jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shingo Urata | Nobuhiro Nakamura | Tomofumi Tada | Aik Rui Tan | Rafael Gómez‐Bombarelli | Hideo Hosono
Source: The Journal of Physical Chemistry C (article, 2022-01-21)
Field: Materials Science — Ceramics and Composites

Suppression of Rayleigh Scattering in Silica Glass by Codoping Boron and Fluorine: Molecular Dynamics Simulations with Force-Matching and Neural Network Potentials Rayleigh scattering attributed to the density fluctuation of silica glass is considered as the intrinsic origin of optical loss in glass fiber. Therefore, minimizing the density fluctuation is key to improving the information and telecommunications networks. In this study, classical molecular dynamics (MD) simulations were employed to theoretically examine the effectiveness of codoping boron and fluorine for ameliorating the homogeneity of silica glass. For the MD simulations, the force-matching potential (FMP) with a Buckingham formula was developed by optimizing the parameters to reproduce the force and energy calculated by the density functional theory (DFT). The accuracy of the FMP was confirmed via comparisons with available experimental data as well as glass models constructed using the neural network potential, which was superior in reproducing the force and energy of the DFT data to the FMP. As a result, the small amount of boron and fluorine added to the silica glass was found not to deteriorate the density fluctuation of silica glass. The additives reduce the viscosity of silica glass, which leads to a lower fictive temperature and, thus, to a better homogeneity. Consequently, the codoping of boron and fluorine was suggested as a possible solution to suppress the Rayleigh scattering of optical glass fiber. Rayleigh scattering ; Materials science ; Boron ; Fluorine ; Silica glass ; Molecular dynamics ; Scattering ; Homogeneity (statistics) ; Borate glass ; Work (physics) ; Composite material ; Thermodynamics ; Optics ; Chemistry ; Computational chemistry ; Organic chemistry ; Physics ; Computer science Glass properties and applications ; Photonic Crystal and Fiber Optics ; Spectroscopy and Laser Applications
Authors: Sergei Manzhos | Manabu Ihara
Source: Physchem (article, 2022-03-29)
Field: Materials Science — Materials Chemistry

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies Machine learning (ML) has found increasing use in physical sciences, including research on energy conversion and storage technologies, in particular, so-called sustainable technologies. While often ML is used to directly optimize the parameters or phenomena of interest in the space of features, in this perspective, we focus on using ML to construct objects and methods that help in or enable the modeling of the underlying phenomena. We highlight the need for machine learning from very sparse and unevenly distributed numeric data in multidimensional spaces in these applications. After a brief introduction of some common regression-type machine learning techniques, we focus on more advanced ML techniques which use these known methods as building blocks of more complex schemes and thereby allow working with extremely sparse data and also allow generating insight. Specifically, we will highlight the utility of using representations with subdimensional functions by combining the high-dimensional model representation ansatz with machine learning methods such as neural networks or Gaussian process regressions in applications ranging from heterogeneous catalysis to nuclear energy. Computer science ; Machine learning ; Artificial intelligence ; Perspective (graphical) ; Construct (python library) ; Process (computing) ; Data science Machine Learning in Materials Science ; Machine Learning and ELM ; Fuel Cells and Related Materials
Authors: Kanun Pokharel | James W. Furness | Yi Yao | Volker Blüm | Tom J. P. Irons | Andrew M. Teale | Jianwei Sun
Source: The Journal of Chemical Physics (article, 2022-10-10)
Field: Materials Science — Materials Chemistry

Exact constraints and appropriate norms in machine-learned exchange-correlation functionals Machine learning techniques have received growing attention as an alternative strategy for developing general-purpose density functional approximations, augmenting the historically successful approach of human-designed functionals derived to obey mathematical constraints known for the exact exchange-correlation functional. More recently, efforts have been made to reconcile the two techniques, integrating machine learning and exact-constraint satisfaction. We continue this integrated approach, designing a deep neural network that exploits the exact constraint and appropriate norm philosophy to de-orbitalize the strongly constrained and appropriately normed (SCAN) functional. The deep neural network is trained to replicate the SCAN functional from only electron density and local derivative information, avoiding the use of the orbital-dependent kinetic energy density. The performance and transferability of the machine-learned functional are demonstrated for molecular and periodic systems. Constraint (computer-aided design) ; Computer science ; Artificial intelligence ; Artificial neural network ; Exploit ; Norm (philosophy) ; Machine learning ; Energy functional ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Pavlína Hájková | Jakub Hornı́k | Elena Čižmárová | František Kalianko
Source: Coatings (article, 2022-11-24)
Field: Materials Science — Materials Chemistry

Metallic Materials for Hydrogen Storage—A Brief Overview The research and development of materials suitable for hydrogen storage has received a great deal of attention worldwide. Due to the safety risks involved in the conventional storage of hydrogen in its gaseous or liquid phase in containers and tanks, development has focused on solid-phase hydrogen storage, including metals. Light metal alloys and high-entropy alloys, which have a high potential for hydrogen absorption/desorption at near-standard ambient conditions, are receiving interest. For the development of these alloys, due to the complexity of their compositions, a computational approach using CALPHAD (Calculation of Phases Diagrams) and machine learning (ML) methods that exploit thermodynamic databases of already-known and experimentally verified systems are being increasingly applied. In order to increase the absorption capacity or to decrease the desorption temperature and to stabilize the phase composition, specific material preparation methods (HEBM—high-energy milling, HPT—high-pressure torsion) referred to as activation must be applied for some alloys. Hydrogen storage ; Desorption ; Materials science ; Hydrogen ; Metal ; Process engineering ; Hydrogen fuel ; Alloy ; Metallurgy ; Chemistry ; Adsorption ; Physical chemistry Hydrogen Storage and Materials ; High Entropy Alloys Studies ; High-Temperature Coating Behaviors
Authors: Jiandong Deng | Guozhu Jia
Source: Fluid Phase Equilibria (article, 2022-07-11)
Field: Materials Science — Materials Chemistry

Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning Dielectric ; Chemistry ; Solvation ; Interpretability ; Work (physics) ; Thermodynamics ; Constant (computer programming) ; Feature (linguistics) ; Artificial intelligence ; Statistical physics ; Biological system ; Machine learning ; Solvent ; Organic chemistry ; Materials science ; Computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Н. Г. Бичан | Е. Н. Овченкова | Alexander A. Ksenofontov | В. А. Мозгова | М. С. Груздев | U. V. Chervonova | I. V. Shelaev | Т. Н. Ломова
Source: Dyes and Pigments (article, 2022-06-02)
Field: Materials Science — Materials Chemistry

Meso-carbazole substituted porphyrin complexes: Synthesis and spectral properties according to experiment, DFT calculations and the prediction by machine learning methods Porphyrin ; Carbazole ; Spectral properties ; Chemistry ; Computational chemistry ; Combinatorial chemistry ; Photochemistry Porphyrin and Phthalocyanine Chemistry ; Photodynamic Therapy Research Studies ; Photochemistry and Electron Transfer Studies
Authors: Manura Liyanage | D. Reith | Volker Eyert | W.A. Curtin
Source: Physical Review Materials (article, 2022-06-24)
Field: Materials Science — Materials Chemistry

Machine learning for metallurgy V: A neural-network potential for zirconium The mechanical performance---including deformation, fracture and radiation damage---of zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the nuclear industry, understanding that atomic scale behavior is crucial. The defects controlling that performance are at size scales far larger than accessible by first principles methods, necessitating the use of semiempirical interatomic potentials. Existing potentials for Zr are not sufficiently quantitative, nor easily extendable to alloys, oxides, or hydrides. To overcome these issues, a neural network machine learning potential (NNP) is developed here within the Behler-Parrinello framework for Zr. With a careful choice of descriptors of the atomic environments and the creation of a first-principles training dataset that includes a wide spectrum of configurations of metallurgical relevance, a very accurate NNP is demonstrated. Specifically, the Zr NNP yields a good description of dislocation structures and their relative energies and fracture behavior, along with bulk, surface, and point-defect properties and structures, and significantly outperforms the best available traditional potentials. Results here will enable large-scale simulations of complex processes and provide the basis for future extensions to alloys, oxides, and hydrides. Materials science ; Zirconium ; Atomic units ; Scale (ratio) ; Deformation (meteorology) ; Fracture (geology) ; Artificial neural network ; Interatomic potential ; Dislocation ; Zirconium alloy ; Metallurgy ; Computer science ; Machine learning ; Molecular dynamics ; Computational chemistry ; Composite material ; Physics Nuclear Materials and Properties ; Fusion materials and technologies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Charlotte Loh | Thomas Christensen | Rumen Dangovski | Samuel Kim | Marin Soljačić
Source: Nature Communications (article, 2022-07-21)
Field: Materials Science — Materials Chemistry

Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science Deep learning techniques have been increasingly applied to the natural sciences, e.g., for property prediction and optimization or material discovery. A fundamental ingredient of such approaches is the vast quantity of labeled data needed to train the model. This poses severe challenges in data-scarce settings where obtaining labels requires substantial computational or labor resources. Noting that problems in natural sciences often benefit from easily obtainable auxiliary information sources, we introduce surrogate- and invariance-boosted contrastive learning (SIB-CL), a deep learning framework which incorporates three inexpensive and easily obtainable auxiliary information sources to overcome data scarcity. Specifically, these are: abundant unlabeled data, prior knowledge of symmetries or invariances, and surrogate data obtained at near-zero cost. We demonstrate SIB-CL's effectiveness and generality on various scientific problems, e.g., predicting the density-of-states of 2D photonic crystals and solving the 3D time-independent Schrödinger equation. SIB-CL consistently results in orders of magnitude reduction in the number of labels needed to achieve the same network accuracies. Generality ; Computer science ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Reduction (mathematics) ; Deep learning ; Scarcity ; Data science ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Neural Networks and Reservoir Computing
Authors: Olukunle C. Olawole | D. K. De | O.F. Olawole | Ravita Lamba | Emily Joel | Sunday O. Oyedepo | A.A. Ajayi | O.A. Adegbite | Fabian I. Ezema | Samira Naghdi | T.D. Olawole | Oluwafunke Obembe | K.O. Oguniran
Source: Heliyon (review, 2022-10-01)
Field: Materials Science — Materials Chemistry

Progress in the experimental and computational methods of work function evaluation of materials: A review The work function, which determines the behaviour of electrons in a material, remains a crucial factor in surface science to understand the corrosion rates and interfacial engineering in making photosensitive and electron-emitting devices. The present article reviews the various experimental methods and theoretical models employed for work function measurement along with their merits and demerits are discussed. Reports from the existing methods of work function measurements that Kelvin probe force microscopy (KPFM) is the most suitable measurement technique over other experimental methods. It has been observed from the literature that the computational methods that are capable of predicting the work functions of different metals have a higher computational cost. However, the stabilized Jellium model (SJM) has the potential to predict the work function of transition metals, simple metals, rare-earth metals and inner transition metals. The metallic plasma model (MPM) can predict polycrystalline metals, while the density functional theory (DFT) is a versatile tool for predicting the lowest and highest work function of the material with higher computational cost. The high-throughput density functional theory and machine learning (HTDFTML) tools are suitable for predicting the lowest and highest work functions of extreme material surfaces with cheaper computational cost. The combined Bayesian machine learning and first principle (CBMLFP) is suitable for predicting the lowest and highest work functions of the materials with a very low computational cost. Conclusively, HTDFTML and CBMLFP should be used to explore the work functions and surface energy in complex materials. Work function ; Kelvin probe force microscope ; Work (physics) ; Density functional theory ; Function (biology) ; Computational model ; Jellium ; Computer science ; Materials science ; Nanotechnology ; Mechanical engineering ; Chemistry ; Artificial intelligence ; Computational chemistry ; Metal ; Metallurgy ; Engineering ; Atomic force microscopy Corrosion Behavior and Inhibition ; Semiconductor materials and devices ; Metal and Thin Film Mechanics
Authors: Wai Tong Chung | Ki Sung Jung | Jacqueline H. Chen | Matthias Ihme
Source: Applications in Energy and Combustion Science (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

BLASTNet: A call for community-involved big data in combustion machine learning Many state-of-the-art machine learning (ML) fields rely on large datasets and massive deep learning models (with O(109) trainable parameters) to predict target variables accurately without overfitting. Within combustion, a wealth of data exists in the form of high-fidelity simulation data and detailed measurements that have been accumulating since the past decade. Yet, this data remains distributed and can be difficult to access. In this work, we present a realistic and feasible framework which combines (i) community involvement, (ii) public data repositories, and (iii) lossy compression algorithms for enabling broad access to high-fidelity data via a network-of-datasets approach. This Bearable Large Accessible Scientific Training Network-of-Datasets (BLASTNet) is consolidated on a community-hosted web-platform (at https://blastnet.github.io/), and is targeted towards improving accessibility to diverse scientific data for deep learning algorithms. For datasets that exceed the storage limitations in public ML repositories, we propose employing lossy compression algorithms on high-fidelity data, at the cost of introducing controllable amounts of error to the data. This framework leverages the well-known robustness of modern deep learning methods to noisy data, which we demonstrate is also applicable in combustion by training deep learning models on lossy direct numerical simulation (DNS) data in two completely different ML problems — one in combustion regime classification and the other in filtered reaction rate regression. Our results show that combustion DNS data can be compressed by at least 10-fold without affecting deep learning models, and that the resulting lossy errors can even improve their training. We thus call on the research community to help contribute to opening a bearable pathway towards accessible big data in combustion. Lossy compression ; Computer science ; Overfitting ; Fidelity ; Deep learning ; Machine learning ; Robustness (evolution) ; Artificial intelligence ; High fidelity ; Big data ; Data mining ; Artificial neural network ; Engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Cloud Computing and Resource Management
Authors: Marius Neumann | Stefan Palkovits
Source: Industrial & Engineering Chemistry Research (article, 2022-03-08)
Field: Materials Science — Materials Chemistry

Reinforcement Learning Approaches for the Optimization of the Partial Oxidation Reaction of Methane Optimizing reactions in the chemical industry is one of the major challenges in the pursuit of economic and ecological sustainability. With ongoing research in this field, the amount of available data has greatly increased, which makes it suitable for machine learning approaches. In this paper, the application of reinforcement learning for finding optimal reaction conditions of the partial oxidation of methane (POX) is tested. Q-learning (QL) agents and deep deterministic policy gradient (DDPG) agents are trained to maximize H2 production by partial oxidation of methane in a simulated plug flow reactor. Although the QL agent showed promising results in a simplified environment, it was not able to achieve improvements in the simulation environment. A clear superiority of the DDPG agent was observed, as it was able to maximize H2 production by adjusting temperature, pressure, flow velocity, and substrate composition. This proves that reinforcement learning is applicable for reaction optimization and a promising concept to improve efficiency in chemical processes. Reinforcement learning ; Partial oxidation ; Methane ; Computer science ; Reinforcement ; Substrate (aquarium) ; Flow (mathematics) ; Biochemical engineering ; Chemistry ; Process engineering ; Artificial intelligence ; Materials science ; Organic chemistry ; Engineering ; Ecology ; Mathematics Catalytic Processes in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Control Systems Optimization
Authors: Jesse Melville | Cal Hargis | Michael T. Davenport | R. Spencer Hamilton | Daniel H. Ess
Source: Journal of Physical Organic Chemistry (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Machine learning analysis of dynamic‐dependent bond formation in trajectories with consecutive transition states Abstract Dynamic motion often controls selectivity in reactions featuring two consecutive potential‐energy transition states. Here we report density functional theory (DFT)‐based direct dynamics trajectories and machine learning classification analysis for cyclopentadienone dimerization and a N 2 extrusion reaction leading to semibullvalene. These reactions have consecutive transition states, and there is dynamic selectivity that determines which of two possible C‐C bonds is formed after the first transition state. For cyclopentadienone dimerization with a bispericyclic first transition state, machine learning analysis using transition‐state based features provided &gt;90% trajectory classification accuracy, but only using AdaBoost and random forest algorithms. Many other relatively sophisticated machine learning algorithms showed poor accuracy despite the obvious motion responsible for selectivity. Feature importance analysis confirmed that the sigmatropic rearrangement vibrational motion in the bispericyclic transition state provides prediction of which of the second C‐C bonds is dynamically formed. For the reaction leading to semibullvalene, machine learning analysis provides solid accuracy for classifying trajectories and predicting which C‐C bond is formed and which C‐C bond is broken immediately after N 2 ejection. Like the cyclopentadienone dimerization reaction, machine learning feature importance analysis showed that the sigmatropic rearrangement vibrational motion in the N 2 extrusion transition state determines which C‐C bond is formed and which is broken. Surprisingly, machine learning struggles to predict which trajectories undergo a subsequent [3,3] sigmatropic rearrangement process, which isomerizes equivalent forms of semibullvalene. Chemistry ; Sigmatropic reaction ; Transition state ; Feature (linguistics) ; Motion (physics) ; Q-learning ; Computational chemistry ; AdaBoost ; Artificial intelligence ; Machine learning ; Algorithm ; Stereochemistry ; Reinforcement learning ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Pengju Wang | Jianpei Xing | Xue Jiang | Jijun Zhao
Source: ACS Applied Materials & Interfaces (article, 2022-07-13)
Field: Materials Science — Materials Chemistry

Transition-Metal Interlink Neural Network: Machine Learning of 2D Metal–Organic Frameworks with High Magnetic Anisotropy Two-dimensional (2D) metal-organic framework (MOF) materials with large perpendicular magnetic anisotropy energy (MAE) are important candidates for high-density magnetic storage. The MAE-targeted high-throughput screening of 2D MOFs is currently limited by the time-consuming electronic structure calculations. In this study, a machine learning model, namely, transition-metal interlink neural network (TMINN) based on a database with 1440 2D MOF materials is developed to quickly and accurately predict MAE. The well-trained TMINN model for MAE successfully captures the general correlation between the geometrical configurations and the MAEs. We explore the MAEs of 2583 other 2D MOFs using our trained TMINN model. From these two databases, we obtain 11 unreported 2D ferromagnetic MOFs with MAEs over 35 meV/atom, which are further demonstrated by the high-level density functional theory calculations. Such results show good performance of the extrapolation predictions of TMINN. We also propose some simple design rules to acquire 2D MOFs with large MAEs by building a Pearson correlation coefficient map between various geometrical descriptors and MAE. Our developed TMINN model provides a powerful tool for high-throughput screening and intentional design of 2D magnetic MOFs with large MAE. Extrapolation ; Materials science ; Ferromagnetism ; Artificial neural network ; Density functional theory ; Anisotropy ; Artificial intelligence ; Correlation coefficient ; Metal-organic framework ; Magnetism ; Throughput ; Computer science ; Magnetic anisotropy ; Biological system ; Machine learning ; Condensed matter physics ; Computational chemistry ; Physics ; Magnetic field ; Adsorption ; Mathematics ; Magnetization ; Physical chemistry ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; 2D Materials and Applications