jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Monika Gešvandtnerová | Dario Rocca | Tomáš Bučko
Source: ChemRxiv (preprint, 2021-02-03)
Field: Chemistry — Inorganic Chemistry

Methanol Carbonylation over Acid Mordenite: Insights from Ab Initio Molecular Dynamics and Machine Learning Thermodynamic Perturbation Theory &lt;div&gt;In this work we present a detailed \textit{ab initio} study of the carbonylation reaction of methoxy groups in the zeolite mordenite, as it is the rate determining step in a series of elementary reactions leading to ethanol. &lt;/div&gt;&lt;div&gt;For the first time we employ full molecular dynamics simulations to evaluate free energies of activation for the reactions in side pockets and main channels. Results show that the reaction in the side pocket is preferred and, when dispersion interactions are taken into account, this preference becomes even stronger. This conclusion is confirmed using multiple levels of density functional theory approximations with (PBE-D2, PBE-MBD, and vdW-DF2-B86R) or without (PBE, HSE06) dispersion corrections. These calculations, that in principle would require several demanding molecular dynamics simulations, were made possible at a minimal computational cost by using a newly developed approach that combines thermodynamic perturbation theory with machine learning.&lt;/div&gt; Ab initio ; Carbonylation ; Molecular dynamics ; Density functional theory ; Mordenite ; Perturbation theory (quantum mechanics) ; Computational chemistry ; Chemistry ; Methanol ; Physics ; Catalysis ; Zeolite ; Quantum mechanics ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Bastien Casier | Maurício Chagas da Silva | Michaël Badawi | Fabien Pascale | Tomáš Bučko | Sébastien Lebègue∥ | Dario Rocca
Source: Journal of Computational Chemistry (preprint, 2021-05-19)
Field: Materials Science — Materials Chemistry

Hybrid localized graph kernel for machine learning energy‐related properties of molecules and solids Nowadays, the coupling of electronic structure and machine learning techniques serves as a powerful tool to predict chemical and physical properties of a broad range of systems. With the aim of improving the accuracy of predictions, a large number of representations for molecules and solids for machine learning applications has been developed. In this work we propose a novel descriptor based on the notion of molecular graph. While graphs are largely employed in classification problems in cheminformatics or bioinformatics, they are not often used in regression problem, especially of energy-related properties. Our method is based on a local decomposition of atomic environments and on the hybridization of two kernel functions: a graph kernel contribution that describes the chemical pattern and a Coulomb label contribution that encodes finer details of the local geometry. The accuracy of this new kernel method in energy predictions of molecular and condensed phase systems is demonstrated by considering the popular QM7 and BA10 datasets. These examples show that the hybrid localized graph kernel outperforms traditional approaches such as, for example, the smooth overlap of atomic positions and the Coulomb matrices. Graph kernel ; Cheminformatics ; Kernel (algebra) ; Molecular graph ; Computer science ; Graph ; Coulomb ; String kernel ; Polynomial kernel ; Artificial intelligence ; Range (aeronautics) ; Theoretical computer science ; Kernel method ; Energy (signal processing) ; Machine learning ; Computational chemistry ; Materials science ; Mathematics ; Chemistry ; Support vector machine ; Physics ; Discrete mathematics ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yuri Haraguchi | Yasuhiko Igarashi | Hiroaki Imai | Yuya Oaki
Source: Advanced Theory and Simulations (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling (Adv. Theory Simul. 10/2021) Size-Distribution Control of Exfoliated Nanosheets The downsizing exfoliation process to obtain 2D materials is not easily controlled only by trial and error. In article number 2100158, Yuri Haraguchi, Yuya Oaki, and co-workers demonstrate that the monodispersed and polydispersed nanosheets are selectively synthesized in a limited number of experiments by machine learning combined with their chemical perspectives on small data. Exfoliation joint ; Control (management) ; Process (computing) ; Nanotechnology ; Materials science ; Distribution (mathematics) ; Computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Graphene and Nanomaterials Applications ; Bone Tissue Engineering Materials
Authors: Nan Xu | Chen Li | Yao Shi | Qing Shao | Yi He
Source: arXiv (Cornell University) (preprint, 2021-03-07)
Field: Materials Science — Materials Chemistry

Optimizing Training Data Set for the Machine Learning Potential of Li-Si Alloys via Structural Similarity-based Screening Machine learning potential enables molecular dynamics simulations of systems beyond the capability of traditional force fields. One challenge in developing machine learning potential is how to construct a data set with low sample redundancy. This work investigates the method to optimize the training data set while maintaining the desirable accuracy of the machine learning potential using the structural similarity algorithm. We construct several subsets ranging from 200-1500 sample configurations by selecting representative configurations from a 6183-sample data set using the farthest point sampling method and examine the ability of the machine learning potential trained from the subsets to predict energy, atomic forces and structural properties of Li-Si systems. The simulation results show that the potential developed from 400 configurations can be as accurate as the one developed from the 6183-sample data set. In addition, our computation results highlight that the structure-comparison algorithms can not only effectively remove redundant from training sets, but also achieve an appropriate distribution of samples in training data sets. Computer science ; Redundancy (engineering) ; Artificial intelligence ; Machine learning ; Sample (material) ; Similarity (geometry) ; Set (abstract data type) ; Construct (python library) ; Data set ; Training set ; Data redundancy ; Data mining ; Algorithm Machine Learning in Materials Science
Authors: Petri Uusitalo | Markku Ohenoja | M Sorsa | Zoey Zhu | Kamelia Boodhoo | Abegão Russo | Alexander Jakob | Miha Grilc | B Likozar | K Neocleous
Source:  (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Catalyst Development for Production of Biorenewable Chemicals Using Machine Learning and High-Throughput Experiments Throughput ; Production (economics) ; Biochemical engineering ; Biotechnology ; Computer science ; Environmental science ; Chemistry ; Biology ; Engineering ; Economics Machine Learning in Materials Science
Authors: Ahmed A. Farghaly | Magali Ferrandon | Daniel Schwalbe‐Koda | James Damewood | Jessica Karaguesian | Rafael Gómez‐Bombarelli | Deborah J. Myers
Source: ECS Meeting Abstracts (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Machine Learning Driven Discovery and Optimization of Multi-Component Oxygen Evolution Reaction Catalysts Component (thermodynamics) ; Catalysis ; Oxygen evolution ; Computer science ; Oxygen ; Chemistry ; Biochemical engineering ; Engineering ; Organic chemistry ; Thermodynamics ; Physics ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Hiroya Nakata
Source: arXiv (Cornell University) (preprint, 2021-05-02)
Field: Materials Science — Materials Chemistry

Predicting highly correlated hydride-ion diffusion in SrTiO$_3$ crystals based on the fragment kinetic Monte Carlo method with machine-learning potential Oxyhydrides have drawn attention because of their fast ion conductivity and strong reducing properties. Recently, hydride ion migration in SrTiO$_{3-x}$H$_{x}$ oxyhydride crystals has been investigated, showing that hydride ion migration is blocked by slow oxygen diffusion. In this study, we investigate the hydride-ion migration mechanism using a kinetic Monte Carlo approach to understanding the relationship between the hydride and oxygen ions. The difficulties in applying the method to hydride and oxygen ion migration involve complex changes in the ionic migration barrier, which shifts dynamically depending on the characteristics of the surrounding hydride and oxygen ions. We can predict these complex changes using a machine-learning neural network model. The simulation can then be performed using this model to predict the temperature-dependent ionic-migration behavior. We found that our simulation results with respect to the activation barrier for hydride ion diffusion accorded well with those obtained by experiment. We also found that hydride ion migration is affected by slow oxygen diffusion and that oxygen diffusion is accelerated by changes in the ionic migration barriers. The parallel-processing efficiency of our proposed method was 84.92 \% for our 1,000-CPU implementation, suggesting that the approach should be widely applicable to simulations of ionic migration in crystals at a reasonable computational cost. Hydride ; Diffusion ; Ion ; Ionic bonding ; Kinetic Monte Carlo ; Oxygen ; Chemical physics ; Chemistry ; Diffusion barrier ; Ionic conductivity ; Monte Carlo method ; Inorganic chemistry ; Materials science ; Physical chemistry ; Thermodynamics ; Hydrogen ; Nanotechnology ; Physics ; Electrode ; Organic chemistry Nuclear Materials and Properties ; Advancements in Solid Oxide Fuel Cells
Authors: Soroush Soltani | Taha Roodbar Shojaei | Nasrin Khanian | Thomas Shean Yaw Choong | Nilofar Asim | Yue Zhao
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Method for Modeling of Microwave-Assisted Esterification of PFAD Over Mesoporous TiO &lt;sub&gt;2&lt;/sub&gt;‒ZnO Catalyst Catalysis ; Mesoporous material ; Artificial neural network ; Microwave ; Microwave irradiation ; Materials science ; Chemical engineering ; Chemistry ; Nuclear chemistry ; Organic chemistry ; Computer science ; Engineering ; Artificial intelligence ; Telecommunications Catalytic Processes in Materials Science ; Membrane Separation and Gas Transport ; Chemical Synthesis and Reactions
Authors: Nongnuch Artrith
Source:  (article, 2021-11-10)
Field: Materials Science — Materials Chemistry

Learning What Makes Catalysts Good Machine learning has proven a powerful tool for accelerating the computational characterization of energy materials. The neural-network approach by Lu et al. allows bypassing time-consuming first principles calculations for the design of catalysts based on high-entropy alloys. This work is an example of a growing number of case studies identifying descriptors of catalytic performance using machine learning instead of physical intuition. Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
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, 2021-03-17)
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 theωB97X-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 ; Density functional theory ; Lithium-ion battery ; Computational chemistry ; Physical chemistry ; Thermodynamics ; Physics ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
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, 2021-05-11)
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) ; Density functional theory ; Chemical physics ; Computational chemistry ; Physical chemistry ; Physics ; Thermodynamics ; Organic chemistry ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Tian Xie | Arthur France‐Lanord | Yanming Wang | Jeffrey Lopez | Michael A. Stolberg | Megan R. Hill | Graham Leverick | Rafael Gómez‐Bombarelli | Jeremiah A. Johnson | Yang Shao‐Horn | Jeffrey C. Grossman
Source: arXiv (Cornell University) (preprint, 2021-01-13)
Field: Materials Science — Materials Chemistry

Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations. Machine learning has been widely adopted to accelerate the screening of materials. Most existing studies implicitly assume that the training data are generated through a deterministic, unbiased process, but this assumption might not hold for the simulation of some complex materials. In this work, we aim to screen amorphous polymer electrolytes which are promising candidates for the next generation lithium-ion battery technology but extremely expensive to simulate due to their structural complexity. We demonstrate that a multi-task graph neural network can learn from a large amount of noisy, biased data and a small number of unbiased data and reduce both random and systematic errors in predicting the transport properties of polymer electrolytes. This observation allows us to achieve accurate predictions on the properties of complex materials by learning to reduce errors in the training data, instead of running repetitive, expensive simulations which is conventionally used to reduce simulation errors. With this approach, we screen a space of 6247 polymer electrolytes, orders of magnitude larger than previous computational studies. We also find a good extrapolation performance to the top polymers from a larger space of 53362 polymers and 31 experimentally-realized polymers. The strategy employed in this work may be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials. Computer science ; Extrapolation ; Amorphous solid ; Polymer electrolytes ; Polymer ; Electrolyte ; Materials science ; Mathematics ; Physics ; Ionic conductivity ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Machine Learning and Algorithms
Authors: Onur Çaylak | Björn Baumeier
Source: ChemRxiv (preprint, 2021-05-24)
Field: Materials Science — Materials Chemistry

Machine Learning of Quasiparticle Energies in Molecules and Clusters We present a ∆-Machine Learning approach for the prediction of GW quasiparticle energies (∆MLQP) and photoelectron spectra of molecules and clusters, using orbital-sensitive graph-based representations in kernel ridge regression based supervised learning. Coulomb matrix, Bag-of-Bonds, and Bonds-Angles-Torsions representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and Kohn–Sham orbital energies, which are both readily available from baseline calculations on the level of density-functional theory (DFT). We first illustrate the effects of different constructions of the orbital-sensitive representations (OSR) on the prediction of frontier orbital energies of 22K molecules of the QM8 dataset, and show that is is possible to predict the full photoelectron spectrum of molecules within the dataset using a single model with a mean-absolute error below 0.1eV. We further demonstrate that the OSR-based ∆MLQP captures the effects of intra- and intermolecular conformations in application to water monomers and dimers. Finally, we show that the approach can be embedded in multiscale simulation workflows, by studying the solvatochromic shifts of quasiparticle and electron-hole excitation energies of solvated acetone in a setup combining Molecular Dynamics, DFT, the GW approximation and the Bethe–Salpeter Equation. Our findings suggest that the ∆MLQP model allows to predict quasiparticle energies and photoelectron spectra of molecules and clusters with GW accuracy at DFT cost. Quasiparticle ; Density functional theory ; Physics ; Molecular orbital ; HOMO/LUMO ; Molecule ; Atomic physics ; Molecular physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Aditya Nandy | Chenru Duan | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2021-11-02)
Field: Materials Science — Materials Chemistry

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure-property relationships from the literature. Models trained on these data sets will improve as they incorporate community feedback. Computer science ; Fidelity ; Property (philosophy) ; Quality (philosophy) ; Machine learning ; Data science ; Artificial intelligence ; Scarcity ; High fidelity ; Data quality ; Data mining ; Metric (unit) ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Chuqiao Shi | Michael C. Cao | Sarah M. Rehn | Sang‐Hoon Bae | Jeehwan Kim | Matthew R. Jones | David A. Muller | Yimo Han
Source: arXiv (Cornell University) (preprint, 2021-11-11)
Field: Materials Science — Materials Chemistry

Uncovering Material Deformations via Machine Learning Combined with\n Four-Dimensional Scanning Transmission Electron Microscopy Understanding lattice deformations is crucial in determining the properties\nof nanomaterials, which can become more prominent in future applications\nranging from energy harvesting to electronic devices. However, it remains\nchallenging to reveal unexpected deformations that crucially affect material\nproperties across a large sample area. Here, we demonstrate a rapid and\nsemi-automated unsupervised machine learning approach to uncover lattice\ndeformations in materials. Our method utilizes divisive hierarchical clustering\nto automatically unveil multi-scale deformations in the entire sample flake\nfrom the diffraction data using four-dimensional scanning transmission electron\nmicroscopy (4D-STEM). Our approach overcomes the current barriers of large 4D\ndata analysis and enables extraction of essential features even without a\npriori knowledge of the sample. Using this purely data-driven analysis, we have\nuncovered different types of material deformations, such as strain, lattice\ndistortion, bending contour, etc., which can significantly impact the band\nstructure and subsequent performance of nanomaterials-based devices. We\nenvision that this data-driven procedure will provide insight into the\nintrinsic structures and accelerate the discovery of novel materials.\n Lattice (music) ; Cluster analysis ; Nanomaterials ; Computer science ; Materials science ; Bending ; A priori and a posteriori ; Sample (material) ; Artificial intelligence ; Nanotechnology ; Biological system ; Physics ; Composite material ; Acoustics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides