jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: X. L. Lei | Andrew J. Medford
Source: arXiv (Cornell University) (preprint, 2021-02-04)
Field: Materials Science — Materials Chemistry

A Universal Framework for Featurization of Atomistic Systems Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields. Computer science ; Multipole expansion ; Fast multipole method ; Force field (fiction) ; Artificial intelligence ; Machine learning ; Statistical physics ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Quantum, superfluid, helium dynamics
Authors: Yildirima | Gök | Harindu Hemasiri | Eren | Kazim | Uygun Oksuz | Ahmad
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-04-28)
Field: Materials Science — Polymers and Plastics

Machine Learning Approach for Metal Oxide based Polymer Composites as Charge Selective Layers in Perovskite Solar Cells A library of metal oxide-conjugated polymer composites was synthesized, encompassing of WO<sub>3</sub>-polyaniline (PANI), WO<sub>3</sub>-poly(<em>N-</em>methylaniline) (PMANI), WO<sub>3</sub>-poly(2-fluoroaniline) (PFANI), WO<sub>3</sub>-polythiophene (PTh), WO<sub>3</sub>-polyfuran (PFu) and WO<sub>3</sub>-poly(3,4-ethylenedioxythiophene) (PEDOT). These composites were probed as hole selective layers for perovskite solar cells (PSCs) fabrication. We adopted machine learning approaches to predict and compare PSCs performances with the developed WO<sub>3</sub> and its composites. The experimental and theoretical results are coherent, when the electro-optical properties of PSC were computed. Notably, for the evaluation of PSCs performance, decision tree model is the ideal for WO<sub>3</sub>-PEDOT composite, while random forest model was found to be suitable for WO<sub>3</sub>-PMANI, WO<sub>3</sub>-PFANI, WO<sub>3</sub>-PFu. While in the case for WO<sub>3</sub>, WO<sub>3</sub>-PANI and WO<sub>3</sub>-PTh, K Nearest Neighbors model was appropriate. Machine learning models can be a pioneering prediction models for the PSCs performance and its validation. Perovskite (structure) ; Materials science ; Metal ; Composite material ; Oxide ; Charge (physics) ; Polymer ; Chemical engineering ; Metallurgy ; Engineering ; Physics Conducting polymers and applications
Authors: Yingfang Yuan | Wenjun Wang | Wei Pang
Source: arXiv (Cornell University) (preprint, 2021-04-13)
Field: Materials Science — Materials Chemistry

Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction Recently, the study of graph neural network (GNN) has attracted much attention and achieved promising performance in molecular property prediction. Most GNNs for molecular property prediction are proposed based on the idea of learning the representations for the nodes by aggregating the information of their neighbor nodes (e.g. atoms). Then, the representations can be passed to subsequent layers to deal with individual downstream tasks. Therefore, the architectures of GNNs can be considered as being composed of two core parts: graph-related layers and task-specific layers. Facing real-world molecular problems, the hyperparameter optimization for those layers are vital. Hyperparameter optimization (HPO) becomes expensive in this situation because evaluating candidate solutions requires massive computational resources to train and validate models. Furthermore, a larger search space often makes the HPO problems more challenging. In this research, we focus on the impact of selecting two types of GNN hyperparameters, those belonging to graph-related layers and those of task-specific layers, on the performance of GNN for molecular property prediction. In our experiments. we employed a state-of-the-art evolutionary algorithm (i.e., CMA-ES) for HPO. The results reveal that optimizing the two types of hyperparameters separately can gain the improvements on GNNs' performance, but optimising both types of hyperparameters simultaneously will lead to predominant improvements. Meanwhile, our study also further confirms the importance of HPO for GNNs in molecular property prediction problems. Hyperparameter ; Computer science ; Graph ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Task (project management) ; Artificial neural network ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zebin Wu
Source: arXiv (Cornell University) (preprint, 2021-03-06)
Field: Materials Science — Materials Chemistry

Autonomous detection of molecular configurations in microscopic images based on deep convolutional neural network In an effort to explore high-throughput processing of microscopic image data, a method based on deep convolutional neural network is proposed. The state-of-the-art computer vision algorithm, Faster R-CNN, was trained for the detection of iron (II) phthalocyanines on Se-terminated Au(111) platform resolved by scanning probe microscopy. The construction of the feature pyramid enables the multi-scale molecule detection in images of different scales from 10 nm to 50 nm. After the detection, the orientation of each molecule is measured by a following program. Based on the statistical distribution of the orientation angles, the preferred adsorption configurations of iron (II) phthalocyanine on the platform are revealed. This method yields high accuracy and recall with F1 score close to 1 after optimization of hyperparameters and training. It is expected to be a feasible solution in the scenarios where autonomous and high-throughput processing of microscopic image data is needed. Convolutional neural network ; Pyramid (geometry) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Orientation (vector space) ; Hyperparameter ; Feature (linguistics) ; Deep learning ; Throughput ; Artificial neural network ; Computer vision ; Optics ; Mathematics ; Physics Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Electrochemical Analysis and Applications
Authors: Ruimin Ma | Hanfeng Zhang | Tengfei Luo
Source: ChemRxiv (preprint, 2021-12-14)
Field: Materials Science — Materials Chemistry

Explore High Thermal Conductivity Amorphous Polymers using Reinforcement Learning Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.4 W/m- K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all newly generated polymers with thermal conductivity > 0.400 W/m-K, we have evaluated their synthesizability by calculating the synthesis accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has a MD-calculated thermal conductivity of 0.693 W/m-K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other materials development tasks for different applications. Thermal conductivity ; Materials science ; Polymer ; Amorphous solid ; Reinforcement learning ; Conductivity ; Leverage (statistics) ; Thermal ; Composite material ; Computer science ; Machine learning ; Thermodynamics ; Physics ; Chemistry ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal properties of materials
Authors: Susumu Fujii | Atsuto Seko
Source: arXiv (Cornell University) (preprint, 2021-05-24)
Field: Materials Science — Materials Chemistry

Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics In silicon, lattice thermal conductivity plays an important role in a wide range of applications such as thermoelectric and microelectronic devices. Grain boundaries (GBs) in polycrystalline silicon can significantly reduce lattice thermal conductivity, but the impact of GB atomic structures on it remains to be elucidated. This study demonstrates accurate predictions of the GB structures, GB energies, and GB phonon properties in silicon using machine learning potentials (MLPs). The results indicate that the MLPs enable robust GB structure searches owing to the fact that the MLPs were developed from a training dataset covering a wide variety of structures. We also investigate lattice thermal conduction at four GB atomic structures using large-scale perturbed molecular dynamics and phonon wave-packet simulations. The comparison of these results indicates that the GB structure dependence of thermal conductivity stems from anharmonic vibrations at GBs rather than from the phonon transmission behavior at GBs. The advantages of the MLPs compared with a typical empirical potential of silicon are also thoroughly investigated. Phonon ; Thermal conductivity ; Silicon ; Materials science ; Anharmonicity ; Microelectronics ; Thermoelectric materials ; Molecular dynamics ; Thermal conduction ; Condensed matter physics ; Grain boundary ; Lattice (music) ; Nanotechnology ; Optoelectronics ; Chemistry ; Physics ; Computational chemistry ; Composite material ; Microstructure Thermal properties of materials ; Silicon and Solar Cell Technologies ; Advancements in Semiconductor Devices and Circuit Design
Authors: Jakob Ropers | Marco M. Mosca | Olga Anosova | Vitaliy Kurlin
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

Introduction to invariant-based machine learning for periodic crystals Invariant (physics) ; Materials science ; Computer science ; Artificial intelligence ; Mathematics ; Mathematical physics X-ray Diffraction in Crystallography
Authors: Evan Walter Clark Spotte‐Smith | Samuel M. Blau | Xiaowei Xie | Hetal Patel | Mingjian Wen | Brandon C. Wood | Shyam Dwaraknath | Kristin A. Persson
Source: ChemRxiv (preprint, 2021-05-21)
Field: Materials Science — Materials Chemistry

Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte(LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural,thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties. Electrolyte ; Lithium (medication) ; Battery (electricity) ; Interphase ; Ion ; Molecule ; Chemistry ; Reactivity (psychology) ; Chemical physics ; Lithium-ion battery ; Computational chemistry ; Physical chemistry ; Physics ; Electrode ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Sihan Yong | Zhuoyuan Zheng | Pingfeng Wang | Yumeng Li
Source:  (preprint, 2021-07-16)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning Assisted Design for Active Cathode Materials Active learning (machine learning) ; Cathode ; Computer science ; Materials science ; Artificial intelligence ; Engineering ; Electrical engineering Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Menno Bokdam | Jonathan Lahnsteiner | D. D. Sarma
Source: arXiv (Cornell University) (preprint, 2021-10-01)
Field: Materials Science — Materials Chemistry

Exploring librational pathways with on-the-fly machine-learning force fields: Methylammonium molecules in MAPbX$_3$ (X=I, Br, Cl) perovskites Two seemingly similar crystal structures of the low-temperature (~100 K) MAPbX$_3$ (X=I,Br,Cl) perovskites, but with different relative Methylammonium (MA) ordering, have appeared as representatives of this orthorhombic phase. Distinguishing them by X-ray diffraction experiments is difficult and conventional first-principles based molecular-dynamics approaches are often too computationally intensive to be feasible. Therefore, to determine the thermodynamically stable structure, we use a recently introduced on-the-fly Machine-Learning Force Field method, which reduces the computation time from years to days. The molecules exhibit a large degree of anharmonic motion depending on temperature: i.e. rattling, twisting and tumbling. We observe the crystal's 'librational pathways' while slowly heating it in isothermal-isobaric simulations. Marked differences in the thermal evolution of structural parameters allow us to determine the real structure of the system via a comparison with experimentally determined crystal structures. Orthorhombic crystal system ; Anharmonicity ; Crystal structure prediction ; Force field (fiction) ; Molecule ; Isothermal process ; Crystal (programming language) ; Molecular dynamics ; Chemical physics ; Crystal structure ; Phase (matter) ; Chemistry ; Diffraction ; Crystallography ; Computational chemistry ; Materials science ; Physics ; Thermodynamics ; Condensed matter physics ; Computer science ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Mingyuan Xu | Tong Zhu | John ZH Zhang
Source: ChemRxiv (preprint, 2021-04-06)
Field: Materials Science — Materials Chemistry

Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly non-trivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural-network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, One can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters and by de-redundancy of a sub-data set of the ANI-1 database. We believe that the ESOINN-DP method provides a novelty idea for the construction of NNPES and especially, the reference datasets, and it can be used for MD simulations of various gas-phase and condensed-phase chemical systems. Artificial neural network ; Computer science ; Redundancy (engineering) ; Chemical space ; Artificial intelligence ; Construct (python library) ; Explosive material ; Machine learning ; Data mining ; Chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Michele Gandolfi | Alessandro Rognoni | Chiara Aieta | Riccardo Conte | Michele Ceotto
Source: arXiv (Cornell University) (preprint, 2021-01-11)
Field: Chemistry — Spectroscopy

Machine Learning for Vibrational Spectroscopy via Divide-and-Conquer\n Semiclassical Initial Value Representation Molecular Dynamics with\n Application to N-Methylacetamide A machine learning algorithm for partitioning the nuclear vibrational space\ninto subspaces is introduced. The subdivision criterion is based on Liouville's\ntheorem, i.e. best preservation of the unitary of the reduced dimensionality\nJacobian determinant within each subspace along a probe full-dimensional\nclassical trajectory. The algorithm is based on the idea of evolutionary\nselection and it is implemented through a probability graph representation of\nthe vibrational space partitioning. We interface this customized version of\ngenetic algorithms with our divide-and-conquer semiclassical initial value\nrepresentation method for calculation of molecular power spectra. First, we\nbenchmark the algorithm by calculating the vibrational power spectra of two\nmodel systems, for which the exact subspace division is known. Then, we apply\nit to the calculation of the power spectrum of methane. Exact calculations and\nfull-dimensional semiclassical spectra of this small molecule are available and\nprovide an additional test of the accuracy of the new approach. Finally, the\nalgorithm is applied to the divide-and-conquer semiclassical calculation of the\npower spectrum of 12-atom trans-N-Methylacetamide.\n Divide and conquer algorithms ; Semiclassical physics ; Curse of dimensionality ; Subspace topology ; Algorithm ; Mathematics ; Statistical physics ; Computer science ; Physics ; Quantum mechanics ; Artificial intelligence ; Quantum Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Spectroscopy and Laser Applications
Authors: Masato Sumita | Kei Terayama | Naoya Suzuki | Shinsuke Ishihara | Ryo Tamura | Mandeep K. Chahal | Daniel T. Payne | Kazuki Yoshizoe | Koji Tsuda
Source: ChemRxiv (preprint, 2021-03-26)
Field: Materials Science — Materials Chemistry

De Novo Creation of a Naked-Eye-Detectable Fluorescent Molecule Based on Quantum-Chemical Computation and Machine Learning Correlations between molecular properties and structures, such as those between the absorption wavelength and conjugate length, are beneficial for designing materials and controlling their properties. However, determining the molecular structures that correlate with the target molecular properties (such as molecular fluorescence) is not an easy task. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum-chemical computation (QC) to develop new fluorescent molecules, which are garnering significant attention in various disciplines. With massive parallel computation (1024 cores, 5 days), DNMG has produced 3,643 candidate molecules within the density functional theory (DFT; one of QC) framework. Among the generated molecules, we have selected an unreported molecule and synthesized it for photoluminescence spectrum measurement. Our experimental verification demonstrated that DNMG can successfully create a new molecule which emits fluorescence detectable by the naked eye, as predicted by the DFT. Molecule ; Fluorescence ; Naked eye ; Density functional theory ; Computation ; Conjugate ; Materials science ; Photoluminescence ; Nanotechnology ; Computer science ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Optics ; Optoelectronics ; Algorithm ; Mathematics ; Quantum mechanics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Photochromic and Fluorescence Chemistry
Authors: Debanjali Chatterjee | Kaustubh Girish Naik | Bairav S. Vishnugopi | Partha P. Mukherjee
Source: ECS Meeting Abstracts (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Microstructure Design of Solid-State Battery Cathodes Microstructure ; Battery (electricity) ; Materials science ; Solid-state ; Cathode ; Metallurgy ; Computer science ; Engineering physics ; Engineering ; Electrical engineering ; Power (physics) ; Physics ; Thermodynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Recycling and Waste Management Techniques
Authors: Leif D. Jacobson | James Stevenson | Farhad Ramezanghorbani | Delaram Ghoreishi | Karl Leswing | Ed Harder | Robert Abel
Source: ChemRxiv (preprint, 2021-08-02)
Field: Materials Science — Materials Chemistry

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions Transferable high dimensional neural network potentials (HDNNP) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrödinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architechture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model which delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semi-empirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters and relative tautomer errors. Molecule ; Artificial neural network ; Potential energy ; Ion ; Torsion (gastropod) ; Extension (predicate logic) ; Organic molecules ; Ionic bonding ; Work (physics) ; Computer science ; Chemistry ; Chemical physics ; Physics ; Biological system ; Artificial intelligence ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics