jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Po-Hao Chiu | Yan-Lin Yang | Heng‐Kwong Tsao | Yu‐Jane Sheng
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2021-07-04)
Field: Materials Science — Materials Chemistry

Deep learning for predictions of hydrolysis rates and conditional molecular design of esters Hydrolysis ; Autoencoder ; Reaction rate constant ; Similarity (geometry) ; Constant (computer programming) ; Value (mathematics) ; Chemistry ; Work (physics) ; Biological system ; Computer science ; Thermodynamics ; Organic chemistry ; Physics ; Deep learning ; Kinetics ; Artificial intelligence ; Machine learning ; Image (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Philomena Schlexer | Tej S. Choksi | Verena Streibel | Frank Abild‐Pedersen
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition. Nanometre ; Nanotechnology ; Range (aeronautics) ; Nanoparticle ; Stability (learning theory) ; Materials science ; Chemical physics ; Physics ; Computer science ; Machine learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Quantum Dots Synthesis And Properties
Authors: Tom Young | Tristan Johnston-Wood | Volker L. Deringer | Fernanda Duarte
Source: ChemRxiv (preprint, 2021-02-16)
Field: Materials Science — Materials Chemistry

A Transferable Active-Learning Strategy for Reactive Molecular Force Fields Predictive simulations of dynamic processes in molecular systems require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct force-field models for large-scale molecular simulation by fitting to high-level quantum-mechanical data. However, machine-learned force fields generally require considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models for diverse chemical systems can be developed in an autonomous way, requiring only hundreds to a few thousand energy and gradient evaluations on the reference potential-energy surface. Our approach relies on a decomposition of the condensed-phase molecular system into intra- and inter-molecular terms, and on the definition of a prospective error metric to quantify accuracy. We demonstrate applications to a range of molecular systems: from bulk water, organic solvents, and a solvated ion onwards to the description of chemical reactivity, including, a bifurcating Diels–Alder reaction in the gas phase and non-equilibrium dynamics (SN2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for complex and/or reactive molecular systems. Force field (fiction) ; Molecular dynamics ; Computer science ; Metric (unit) ; Field (mathematics) ; Gaussian process ; Potential energy ; Statistical physics ; Gaussian ; Chemistry ; Artificial intelligence ; Computational chemistry ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Protein Structure and Dynamics
Authors: Dong Hyeon Mok | Seoin Back
Source: Journal of Chemical Information and Modeling (article, 2021-08-23)
Field: Materials Science — Materials Chemistry

Atomic Structure-Free Representation of Active Motifs for Expedited Catalyst Discovery To discover new catalysts using density functional theory (DFT) calculations, binding energies of reaction intermediates are considered as descriptors to predict catalytic activities. Recently, machine learning methods have been developed to reduce the number of computationally intensive DFT calculations for a high-throughput screening. These methods require several steps such as bulk structure optimization, surface structure modeling, and active site identification, which could be time-consuming as the number of new candidate materials increases. To bypass these processes, in this work, we report an atomic structure-free representation of active motifs to predict binding energies. We identify binding site atoms and their nearest neighboring atoms positioned in the same layer and the sublayer, and their atomic properties are collected to construct fingerprints. Our method enabled a quicker training (200–400 s using CPU) compared to the previous deep-learning models and predicted CO and H binding energies with mean absolute errors (MAEs) of 0.120 and 0.105 eV, respectively. Our method is also capable of creating all possible active motifs without any DFT calculations and predicting their binding energies using the trained model. The predicted binding energy distributions can suggest promising candidates to accelerate catalyst discovery. Density functional theory ; Binding energy ; Active site ; Catalysis ; Representation (politics) ; Chemistry ; Materials science ; Computational chemistry ; Chemical physics ; Computer science ; Physics ; Atomic physics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Ashwin P. Rao | Phillip R. Jenkins | John D. Auxier | Michael B. Shattan | Anil K. Patnaik
Source: Applied Optics (article, 2021-12-23)
Field: Materials Science — Materials Chemistry

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys. Hyperparameter ; Support vector machine ; Artificial neural network ; Hyperparameter optimization ; Gaussian process ; Kernel (algebra) ; Gaussian Nuclear Materials and Properties ; Radioactive contamination and transfer ; Nuclear reactor physics and engineering
Authors: Pawan K. Tiwari | Mugdha Sahu | Gagan Kumar | Mohsen Ashourian
Source: Computational Intelligence and Neuroscience (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Pivotal Role of Quantum Dots in the Advancement of Healthcare Research The quantum dot is a kind of nanoparticle whose dimension is smaller than the size of a typical nanoparticle ranging from tens of nanometers to a few hundredths of nanometers. The quantum mechanical behavior associated with the quantum dot displays different optical and electronic properties, enabling the quantum dot to find potential applications in a multitude of areas such as solar cells, light-emitting diodes, lasers, and biomedical applications. The objective of this investigation is to explore its fundamentals, synthesis, and applications, especially in the healthcare domain. We have discussed the quantum dot synthesis techniques using chemical methods, namely, wet-chemical methods and vapor-phase methods and plasma processing methods, namely, an ion sputtering method and plasma-enhanced chemical vapor deposition method. We have thoroughly investigated the application of quantum dots in imaging, diagnostics, and gene therapy areas. A significant outcome of this review is to propose quantum dots as a new modality in the treatment of cancer and gene therapeutics in the healthcare domain and the potentials of artificial intelligence to improve their performance via the applications of neural networks. Quantum dot ; Nanotechnology ; Materials science ; Nanoparticle ; Quantum ; Quantum dot laser ; Nanometre ; Optoelectronics ; Diode ; Computer science ; Physics ; Semiconductor laser theory Quantum Dots Synthesis And Properties ; Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques
Authors: Zhilong Wang | Yanqiang Han | Junfei Cai | Sicheng Wu | Jinjin Li
Source: Energy storage materials (article, 2021-11-15)
Field: Materials Science — Materials Chemistry

DeepTMC: A deep learning platform to targeted design doped transition metal compounds Materials science ; Doping ; Transition metal ; Nanotechnology ; Metal ; Engineering physics ; Optoelectronics ; Metallurgy ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Junfan Xia | Yaolong Zhang | Bin Jiang
Source: Chinese Journal of Chemical Physics (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Efficient selection of linearly independent atomic features for accurate machine learning potentials Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost. However, the reliability, speed, and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented. A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation. Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates, based on the correlations that are intrinsic to the training data. Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors, we demonstrate the efficiency and accuracy of this new strategy. The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials. Benchmark (surveying) ; Computer science ; Transferability ; Machine learning ; Representation (politics) ; Artificial neural network ; Artificial intelligence ; Atom (system on chip) ; Set (abstract data type) ; Selection (genetic algorithm) ; Reliability (semiconductor) ; Algorithm ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Joonchul Kim | Eunsong Kim | Kyoungmin Min
Source: Advanced Theory and Simulations (article, 2021-09-04)
Field: Materials Science — Materials Chemistry

Synthesizable Double Perovskite Oxide Search via Machine Learning and High‐Throughput Computational Screening Abstract Double perovskite structures have great potential for applications in batteries, lighting devices, and energy‐harvesting materials. In this study, the synthesizability of ABB′O 3 double perovskite materials is predicted using machine learning. The machine learning algorithms are validated by performing high‐throughput computational screening. First, material properties extracted from the Materials Project database are used as training data to develop models to predict the formation energy and convex hull energy of general inorganic materials. A regression model predicts the formation energy of general inorganic materials with a high accuracy; an R‐squared value equal to 0.98 and a root‐mean‐square error of 0.175 eV atom −1 are recorded. In addition, a classification accuracy for the convex hull energy of 0.77 is calculated, with an F1‐score of 0.771, in a separate model. Both models are employed to estimate the possible synthesizability of 11 763 ABB′O3 structures and their accuracy is further validated by performing first‐principles calculations, whose classification accuracy for the convex hull energy reaches an accuracy of 0.646, with an F1‐score of 0.733. The constructed surrogate model, as well as the materials database, can guide the discovery of synthesizable double perovskite oxide structures. Convex hull ; Perovskite (structure) ; Energy (signal processing) ; Throughput ; Mean squared error ; Computer science ; Hull ; Root mean square ; Algorithm ; Convex optimization ; Atom (system on chip) ; Regression ; Regular polygon ; Oxide ; Artificial intelligence ; Machine learning ; Materials science ; Mathematics ; Statistics ; Engineering ; Geometry ; Parallel computing ; Electrical engineering ; Composite material ; Chemical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Ling Qiao | Jingchuan Zhu | Yuan Wang
Source: Advanced Engineering Materials (article, 2021-02-10)
Field: Materials Science — Materials Chemistry

Modeling of Alloying Effect on Isothermal Transformation: A Case Study for Pearlitic Steel Machine learning (ML) has been rapidly revolutionizing many fields and plays a vital role in materials science. To understand the microstructure of steels and even obtain optimized processing parameters, accurate predictions on the time–temperature–transformation (TTT) diagram are in urgent need. Herein, artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) models are developed and compared for quantitatively predicting typical features, such as the nose tip temperature and incubation period, of TTT diagrams for pearlitic steels. As an important evaluating indicators of prediction accuracy, the correlation coefficient ( R 2 ), the root mean square error (RMSE), and the mean absolute percent error (MAPE) are used to evaluate the proposed models. The results show that the proposed SVM model exhibits higher prediction accuracy compared with the ANN and ELM and demonstrates the advantage of the machine learning approach in predictions of TTT diagrams. Moreover, a series of experiments demonstrate that the proposed SVM predictor performs favorably, with satisfactory prediction accuracy. The microstructures of the isothermal transformation products of the prepared pearlitic steel are investigated using dilatation and microscopy technique. Support vector machine ; Isothermal process ; Materials science ; Mean absolute percentage error ; Artificial neural network ; Mean squared error ; Isothermal transformation diagram ; Microstructure ; Transformation (genetics) ; Artificial intelligence ; Mean absolute error ; Machine learning ; Pattern recognition (psychology) ; Computer science ; Metallurgy ; Statistics ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Taeho Kim | Byoung Hoon You | Song‐Hee Han | Ho Chul Shin | Kee‐Choo Chung | Hwangseo Park
Source: International Journal of Molecular Sciences (article, 2021-10-12)
Field: Chemistry — Spectroscopy

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood-brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability. Quantitative structure–activity relationship ; Artificial neural network ; Blood–brain barrier ; Artificial intelligence ; Computer science ; Computational biology ; Machine learning ; Neuroscience ; Biology ; Central nervous system Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Yifei Xue | Wenliang Ji | Ying Jiang | Ping Yu | Lanqun Mao
Source: Angewandte Chemie (article, 2021-08-19)
Field: Chemistry — Electrochemistry

Deep Learning for Voltammetric Sensing in a Living Animal Brain Abstract Numerous neurochemicals have been implicated in the modulation of brain function, making them appealing analytes for sensors and diagnostics. However, it is a grand challenge to selectively measure multiple neurochemicals simultaneously in vivo because of their great variations in concentrations, dynamic nature, and composition. Herein, we present a deep learning‐based voltammetric sensing platform for the highly selective and simultaneous analysis of three neurochemicals in a living animal brain. The system features a carbon fiber electrode capable of capturing the mixed dynamics of a neurotransmitter, neuromodulator, and ions. Then a powerful deep neural network is employed to resolve individual chemical and spatial‐temporal information. With this, a single electrochemical measurement reveals an interplaying concentration changes of dopamine, ascorbate, and ions in living rat brain, which is unobtainable with existing analytical methodologies. Our strategy provides a powerful means to expedite research in neuroscience and empower sensing‐aided diagnostic applications. Brain function ; Neuroscience ; Computer science ; Nanotechnology ; Chemistry ; Materials science ; Biology Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: M. Vollmar | Gwyndaf Evans
Source: Crystallography Reviews (article, 2021-04-03)
Field: Materials Science — Materials Chemistry

Machine learning applications in macromolecular X-ray crystallography After more than half a century of evolution, machine learning and artificial intelligence, in general, are entering a truly exciting era of broad application in commercial and research sectors. In X-ray crystallography, and its application to structural biology, machine learning is finding a home within expert and automated systems, is forecasting experiment and data analysis outcomes, is predicting whether crystals can be grown and even generating macromolecular structures. This review provides a historical perspective on AI and machine learning, offers an introduction and guide to its application in crystallography and concludes with topical examples of how it is currently influencing macromolecular crystallography. Crystallography ; X-ray ; X-ray crystallography ; Macromolecule ; Materials science ; Computer science ; Chemistry ; Physics ; Diffraction ; Optics ; Biochemistry Enzyme Structure and Function ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Ryan J. DiRisio | Fenris Lu | Anne B. McCoy
Source: The Journal of Physical Chemistry A (article, 2021-06-24)
Field: Materials Science — Materials Chemistry

GPU-Accelerated Neural Network Potential Energy Surfaces for Diffusion Monte Carlo Diffusion Monte Carlo (DMC) provides a powerful method for understanding the vibrational landscape of molecules that are not well-described by conventional methods. The most computationally demanding step of these calculations is the evaluation of the potential energy. In this work, a general approach is developed in which a neural network potential energy surface is trained by using data generated from a small-scale DMC calculation. Once trained, the neural network can be evaluated by using highly parallelizable calls to a graphics processing unit (GPU). The power of this approach is demonstrated for DMC simulations on H<sub>2</sub>O, CH<sub>5</sub><sup>+</sup>, and (H<sub>2</sub>O)<sub>2</sub>. The need to include permutation symmetry in the neural network potentials is explored and incorporated into the molecular descriptors of CH<sub>5</sub><sup>+</sup> and (H<sub>2</sub>O)<sub>2</sub>. It is shown that the zero-point energies and wave functions obtained by using the neural network potentials are nearly identical to the results obtained when using the potential energy surfaces that were used to train the neural networks at a substantial savings in the computational requirements of the simulations. Artificial neural network ; Monte Carlo method ; Computer science ; Graphics processing unit ; Parallelizable manifold ; Computational science ; Potential energy surface ; Energy (signal processing) ; Statistical physics ; Artificial intelligence ; Algorithm ; Physics ; Mathematics ; Molecule ; Parallel computing Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions