jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shambhawi Shambhawi | Gábor Cśanyi | Alexei A. Lapkin
Source: Chemistry - Methods (article, 2021-08-04)
Field: Materials Science — Materials Chemistry

Active Learning Training Strategy for Predicting O Adsorption Free Energy on Perovskite Catalysts using Inexpensive Catalyst Features Abstract Machine learning (ML) based energy prediction models are among the most effective descriptor‐based catalyst screening tools for heterogeneous reaction systems. However, their implementations are limited due to expensive data labelling, ab initio feature evaluation and lack of universal catalyst features, that is, beyond d‐band theory. Herein, we propose an inexpensive geometric feature for application on systems beyond d‐band theory, for example perovskites comprising of s‐, p‐, d‐ and f‐block elements. We outline a workflow that inputs these features into an active learning algorithm that enables effective data labelling, whilst improving prediction accuracies of existing models. We then use batch sampling to define termination criteria and to implement time‐series error forecasting for further reducing the number of expensive data labelling for training. We implement this workflow to train ML models for predicting oxygen adsorption free energy on perovskites and achieve similar, if not better, prediction accuracies as obtained from ab initio features. Workflow ; Feature (linguistics) ; Computer science ; Ab initio ; Energy (signal processing) ; Perovskite (structure) ; Machine learning ; Labelling ; Artificial intelligence ; Chemistry ; Mathematics ; Database Machine Learning in Materials Science ; Topic Modeling ; Electrocatalysts for Energy Conversion
Authors: Samuel P. Niblett | Mirza Galib | David T. Limmer
Source: The Journal of Chemical Physics (preprint, 2021-10-26)
Field: Materials Science — Materials Chemistry

Learning intermolecular forces at liquid–vapor interfaces By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. We find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated. Artificial neural network ; Electrostatics ; Invariant (physics) ; Statistical physics ; Dipole ; Perspective (graphical) ; Simple (philosophy) ; Computer science ; Field (mathematics) ; Translation (biology) ; Physics ; Artificial intelligence ; Mathematics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Theoretical and Computational Physics
Authors: Dávid Péter Kovács | Cas van der Oord | Jiri Kucera | Alice E. A. Allen | Daniel J. Cole | Christoph Ortner | Gábor Cśanyi
Source:  (preprint, 2021-07-19)
Field: Materials Science — Materials Chemistry

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as re- fitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking. Transferability ; Force field (fiction) ; Flexibility (engineering) ; Dihedral angle ; Molecular dynamics ; Cluster (spacecraft) ; Computer science ; Statistical physics ; Molecule ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Artificial intelligence ; Quantum mechanics ; Machine learning ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Peng Yao | Cheng Shang | Zhi‐Pan Liu
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

The dome of gold nanolized for catalysis Gold is noble in bulk but turns out to be a superior catalyst at the nanoscale when supported on oxides, in particular titania. The critical thickness for activity, namely two-layer gold particles on titania, observed two decades ago represents one of the most influential mysteries in the recent history of heterogeneous catalysis. By developing a Bayesian optimization controlled global potential energy surface exploration tool with machine learning potential, here we determine the atomic structures of gold particles within ∼2 nm on a TiO<sub>2</sub> surface. We show that the smallest stable Au nanoparticle is Au<sub>24</sub> which is pinned on the oxygen-rich TiO<sub>2</sub> and exhibits an unprecedented dome architecture made by a single-layer Au sheet but with an apparent two-atomic-layer height. Importantly, this has the highest activity for CO oxidation at room temperature. The physical origin of the high activity is the outstanding electron storage ability of the nano-dome, which activates the lattice oxygen of the oxide. The determined CO oxidation mechanism, the simulated rate and the fitted apparent energy barrier are consistent with known experimental facts, providing key evidence for the presence of both the high-activity Au dome and the low-activity close-packed Au particles in real catalysts. The future direction for the preparation of active and stable Au-based catalysts is thus outlined. Dome (geology) ; Catalysis ; Geology ; Chemistry ; Paleontology ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Kailiang Lu | Dongping Chang | Tian Lu | Xiaobo Ji | Minjie Li | Wencong Lu
Source: Journal of Superconductivity and Novel Magnetism (article, 2021-04-22)
Field: Materials Science — Materials Chemistry

Machine Learning Model for High-Throughput Screening of Perovskite Manganites with the Highest Néel Temperature Antiferromagnetism ; Perovskite (structure) ; Machine learning ; Paramagnetism ; Materials science ; Artificial intelligence ; Condensed matter physics ; Support vector machine ; Computer science ; Algorithm ; Manganite ; Redundancy (engineering) ; Ferromagnetism ; Physics ; Chemistry ; Crystallography Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Stephen Xie | Matthias Rupp | Richard G. Hennig
Source: arXiv (Cornell University) (preprint, 2021-10-01)
Field: Materials Science — Materials Chemistry

Ultra-fast interpretable machine-learning potentials All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long time scales. Computer science ; Eigenvalues and eigenvectors ; Phonon ; Statistical physics ; Atom (system on chip) ; Basis (linear algebra) ; Artificial intelligence ; Machine learning ; Molecular dynamics ; Algorithm ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Saientan Bag | Manuel Konrad | Tobias Schlöder | Pascal Friederich | Wolfgang Wenzel
Source: Journal of Chemical Theory and Computation (article, 2021-10-08)
Field: Materials Science — Materials Chemistry

Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies Adsorption and desorption of molecules are key processes in extraction and purification of biomolecules, engineering of drug carriers, and designing of surface-specific coatings. To understand the adsorption process on the atomic scale, state-of-the-art quantum mechanical and classical simulation methodologies are widely used. However, studying adsorption using a full quantum mechanical treatment is limited to picoseconds simulation timescales, while classical molecular dynamics simulations are limited by the accuracy of the existing force fields. To overcome these challenges, we propose a systematic way to generate flexible, application-specific highly accurate force fields by training artificial neural networks. As a proof of concept, we study the adsorption of the amino acid alanine on graphene and gold (111) surfaces and demonstrate the force field generation methodology in detail. We find that a molecule-specific force field with Lennard-Jones type two-body terms incorporating the 3rd and 7th power of the inverse distances between the atoms of the adsorbent and the surfaces yields optimal results, which is surprisingly different from typical Lennard-Jones potentials used in traditional force fields. Furthermore, we present an efficient and easy-to-train machine learning model that incorporates system-specific three-body (or higher order) interactions that are required, for example, for gold surfaces. Our final machine learning-based force field yields a mean absolute error of less than 4.2 kJ/mol at a speed-up of ∼10<sup>5</sup> times compared to quantum mechanical calculation, which will have a significant impact on the study of adsorption in different research areas. Force field (fiction) ; Quantum ; Adsorption ; Biomolecule ; Computer science ; Molecular dynamics ; Materials science ; Molecule ; Automatic summarization ; Nanotechnology ; Chemical physics ; Statistical physics ; Chemistry ; Computational chemistry ; Physics ; Artificial intelligence ; Physical chemistry ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Thermal properties of materials
Authors: Kevin Kaufmann | Kenneth S. Vecchio
Source: Microscopy and Microanalysis (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

An Acquisition Parameter Study for Machine-Learning-Enabled Electron Backscatter Diffraction Methods within the domain of artificial intelligence are gaining traction for solving a range of materials science objectives, notably the use of deep neural networks for computer vision for the analysis of electron diffraction patterns. An important component of deploying these models is an understanding of the performance as experimental diffraction conditions are varied. This knowledge can inspire confidence in the classifications over a range of operating conditions and identify where performance is degraded. Elucidating the relative impact of each parameter will suggest the most important parameters to vary during the collection of future training data. Knowing which data collection efforts to prioritize is of concern given the time required to collect or simulate vast libraries of diffraction patterns for a wide variety of materials without considering varying any parameters. In this work, five parameters, frame averaging, detector tilt, sample-to-detector distance, accelerating voltage, and pattern resolution, essential to electron diffraction are individually varied during the collection of electron backscatter diffraction patterns to explore the effect on the classifications produced by a deep neural network trained from diffraction patterns captured using a fixed set of parameters. The model is shown to be resilient to nearly all the individual changes examined here. Electron backscatter diffraction ; Diffraction ; Detector ; Electron diffraction ; Computer science ; Artificial neural network ; Data acquisition ; Data collection ; Artificial intelligence ; Optics ; Statistics ; Physics ; Mathematics ; Telecommunications Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Non-Destructive Testing Techniques
Authors: Tymofii Yu. Nikolaienko | Л. А. Булавін
Source: Computational and Theoretical Chemistry (article, 2021-07-26)
Field: Materials Science — Materials Chemistry

The machine-learned radii of atoms Chemistry ; Atomic radius ; Covalent bond ; Intuition ; Algorithm ; Atomic physics ; Physics ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Duy Khanh Ninh | Thi-Ngoc-Canh Doan | Chi Khanh Ninh | Thi Thanh Xuan Nguyen | Nhan Le Thanh
Source:  (article, 2021-01-29)
Field: Chemistry — Analytical Chemistry

Fruit recognition based on near-infrared spectroscopy using deep neural networks Near-infrared (NIR) spectroscopy has been widely used to determine the varieties and chemical properties of agricultural and food products. The major advantage of NIR spectroscopy is that the analysis is carried out in a simple, fast, and non-destructive manner, making it suitable for food applications. As the first step in applying NIR spectroscopy for fruit recognition and analysis in Vietnam, this paper presents deep neural networks (DNNs) based solutions for automatic recognition of several kinds of fruits. We compared two proposed DNN architectures based on Convolutional Neural Network (CNN) and Residual Network (ResNet). Additionally, we proposed feature extraction methods using the first and second derivatives of the Savitzky-Golay (SG) filtered normalized NIR data. Experimental results show that the deep learning approach combined with reasonable feature extraction process can achive the accuracy of approximately 99% for the task of classifying five types of fruits including Apple, Avocado, Dragon Fruit, Guava, and Mango. The ResNet-based model is more compact and has slightly better recognition performance than the CNN-based one. The inclusion of the first and second derivatives of SG-smoothed normalized spectra improves the recognition accuracy of the proposed DNN models by more than 8%. Moreover, the recognition performance of the proposed DNN models surpasses that of traditional classifiers, including k-nearest neighbors, Naive Bayes, and support vector machine. Our proposed methods were proved to be robust against the freshness of fruits, the NIR device's calibration parameters, and the measurement position on the body of fruits. Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Convolutional neural network ; Artificial neural network ; Feature extraction ; Near-infrared spectroscopy ; Deep learning ; Feature (linguistics) ; Support vector machine Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: AkshatKumar Nigam | Robert Pollice | Mario Krenn | Gabriel dos Passos Gomes | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2021-01-06)
Field: Materials Science — Materials Chemistry

Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption. Chemical space ; Computer science ; Generative grammar ; Generative model ; Benchmark (surveying) ; Deep learning ; Tree traversal ; Artificial intelligence ; Interpolation (computer graphics) ; Machine learning ; Virtual screening ; Algorithm ; Theoretical computer science ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Jingbai Li | Rachel Stein | Daniel Adrion | Steven A. Lopez
Source: ChemRxiv (preprint, 2021-07-28)
Field: Materials Science — Materials Chemistry

Machine-learning photodynamics simulations uncover the role of substituent effects on the photochemical formation of cubanes Photochemical [2+2]-photocycloadditions are used to efficiently access strained organic molecular architectures, storing solar energy in chemical bonds. Functionalized [3]-ladderenes have been shown to undergo [2+2]-photocycloadditions to afford cubanes, an energy-dense class of organic molecules. The substituents (e.g., methyl, trifluoromethyl, and cyclopropyl) affect the overall reactivities of these cubane precursors leading to a yield from 1% to 48%. We now integrate single and multireference calculations and our machine-learning-accelerated non-adiabatic molecular dynamics (ML-NAMD) to understand how substituents affect the mechanistic photodynamics of [2+2]-photocycloadditions. Our calculations show that steric clashes destabilize the 4π-electrocyclic ring-opening pathway and minimum energy conical intersections by 0.72–1.15 eV and reaction energies by 0.68–2.34 eV. In contrast, favorable dispersive interactions stabilize the [2+2]-photocycloaddition pathway, lower the conical intersection energies by 0.31–0.85 eV and cubane reaction energies by 0.03–0.82 eV. The 2 ps ML-NAMD trajectories reveal that closed-shell repulsions block a 6π-conrotatory electrocyclic ring-opening pathway with increasing steric bulk. 57% of the methyl-substituted [3]-ladderene trajectories proceed through the 6π-conrotatory electrocyclic ring-opening, whereas the trifluoromethyl- and cyclopropyl-substituted 3-ladderenes chemoselectively proceed through [2+2]-photocycloaddition pathways. The predicted cubane yields (H: 0.4% < CH3: 1% < CF3: 14% < cPr: 20%) match the experimental trend; these substituents pre-distort the reactants to resemble the conical intersection leading to cubane. Conrotatory and disrotatory ; Conical intersection ; Chemistry ; Steric effects ; Cubane ; Ring (chemistry) ; Trifluoromethyl ; Photochemistry ; Substituent ; Electrocyclic reaction ; Antiaromaticity ; Computational chemistry ; Molecule ; Stereochemistry ; Aromaticity ; Organic chemistry Photochromic and Fluorescence Chemistry ; Radical Photochemical Reactions ; Machine Learning in Materials Science
Authors: Yu Zhang | Ruoyu Wang | Xue Wang
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Prediction Method of Wax Deposition Rate in Crude Oil Pipeline Based on RBF Neural Network and Support Vector Machine Wax-bearing crude oil will precipitate wax crystals in pipeline transportation, which will cause hidden dangers and affect the economic benefits of the pipeline. In order to study the complex wax deposition on the pipe wall and calculate the wax deposition under other conditions, this paper uses RBF neural network and support vector machine to predict the wax deposition data in Huachi operation area. The results show that the errors of the two methods meet the requirements. Because support vector machine can model and calculate finite samples, it is found that the accuracy of support vector machine is higher. Wax ; Pipeline (software) ; Support vector machine ; Artificial neural network ; Deposition (geology) ; Petroleum engineering ; Crude oil ; Pipeline transport ; Computer science ; Artificial intelligence ; Materials science ; Engineering ; Mechanical engineering ; Geology ; Composite material Petroleum Processing and Analysis ; Water Quality Monitoring and Analysis