of the magnetic state is an essential step to investigate two-dimensional magnetic materials. Combining high-throughput calculations and machine-learning methods, we have classified the magnetic states of 23 825 MXenes in the aNANt database. A simple descriptor, obtained by averaging the product of the element feature, connectivity, and Coulomb matrix, was found to improve the performance of the machine-learning models. Using this descriptor on 4153 data produced using first-principles calculations, predictive machine-learning models were developed and 1432 MXene with a high saturation magnetization were predicted. The proposed descriptor is useful for the magnetic classification of other materials, and the identified magnetic MXene materials can be used as an important reference for further study. MXenes ; Magnetization ; Artificial intelligence ; Computer science ; Feature (linguistics) ; Saturation (graph theory) ; Simple (philosophy) ; Pattern recognition (psychology) ; Machine learning ; Materials science ; Data mining ; Physics ; Mathematics ; Magnetic field ; Nanotechnology MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Zhichao Han | David S. Kammer | Olga Fink
Source: PNAS Nexus (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Learning physics-consistent particle interactions Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hidden in many cases. Machine learning methods have the potential to learn the behavior of interacting particle systems by combining experiments with data analysis methods. However, most existing algorithms focus on learning the kinetics at the particle level. Learning pairwise interaction, e.g., pairwise force or pairwise potential energy, remains an open challenge. Here, we propose an algorithm that adapts the Graph Networks framework, which contains an edge part to learn the pairwise interaction and a node part to model the dynamics at particle level. Different from existing approaches that use neural networks in both parts, we design a deterministic operator in the node part that allows to precisely infer the pairwise interactions that are consistent with underlying physical laws by only being trained to predict the particle acceleration. We test the proposed methodology on multiple datasets and demonstrate that it achieves superior performance in inferring correctly the pairwise interactions while also being consistent with the underlying physics on all the datasets. While the previously proposed approaches are able to be applied as simulators, they fail to infer physically consistent particle interactions that satisfy Newton's laws. Moreover, the proposed physics-induced graph network for particle interaction also outperforms the other baseline models in terms of generalization ability to larger systems and robustness to significant levels of noise. The developed methodology can support a better understanding and discovery of the underlying particle interaction laws, and hence, guide the design of materials with targeted properties. Pairwise comparison ; Computer science ; Graph ; Generalization ; Robustness (evolution) ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mustafa Alsaady | Suhaib Umer Ilyas | Abulhassan Ali | Khuram Maqsood | Yuying Yan | Pau Loke Show
Source: Journal of Inorganic and Organometallic Polymers and Materials (article, 2022-09-22)
Field: Chemistry — Analytical Chemistry

Rheological Characteristics and Environmental Remediation Using Fe3O4–SiC Hybrid Nanomaterials in Heat Transfer Oil: Experimental Evaluation and Modeling Nanofluid ; Materials science ; Rheology ; Viscosity ; Nanomaterials ; Heat transfer ; Shear thinning ; Response surface methodology ; Nanoparticle ; Shear rate ; Chemical engineering ; Composite material ; Thermodynamics ; Nanotechnology ; Chromatography ; Chemistry Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Nanofluid Flow and Heat Transfer
Authors: Sourav Deb | Anik Sahoo | Priyam Mondal | Sujoy Baitalik
Source: Dalton Transactions (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Analysis and prediction of anion- and temperature responsive behaviours of luminescent Ru(<scp>ii</scp>)-terpyridine complexes by using Boolean, fuzzy logic, artificial neural network and adapted neuro fuzzy inference models Anion- and temperature responsive behaviors of three luminescent Ru(II)-terpyridine complexes are utilized here to demonstrate multiple Boolean (BL) and fuzzy logic (FL) operations. Taking advantage of the imidazole NH protons, anion-promoted alteration of the photophysical characteristics of the complexes was thoroughly investigated <i>via</i> absorption, and emission spectral and lifetime measurements. In their free state, the complexes display luminescence representing the "on-state", whereas quenching of luminescence is observed with anions demonstrating the "off-state". Likewise, lowering of temperature induces a substantial increase of luminescence and lifetime demonstrating the "on-state", while the increase of temperature induces a significant decrease of emission intensity and lifetime indicating the "off-state" and the process is reversible in both cases. The complexes thus can act as anion- and temperature-responsive molecular switches. The spectral signatures of the complexes under the influence of anions and temperature were employed to mimic multiple BL and FL functions. Performing very detailed sensing studies by varying the analyte concentration within a wide domain is very tedious, time-consuming and expensive. In order to overcome the lacuna, we implemented machine learning and soft computing tools such as artificial neural networks (ANNs), fuzzy-logic and adaptive neuro-fuzzy inference system (ANFIS) to predict the experimental anion sensing data of the complexes. Luminescence ; Terpyridine ; Fuzzy logic ; Artificial neural network ; Ion ; Adaptive neuro fuzzy inference system ; Computer science ; Materials science ; Biological system ; Chemistry ; Algorithm ; Artificial intelligence ; Fuzzy control system ; Optoelectronics ; Organic chemistry Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Luminescence and Fluorescent Materials
Authors: Jean-Baptiste Charraud | G. Geneste | Marc Torrent | Jean‐Bernard Maillet
Source: The Journal of Chemical Physics (article, 2022-05-04)
Field: Materials Science — Materials Chemistry

Machine learning accelerated random structure searching: Application to yttrium superhydrides The search for new superhydrides, promising materials for both hydrogen storage and high temperature superconductivity, made great progress, thanks to atomistic simulations and Crystal Structure Prediction (CSP) algorithms. When they are combined with Density Functional Theory (DFT), these methods are highly reliable and often match a great part of the experimental results. However, systems of increasing complexity (number of atoms and chemical species) become rapidly challenging as the number of minima to explore grows exponentially with the number of degrees of freedom in the simulation cell. An efficient sampling strategy preserving a sustainable computational cost then remains to be found. We propose such a strategy based on an active-learning process where machine learning potentials and DFT simulations are jointly used, opening the way to the discovery of complex structures. As a proof of concept, this method is applied to the exploration of tin crystal structures under various pressures. We showed that the α phase, not included in the learning process, is correctly retrieved, despite its singular nature of bonding. Moreover, all the expected phases are correctly predicted under pressure (20 and 100 GPa), suggesting the high transferability of our approach. The method has then been applied to the search of yttrium superhydrides (YH<sub>x</sub>) crystal structures under pressure. The YH<sub>6</sub> structure of space group Im-3m is successfully retrieved. However, the exploration of more complex systems leads to the appearance of a large number of structures. The selection of the relevant ones to be included in the active learning process is performed through the analysis of atomic environments and the clustering algorithm. Finally, a metric involving a distance based on x-ray spectra is introduced, which guides the structural search toward experimentally relevant structures. The global process (active-learning and new selection methods) is finally considered to explore more complex and unknown YH<sub>x</sub> phases, unreachable by former CSP algorithms. New complex phases are found, demonstrating the ability of our approach to push back the exponential wall of complexity related to CSP. Maxima and minima ; Computer science ; Density functional theory ; Crystal structure prediction ; Algorithm ; Statistical physics ; Crystal (programming language) ; Machine learning ; Crystal structure ; Tetragonal crystal system ; Artificial intelligence ; Mathematics ; Chemistry ; Computational chemistry ; Physics ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions