jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Omri Abarbanel | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2021-03-08)
Field: Materials Science — Materials Chemistry

Machine Learning to Accelerate Screening for Marcus Reorganization Energies Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic applications, including solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy, which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use towards screening new compounds with low internal reorganization energies. Our models have an overall root mean square error of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters. Intramolecular force ; Charge (physics) ; Marcus theory ; Work (physics) ; Density functional theory ; Computer science ; Range (aeronautics) ; Transistor ; Materials science ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Voltage ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Conducting polymers and applications
Authors: Marcelo Otero | Silvina N. Sarno | Sofía L. Acebedo | Javier A. Ramírez
Source: Journal of Theoretical Biology (article, 2021-01-25)
Field: Chemistry — Spectroscopy

Tracing molecular properties throughout evolution: A chemoinformatic approach Biology ; Natural selection ; Computational biology ; ENCODE ; Context (archaeology) ; Selection (genetic algorithm) ; Evolutionary biology ; Diversification (marketing strategy) ; Tracing ; Genome ; Molecular evolution ; Gene ; Computer science ; Genetics ; Artificial intelligence Molecular spectroscopy and chirality ; Genetic diversity and population structure
Authors: Joe D. Morrow | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2021-11-22)
Field: Materials Science — Materials Chemistry

Meta-Learning of Interatomic Potential Models for Accelerated Materials Simulations Machine learning (ML) based interatomic potentials are emerging tools for materials simulations but require a trade-off between accuracy and speed. Here we show how one can use one ML potential model to train another: we use an existing, accurate, but more computationally expensive model to generate reference data (locations and labels) for a series of much faster meta-learned potentials. Without the need for quantum-mechanical reference computations at the meta-learning stage, extensive reference datasets can be easily generated, and we find that this improves the quality of fast potentials with less flexible functional forms. We apply the technique to disordered silicon, including a simulation of vitrification and polycrystalline grain formation under pressure with a system size of a million atoms. Our work provides conceptual insight into the machine learning of interatomic potential models, and it suggests a route toward accelerated simulations of condensed-phase systems and nanostructured materials. Interatomic potential ; Computer science ; Computation ; Work (physics) ; Statistical physics ; Quality (philosophy) ; Polycrystalline silicon ; Molecular dynamics ; Computational science ; Artificial intelligence ; Algorithm ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Evan Sheridan | Christopher N. Rhodes | François Jamet | Ivan Rungger | Cédric Weber
Source: Physical review. B./Physical review. B (preprint, 2021-11-17)
Field: Chemistry — Physical and Theoretical Chemistry

Data-driven dynamical mean-field theory: An error-correction approach to solve the quantum many-body problem using machine learning Machine learning opens new avenues for modeling correlated materials. Quantum embedding approaches, such as dynamical mean-field theory (DMFT), provide corrections to first-principles calculations for strongly correlated materials, which are poorly described at lower levels of theory. Such embedding approaches are computationally demanding on classical computing architectures, and hence remain restricted to small systems, which limits the scope of applicability without exceptional computational resources. Here we outline a data-driven machine-learning process for solving the Anderson impurity model (AIM)---the central component of DMFT calculations. The key advance is the use of an ensemble error-correction approach to generate fast and accurate solutions of AIM. An example calculation of the Mott transition using DMFT in the single band Hubbard model is given as an example of the technique, and is validated against the most accurate available method. This approach is called data-driven dynamical mean-field theory (${\mathbf{d}}^{3}\mathbf{MFT}$). Embedding ; Computer science ; Statistical physics ; Field (mathematics) ; Process (computing) ; Dynamical mean field theory ; Hubbard model ; Scope (computer science) ; Quantum Monte Carlo ; Quantum ; Key (lock) ; Mean field theory ; Algorithm ; Physics ; Artificial intelligence ; Theoretical computer science ; Monte Carlo method ; Quantum mechanics ; Mathematics ; Density functional theory Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science ; Chemical and Physical Properties of Materials
Authors: Joe D. Morrow | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2021-11-22)
Field: Materials Science — Materials Chemistry

Indirect Learning of Interatomic Potentials for Accelerated Materials Simulations Machine learning (ML) based interatomic potentials are emerging tools for materials simulations but require a trade-off between accuracy and speed. Here we show how one can use one ML potential model to train another: we use an existing, accurate, but more computationally expensive model to generate reference data (locations and labels) for a series of much faster potentials. Without the need for quantum-mechanical reference computations at the secondary stage, extensive reference datasets can be easily generated, and we find that this improves the quality of fast potentials with less flexible functional forms. We apply the technique to disordered silicon, including a simulation of vitrification and polycrystalline grain formation under pressure with a system size of a million atoms. Our work provides conceptual insight into the machine learning of interatomic potential models, and it suggests a route toward accelerated simulations of condensed-phase systems and nanostructured materials. Interatomic potential ; Computer science ; Computation ; Work (physics) ; Molecular dynamics ; Statistical physics ; Computational science ; Materials science ; Algorithm ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Sara E. Skrabalak
Source: Chemistry of Materials (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Our Most Downloaded Papers Published in 2020 ADVERTISEMENT RETURN TO ISSUEEditorialNEXTOur Most Downloaded Papers Published in 2020Sara E. Skrabalak*Sara E. SkrabalakDepartment of Chemistry, Indiana University−Bloomington, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States*Email: [email protected]More by Sara E. Skrabalakhttp://orcid.org/0000-0002-1873-100XCite this: Chem. Mater. 2021, 33, 1, 1–3Publication Date (Web):January 12, 2021Publication History Published online12 January 2021Published inissue 12 January 2021https://pubs.acs.org/doi/10.1021/acs.chemmater.0c04607https://doi.org/10.1021/acs.chemmater.0c04607editorialACS PublicationsCopyright © 2021 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views3632Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (4 MB) Get e-AlertscloseSUBJECTS:Batteries,Crystallization,Energy,Machine learning,Materials Get e-Alerts Computer science ; World Wide Web ; Library science Covalent Organic Framework Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Glenn Palmer | Siqi Du | Alexander Politowicz | Joshua Paul Emory | Xiyu Yang | Anupraas Gautam | G S Gupta | Zhelong Li | Ryan Jacobs | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2021-05-27)
Field: Materials Science — Materials Chemistry

Calibrated bootstrap for uncertainty quantification in regression models Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted. A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models, which are often generated by the generally applicable bootstrap method. In this work, we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty and propose a calibration method to dramatically improve its accuracy. We demonstrate the effectiveness of this calibration method for both synthetic data and physical datasets from the field of Materials Science and Engineering. The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models. Calibration ; Variance (accounting) ; Computer science ; Uncertainty quantification ; Range (aeronautics) ; Regression ; Machine learning ; Ensemble learning ; Field (mathematics) ; Data mining ; Sensitivity analysis ; Artificial intelligence ; Uncertainty analysis ; Statistics ; Mathematics ; Engineering ; Simulation Machine Learning in Materials Science ; Fault Detection and Control Systems ; Machine Learning and Data Classification
Authors: Ömer Tayfuroğlu | Abdulkadır Kocak | Yunus Zorlu
Source: ChemRxiv (preprint, 2021-12-07)
Field: Materials Science — Materials Chemistry

Development and Application of a Single Neural Network Potential for IRMOF-n (n=1,4,6,7,10) Metal‑organic frameworks (MOFs) with their exceptional porous and organized structures have been subject of numerous applications. Predicting macroscopic properties from atomistic simulations require the most accurate force fields, which is still a major problem due to MOFs’ hybrid structures governed by covalent, ionic and dispersion forces. Application of ab‑initio molecular dynamics to such large periodic systems are thus beyond the current computational power. Therefore, alternative strategies must be developed to reduce computational cost without losing reliability. In this work, we describe the construction of a neural network potential (NNP) for IRMOF‑n series (n=1,4,7,10) trained by PBE-D4/def2-TZVP reference data of MOF fragments. We validated the resulting NNP on both fragments and bulk MOF structures by prediction of properties such as equilibrium lattice constants, phonon density of states and linker orientation. The energy and force RMSE values for the fragments are only 0.0017 eV/atom and 0.15 eV/Å, respectively. The NNP predicted equilibrium lattice constants of bulk structures, which are not included in training, are off by only 0.2-2.4% from experimental results. Moreover, our fragment trained NNP greatly predicts phenylene ring torsional energy barrier, equilibrium bond distances and vibrational density of states of bulk MOFs. Furthermore, NNP allows us to investigate unusual behaviors of selected MOFs such as the thermal expansion properties and the effect of mechanical strain on the adsorption of hydrogen and methane molecules. The NNP based molecular dynamics (MD) simulations suggest the IRMOF‑4 and IRMOF‑7 to have positive‑to‑negative thermal expansion coefficients while the rest to have only negative thermal expansion under the studied temperatures of 200 K to 400 K. The deformation of bulk structure by reduction of unit cell volume has shown to increase volumetric methane uptake in IRMOF‑1 but decrease in IRMOF‑7 due to the steric hindrance. Materials science ; Force field (fiction) ; Chemical physics ; Molecular dynamics ; Density functional theory ; Ionic bonding ; Phonon ; Statistical physics ; Computational chemistry ; Chemistry ; Condensed matter physics ; Physics Thermal Expansion and Ionic Conductivity ; Thermal and Kinetic Analysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Eric J. Dufek | Tanvir R. Tanim | M. Ross Kunz | Sang‐Wook Kim | Zonggen Yi | Kevin L. Gering | Bor‐Rong Chen | Paul Gasper | Kandler Smith | Matthew Shirk
Source: ECS Meeting Abstracts (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Physics-Based Machine Learning: Data Needs and Practices for Failure Mode Classification and Life Prediction Machine learning and other advanced analysis approaches hold promise to reduce the time and effort needed to transition a new battery technology from innovation to deployment. Prediction of life and performance from sparse data is a key part of the technology revolution which will serve as a cornerstone of a reduced carbon energy future. Complimenting the prediction of life is the classification and prediction of specific failure modes and mechanisms to provide sufficient feed back to the R&D process so that key technology gaps can be addressed earlier and to ensure appropriate measures are taken to reduce long-term development costs. Cornerstone ; Software deployment ; Key (lock) ; Process (computing) ; Computer science ; Term (time) ; Machine learning ; Failure mode and effects analysis ; Artificial intelligence ; Mode (computer interface) ; Data science ; Risk analysis (engineering) ; Reliability engineering ; Engineering ; Software engineering ; Computer security ; Business Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fault Detection and Control Systems
Authors: Yoshitaka Umeno | Atsushi Kubo | Chutian WANG
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Atomistic model analysis of buckling-induced property change in carbon nanotubes Carbon nanotubes (CNTs) are one of the most promising nanostructured materials for novel nano-devices utilizing its peculiar mechanical properties and functional aspects. In particular, CNTs can accommodate reversible buckling deformation under pressure, which exhibits large deformation at a critical stress or strain leading to abrupt change in the electronic properties. In this study, we carried out molecular dynamics simulations to investigate the effect of the Stone-Wales (SW) defect on the buckling behavior. Sharp peaks in the stress-strain curves corresponding to buckling become smooth and gradual change with the presence of the SW defect because it incorporates local strain. Critical stress and strain as functions of CNT radius also become less peaky due to the defect, suggesting practicality of using defects for realizing reliable nano-devices. Application of an artificial neural network model for the prediction of density of states was also attempted, revealing the need for further development of the model for better accuracy. Buckling ; Materials science ; Carbon nanotube ; Deformation (meteorology) ; RADIUS ; Stress (linguistics) ; Composite material ; Strain (injury) ; Molecular dynamics ; Nano- ; Nanotechnology ; Carbon fibers ; Structural engineering ; Computer science ; Computational chemistry ; Composite number ; Chemistry ; Engineering Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications ; Diamond and Carbon-based Materials Research
Authors: Joe Z. Wu | Solmaz Azimi | Sheenam Khuttan | Nanjie Deng | Emilio Gallicchio
Source: Journal of Chemical Theory and Computation (preprint, 2021-05-13)
Field: Materials Science — Materials Chemistry

Alchemical Transfer Approach to Absolute Binding Free Energy Estimation The alchemical transfer method (ATM) for the calculation of standard binding free energies of noncovalent molecular complexes is presented. The method is based on a coordinate displacement perturbation of the ligand between the receptor binding site and the explicit solvent bulk and a thermodynamic cycle connected by a symmetric intermediate in which the ligand interacts with the receptor and solvent environments with equal strength. While the approach is alchemical, the implementation of the ATM is as straightforward as that for physical pathway methods of binding. The method is applicable, in principle, with any force field, as it does not require splitting the alchemical transformations into electrostatic and nonelectrostatic steps, and it does not require soft-core pair potentials. We have implemented the ATM as a freely available and open-source plugin of the OpenMM molecular dynamics library. The method and its implementation are validated on the SAMPL6 SAMPLing host-guest benchmark set. The work paves the way to streamlined alchemical relative and absolute binding free energy implementations on many molecular simulation packages and with arbitrary energy functions including polarizable, quantum-mechanical, and artificial neural network potentials. Free energy perturbation ; Molecular dynamics ; Thermodynamic integration ; Binding energy ; Umbrella sampling ; Electrostatics ; Computer science ; Chemistry ; Physics ; Statistical physics ; Chemical physics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Ryo Tamura | Momo Matsuda | Jianbo Lin | Yasunori Futamura | Tetsuya Sakurai | Tsuyoshi Miyazaki
Source: arXiv (Cornell University) (preprint, 2021-07-29)
Field: Materials Science — Materials Chemistry

Unsupervised learning-based structural analysis: Search for a characteristic low-dimensional space by local structures in atomistic simulations Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of datapoints for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system. Molecular dynamics ; Component (thermodynamics) ; Atom (system on chip) ; Binary number ; Statistical physics ; Space (punctuation) ; Computer science ; Locality ; Local structure ; Silicon ; Materials science ; Chemical physics ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Advanced Electron Microscopy Techniques and Applications
Authors: Jiahui Chen | Weihua Geng | Guo‐Wei Wei
Source: Chinese Journal of Chemical Physics (preprint, 2021-12-01)
Field: Materials Science — Materials Chemistry

MLIMC: Machine learning-based implicit-solvent Monte Carlo Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction. Monte Carlo method ; Solvation ; Computer science ; Solvent models ; Implicit solvation ; Molecular dynamics ; Solvent ; Water model ; Algorithm ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Alexander V. Shapeev | Dmitry Bocharov | Alexei Kuzmin
Source: Computational Materials Science (preprint, 2021-11-22)
Field: Materials Science — Surfaces, Coatings and Films

Validation of moment tensor potentials for fcc and bcc metals using EXAFS spectra Pseudopotential ; Extended X-ray absorption fine structure ; Spectral line ; Valence (chemistry) ; Density functional theory ; Tensor (intrinsic definition) ; Valence electron ; Electron ; Materials science ; Chemistry ; Atomic physics ; Physics ; Absorption spectroscopy ; Computational chemistry ; Optics ; Quantum mechanics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography