jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saffat Bokul | Samiha Sabrin Md Abdus Shukur | Saquib Ahmed | Tonmoy Bhowmick | Md. Ashraful Alam
Source: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (article, 2020-12-16)
Field: Materials Science — Materials Chemistry

Classification of Non-Topological Magnetic Configurations Using Machine Learning Electron microscopy equipment is extensively used in semiconductor manufacturing and research to Figure out different magnetic configurations in materials. Machine learning is used to carry out efficient studies and analyses in the field of condensed matter physics. This paper proposes a machine learning approach that classifies between different non-topological magnetic configurations. We propose multiple machine learning models that are trained on data that has been generated based on the physical properties of respective magnetic structures. Multi-class classification between non-topological structures such as Ferromagnets, Anti-Ferromagnets & Spin-spiral were done to study how machine learning techniques perform on this task. To accomplish this, convolutional neural network (CNN) and support vector machine (SVM) with principal component analysis (PCA) algorithms have been used. Our experimental results show that both CNN and SVM perform exceptionally well in distinguishing between different non-topological magnetic configurations. Artificial intelligence ; Support vector machine ; Computer science ; Machine learning ; Convolutional neural network ; Artificial neural network ; Topology (electrical circuits) ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Neural Networks and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Shehtab Zaman | Christopher Owen | Kenneth Chiu | Michael J. Lawler
Source: arXiv (Cornell University) (preprint, 2020-10-29)
Field: Materials Science — Materials Chemistry

Graph Neural Network for Metal Organic Framework Potential Energy Approximation Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal ions and organic linkers. MOFs play an important role in industrial applications such as gas separation, gas purification, and electrolytic catalysis. Important MOF properties such as potential energy are currently computed via techniques such as density functional theory (DFT). Although DFT provides accurate results, it is computationally costly. We propose a machine learning approach for estimating the potential energy of candidate MOFs, decomposing it into separate pair-wise atomic interactions using a graph neural network. Such a technique will allow high-throughput screening of candidates MOFs. We also generate a database of 50,000 spatial configurations and high-quality potential energy values using DFT. Artificial neural network ; Computer science ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science
Authors: Nicholas A. Jose | Mikhail Kovalev | Eric Bradford | Artur M. Schweidtmann | Hua Chun Zeng | Alexei A. Lapkin
Source:  (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

An accelerated approach for efficient development and scaling of new material technologies, combining flow synthesis with machine learning. Case study: Nanostructured ZnO for antibacterial coatings Scaling ; Computer science ; Flow (mathematics) ; Nanotechnology ; Materials science ; Mathematics Machine Learning in Materials Science
Authors: Marc Philipp Bahlke | Natnael Mogos | Jonny Proppe | Carmen Herrmann
Source: ChemRxiv (preprint, 2020-07-08)
Field: Materials Science — Materials Chemistry

Exchange Spin Coupling from Gaussian Process Regression Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science. Kriging ; Extrapolation ; Artificial intelligence ; Machine learning ; Computer science ; Dipole ; Determinantal point process ; Kernel method ; Statistical physics ; Mathematics ; Physics ; Support vector machine ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Marc Philipp Bahlke | Natnael Mogos | Jonny Proppe | Carmen Herrmann
Source: ChemRxiv (preprint, 2020-07-03)
Field: Materials Science — Materials Chemistry

Exchange Spin Coupling from Gaussian Process Regression Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science. Kriging ; Extrapolation ; Artificial intelligence ; Machine learning ; Computer science ; Determinantal point process ; Dipole ; Kernel method ; Statistical physics ; Mathematics ; Physics ; Support vector machine ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Marc Philipp Bahlke | Natnael Mogos | Jonny Proppe | Carmen Herrmann
Source: ChemRxiv (preprint, 2020-07-01)
Field: Materials Science — Materials Chemistry

Exchange Spin Coupling fromGaussian Progress Regression Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science. Extrapolation ; Computer science ; Kriging ; Artificial intelligence ; Machine learning ; Kernel method ; Dipole ; Theoretical computer science ; Mathematics ; Physics ; Quantum mechanics ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Hiroki Nakamura
Source: Atomic Energy Society of Japan (article, 2020-01-22)
Field: Chemistry — Inorganic Chemistry

Evaluation of high-temperature properties of CaF 2 with machine-learning molecular dynamics Molecular dynamics ; Dynamics (music) ; Materials science ; Nuclear engineering ; Chemistry ; Computer science ; Physics ; Computational chemistry ; Engineering Inorganic Fluorides and Related Compounds ; Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis
Authors: Yury Suleymanov
Source: Science (article, 2020-05-14)
Field: Materials Science — Materials Chemistry

Electric dipole catalytic descriptor Surface Science Identifying correlations between certain properties of heterogeneous systems and their catalytic performance is crucial for the development of efficient screening methods to generate new leads for catalysts with superior properties. Using machine-learning neural networks trained on density functional theory simulations, Wang et al. show that the electric dipole and its related parameters can accurately predict the key parameters of surface-molecular adsorbate interactions: molecular adsorption energy and transferred charge. The predictive power of the proposed neural network models is comparable to the widely used d-band theory. The transferability of these models between different heterogeneous systems suggests that the electric dipole moment is a promising new type of catalytic descriptor. J. Am. Chem. Soc. 142 , 7737 (2020). Dipole ; Transferability ; Artificial neural network ; Moment (physics) ; Electric dipole moment ; Computer science ; Density functional theory ; Materials science ; Statistical physics ; Physics ; Chemistry ; Artificial intelligence ; Machine learning ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science
Authors: H. Mokrane
Source: International Journal of Advancements in Technology (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Astrochemistry overview and challenges: Machine Learning Approach Astrochemistry is the field studying chemical processes in astronomical environments. It is rapidly evolving and explores the issue of the formation of molecules of increasing complexity in space and particular physical conditions that deviate significantly from those frequently encountered in chemistry laboratories. The presence of this molecular diversity constitutes a firm evidence that efficient formation processes in the interstellar medium. This talk aims to introduce a new methodology and new approach for studying astrochemistry and present ideas that are explored by astrochemists to investigate the chemistry taking place in various astronomical environments. The more ambitious question of the molecular complexity is addressed, the issue of molecular complexity constitutes one of the main modern scientific questions addressed by astrochemistry. More than 200 different species detected up today in the interstellar and circumstellar media have also been identified in icy environments. The formation of water molecules from the reaction between O3 and D-atoms is studied experimentally for the first time. Ozone is deposited on non-porous amorphous solid water ice, and D-atoms are then sent onto the sample held at 10 K. HDO molecules are detected during the desorption of the whole substrate where isotope mixing takes place, indicating that water synthesis has occurred. The efficiency of water formation via hydrogenation of ozone is of the same order of magnitude as that found for reactions involving O-atoms or O2 molecules and exhibits no apparent activation barrier. These experiments validate the assumption made by models using ozone as one of the precursors of water formation via solid-state chemistry on interstellar dust grains. The fact that, for most of the species observed so far in the ISM, the most abundant isomer of a given generic chemical formula is the most stable one (MEP) suffers very few exceptions. Two couples of isomers, CH3COOH/HCOOCH3 and CH3CH2OH/CH3OCH3 whose formation is thought to occur on the icy mantles of interstellar grains. Here I applied machine learning and AI to build and improve molecular reactions and I will introduce a new methodology in astrochemistry. Astrochemistry ; Astrobiology ; Molecule ; Chemical physics ; Amorphous solid ; Interstellar medium ; Chemistry ; Nanotechnology ; Physics ; Materials science ; Astrophysics ; Organic chemistry Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; SAS software applications and methods
Authors: Seoin Back | Jonggeol Na | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2020-10-13)
Field: Materials Science — Materials Chemistry

An Efficient Discovery of Active, Selective and Stable Catalysts for Electrochemical H2O2 Synthesis Through Active Motif Screening Electrochemical reduction of O2 provides a clean and decentralized pathway to produce H2O2 compared to the current energy-intensive anthraquinone process. As the electrochemical reduction of O2 proceeds via either two-electron or four-electron path- way, it is thus essential to control the selectivity as well as to maximize the catalytic activity. Siahrostami et al. demonstrated a novel approach to control the reaction pathway by optimizing an adsorption ensemble to tune adsorption sites of reaction intermediates, and identified Pt-Hg catalysts from density functional theory (DFT) calculations and experimentally validated this catalyst (Nat. Mater. 2013, 12, 1137). Inspired by this concept, in this work, we apply a state-of-the-art high-throughput screening to develop O2 reduction catalyst for selective H2O2 production. Starting from Materials Project database, we evaluate activity, selectivity and electrochemical stability. To efficiently perform the screening, we introduce an active motif based approach which pre-screens unpromising materials and only performs DFT calculations for promising materials, which significantly reduce the number of the required calculations. We not only provide a list of promising candidates identified by DFT calculations, but also suggest element species to achieve high catalytic activity or H2O2 selectivity for future experimental attempts. Finally, we discuss a strategy for efficient future high-throughput screening using a machine learning pipeline consisting of a non-linear dimension reduction and a density-based clustering. Catalysis ; Density functional theory ; Electrochemistry ; Combinatorial chemistry ; Selectivity ; Chemistry ; Computer science ; High-throughput screening ; Nanotechnology ; Materials science ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: J. Zhang | Danni Wei | Feng Zhang | Xi Chen | Dawei Wang
Source: arXiv (Cornell University) (preprint, 2020-12-21)
Field: Materials Science — Materials Chemistry

Structural phase transition of two-dimensional monolayer SnTe from artificial neural network As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in ferroelectric materials. Here, we build and train an artificial neural network to accurately predict the energy change associated with atom displacements and use the trained artificial neural network in Monte-Carlo simulations on ferroelectric materials to investigate their phase transitions. We apply this approach to two-dimensional monolayer SnTe and show that it can indeed be used to simulate the phase transitions and predict the transition temperature. The artificial neural network, when viewed as a universal mathematical structure, can be readily transferred to the investigation of other ferroelectric materials when training data generated with ab initio methods are available. Artificial neural network ; Phase transition ; Monolayer ; Ferroelectricity ; Phase (matter) ; Artificial intelligence ; Computer science ; Ab initio ; Statistical physics ; Materials science ; Monte Carlo method ; Machine learning ; Condensed matter physics ; Nanotechnology ; Physics ; Mathematics ; Optoelectronics Machine Learning in Materials Science
Authors: Lixin Sun | Jonathan Vandermause | Simon Batzner | Yu Xie | David J. Clark | Wei Chen | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2020-12-07)
Field: Materials Science — Materials Chemistry

Multitask machine learning of collective variables for enhanced sampling of rare events Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space, and separate downstream parts map the latent space to predictions of basin class labels and potential energies. The resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, and alanine dipeptide in vacuum. This approach enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders. Computer science ; Artificial intelligence ; Curse of dimensionality ; Dimensionality reduction ; Adaptive sampling ; Machine learning ; Chemical space ; Latent variable ; Artificial neural network ; Mathematics ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Kota Suzuki
Source: The Japan Institute of Metals and Materials (article, 2020-08-04)
Field: Materials Science — Materials Chemistry

[Keynote Lecture] Material search method of lithium ionic conductors using machine learning technique Lithium (medication) ; Electrical conductor ; Computer science ; Ionic bonding ; Artificial intelligence ; Materials science ; Machine learning ; Chemistry ; Psychology ; Composite material ; Ion Machine Learning in Materials Science
Authors: Christine Cardinal Roberts | Eric Allcorn | Angelica Benavidez | Patrick F. Fleig | Fernando H. Garzón | Mohan Karulkar | Martin B. Nemer | Mark A. Rodriguez
Source: ECS Meeting Abstracts (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

(Invited) Solid Electrolytes As Thermally Activated Ionic Switches Inorganic solid electrolytes are naturally less ionically conductive at room temperature than at elevated temperatures where they can be superionic. This intrinsic Arrhenius-type behavior is useful for niche applications that require a thermally activated switch, for example in batteries with long room temperature storage lifetimes, geothermal batteries, or novel circuits. We seek electrochemically stable materials with poor room temperature ionic conductivities and high activation energies, starting with the lithium-stuffed garnet class of solid electrolytes (Li 5+x M 3 N 2 O 12+0.5x ). To efficiently identify attractive dopants, we seeded a machine learning algorithm with attributes published in the available literature. This approach confirmed the lithium concentration and dopant electronegativities are important parameters in materials design. We then have explored these predicted relationships through garnet materials synthesis and electrochemical impedance spectroscopy measurements up to 400 o C. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. Electronegativity ; Electrolyte ; Fast ion conductor ; Materials science ; Lithium (medication) ; Dopant ; Ionic bonding ; Ionic conductivity ; Arrhenius equation ; Engineering physics ; Chemical engineering ; Ion ; Nanotechnology ; Activation energy ; Chemistry ; Optoelectronics ; Doping ; Engineering ; Physical chemistry ; Electrode Machine Learning in Materials Science
Authors: Grégory Fonseca | Igor Poltavskyi | Alexandre Tkatchenko
Source: Bulletin of the American Physical Society (article, 2020-03-03)
Field: Materials Science — Materials Chemistry

Unbiasing machine learning for molecular dynamics: emphasising out-of-equilibrium geometries using clustering Cluster analysis ; Dynamics (music) ; Statistical physics ; Molecular dynamics ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science