jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jenke Scheen | Wilson Wu | Antonia S. J. S. Mey | Paolo Tosco | Mark Mackey | Julien Michel
Source: ChemRxiv (preprint, 2020-05-29)
Field: Materials Science — Materials Chemistry

A Hybrid Alchemical Free Energy and Machine Learning Methodology for the Calculation of Absolute Hydration Free Energies of Small Molecules A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database, and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration, and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations, and to flag molecules which will benefit the most from bespoke forcefield parameterisation efforts. Bespoke ; Computer science ; Range (aeronautics) ; Set (abstract data type) ; Fraction (chemistry) ; Molecule ; Algorithm ; Chemistry ; Materials science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Guido Falk von Rudorff | Stefan Heinen | Marco Bragato | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-05-31)
Field: Materials Science — Materials Chemistry

Thousands of reactants and transition states for competing E2 and S$_\text{N}$2 reactions Reaction barriers are a crucial ingredient for first principles based computational retro-synthesis efforts as well as for comprehensive reactivity assessments throughout chemical compound space. While extensive databases of experimental results exist, modern quantum machine learning applications require atomistic details which can only be obtained from quantum chemistry protocols. For competing E2 and S$_\text{N}$2 reaction channels we report 4'466 transition state and 143'200 reactant complex geometries and energies at respective MP2/6-311G(d) and single point DF-LCCSD/cc-pVTZ level of theory covering the chemical compound space spanned by the substituents NO$_2$, CN, CH$_3$, and NH$_2$ and early halogens (F, Cl, Br) as nucleophiles and leaving groups. Reactants are chosen such that the activation energy of the competing E2 and S$_\text{N}$2 reactions are of comparable magnitude. The correct concerted motion for each of the one-step reactions has been validated for all transition states. We demonstrate how quantum machine learning models can support data set extension, and discuss the distribution of key internal coordinates of the transition states. Transition state ; Halogen ; Nucleophile ; Reactivity (psychology) ; Chemical space ; Space (punctuation) ; Chemistry ; Reaction coordinate ; Quantum chemistry ; Transition state theory ; Quantum ; Computational chemistry ; Statistical physics ; Reaction mechanism ; Chemical physics ; Physics ; Catalysis ; Computer science ; Quantum mechanics ; Reaction rate constant ; Kinetics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; History and advancements in chemistry
Authors: Christopher X. Zhou | William M. Grumbles | Thomas R. Cundari
Source: ChemRxiv (preprint, 2020-07-14)
Field: Materials Science — Materials Chemistry

Using Machine Learning to Predict the pKa of C–H Bonds. Relevance to Catalytic Methane Functionalization Six machine learning models (random forest, neural network, support vector machine, k-nearest neighbors, Bayesian ridge regression, least squares linear regression) were trained on a dataset of 3d transition metal-methyl and -methane complexes to predict pKa(C–H), a property demonstrated to be important in catalytic activity and selectivity. Results illustrate that the machine learning models are quite promising, with RMSE metrics ranging from 4.6 to 8.8 pKa units, despite the relatively modest amount of data available to train on. Importantly, the machine learning models agreed that (a) conjugate base properties were more impactful than those of the corresponding conjugate acid, and (b) the energy of the highest occupied molecular orbital conjugate base was the most significant input feature in the prediction of pKa(C–H). Furthermore, results from additional testing conducted using an external dataset of Sc-methyl complexes demonstrated the robustness of all models, with RMSE metrics ranging from 1.5 to 6.6 pKa units. In all, this research demonstrates the potential of machine learning models in organometallic catalyst development. Machine learning ; Artificial neural network ; Artificial intelligence ; Support vector machine ; Feature selection ; Random forest ; Computer science ; Catalysis ; Regression ; Mean squared error ; Linear regression ; Statistical learning ; Ranging ; Quantitative structure–activity relationship ; Bayesian network ; Conjugate ; Chemistry ; Mathematics ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Howard Yanxon | David Zagaceta | Binh Tang | David S. Matteson | Qiang Zhu
Source: Machine Learning Science and Technology (preprint, 2020-11-10)
Field: Materials Science — Materials Chemistry

PyXtal_FF: a python library for automated force field generation Abstract We present PyXtal_FF—a package based on Python programming language—for developing machine learning potentials (MLPs). The aim of PyXtal_FF is to promote the application of atomistic simulations through providing several choices of atom-centered descriptors and machine learning regressions in one platform. Based on the given choice of descriptors (including the atom-centered symmetry functions, embedded atom density, SO4 bispectrum, and smooth SO3 power spectrum), PyXtal_FF can train MLPs with either generalized linear regression or neural network models, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from ab-initio simulations. The trained MLP model from PyXtal_FF is interfaced with the Atomic Simulation Environment (ASE) package, which allows different types of light-weight simulations such as geometry optimization, molecular dynamics simulation, and physical properties prediction. Finally, we will illustrate the performance of PyXtal_FF by applying it to investigate several material systems, including the bulk SiO 2 , high entropy alloy NbMoTaW, and elemental Pt for general purposes. Full documentation of PyXtal_FF is available at https://pyxtal-ff.readthedocs.io . Python (programming language) ; Bispectrum ; Computer science ; Artificial neural network ; Algorithm ; Artificial intelligence ; Molecular dynamics ; Computational science ; Spectral density ; Machine learning ; Computational chemistry ; Programming language ; Chemistry Machine Learning in Materials Science ; Ion-surface interactions and analysis ; X-ray Diffraction in Crystallography
Authors: Masashi Tsubaki | Teruyasu Mizoguchi
Source: arXiv (Cornell University) (article, 2020-11-16)
Field: Materials Science — Materials Chemistry

On the equivalence of molecular graph convolution and molecular wave function with poor basis set In this study, we demonstrate that the linear combination of atomic orbitals (LCAO), an approximation of quantum physics introduced by Pauling and Lennard-Jones in the 1920s, corresponds to graph convolutional networks (GCNs) for molecules. However, GCNs involve unnecessary nonlinearity and deep architecture. We also verify that molecular GCNs are based on a poor basis function set compared with the standard one used in theoretical calculations or quantum chemical simulations. From these observations, we describe the quantum deep field (QDF), a machine learning (ML) model based on an underlying quantum physics, in particular the density functional theory (DFT). We believe that the QDF model can be easily understood because it can be regarded as a single linear layer GCN. Moreover, it uses two vanilla feedforward neural networks to learn an energy functional and a Hohenberg--Kohn map that have nonlinearities inherent in quantum physics and the DFT. For molecular energy prediction tasks, we demonstrated the viability of an ``extrapolation,'' in which we trained a QDF model with small molecules, tested it with large molecules, and achieved high extrapolation performance. This will lead to reliable and practical applications for discovering effective materials. The implementation is available at this https URL. Extrapolation ; Wave function ; Computer science ; Basis set ; Quantum ; Atomic orbital ; Deep learning ; Statistical physics ; Density functional theory ; Theoretical computer science ; Quantum mechanics ; Algorithm ; Artificial intelligence ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Nikolay Borodinov | Wan‐Yu Tsai | Vladimir V. Korolkov | Nina Balke | Sergei V. Kalinin | Olga S. Ovchinnikova
Source: ChemRxiv (preprint, 2020-01-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Multidomain Processing for Texture-Based Image Segmentation and Analysis Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials properties such as local ordering, molecular orientation and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. Usage of supervised learning and feature extraction allows to retain the consideration of specific problem-dependent features (such as types of periodical structure observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for segmentation of molecular resolved AFM images based on crystal orientation of observed domains, automated selection of boundaries and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique. Segmentation ; Computer science ; Orientation (vector space) ; Artificial intelligence ; Scalability ; Texture (cosmology) ; Image processing ; Atomic force microscopy ; Pattern recognition (psychology) ; Image segmentation ; Feature extraction ; Nanoscopic scale ; Machine learning ; Image (mathematics) ; Materials science ; Nanotechnology ; Mathematics ; Geometry Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Mads Koerstz | Anders S. Christensen | Kurt V. Mikkelsen | Mogens Brøndsted Nielsen | Jan H. Jensen
Source: ChemRxiv (preprint, 2020-07-08)
Field: Materials Science — Materials Chemistry

High Throughput Virtual Screening of 230 Billion Molecular Solar Heat Battery Candidates The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising can- didate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and lifetime of the high energy form (i.e. the free energy barrier to the back reaction) of the canonical parent compound must be increased significantly to be of practical use. We use semiempirical quantum chemical methods, machine learning, and density functional theory to virtually screen over 230 billion substituted DHA molecules to identify promis- ing candidates. We identify a molecule with a predicted energy density of 0.38 kJ/g, which is significantly larger than the 0.14 kJ/g computed for the parent compound. The free energy barrier to the back reaction is 11 kJ/mol higher than the parent compound, which should correspond to a half-life of about 10 days - 4 months. This is considerably longer than the 3-39 hours (depending on solvent) observed for the parent compound and sufficiently long for many practical applications. However, the main conclusion of this study is that there are no molecules among the 230 billion with a storage density approaching 1 kJ/g. Energy density ; Molecule ; Density functional theory ; Solar energy ; Thermal energy ; Energy storage ; Chemistry ; Materials science ; Thermodynamics ; Physics ; Engineering physics ; Computational chemistry ; Organic chemistry ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Gowoon Cheon | Lusann Yang | Kevin McCloskey | Evan J. Reed | Ekin D. Cubuk
Source: arXiv (Cornell University) (preprint, 2020-12-05)
Field: Materials Science — Materials Chemistry

Dataset of Random Relaxations for Crystal Structure Search of Li-Si System Crystal structure search is a long-standing challenge in materials design. We present a dataset of more than 100,000 structural relaxations of potential battery anode materials from randomized structures using density functional theory calculations. We illustrate the usage of the dataset by training graph neural networks to predict structural relaxations from randomly generated structures. Our models directly predict stresses in addition to forces, which allows them to accurately simulate relaxations of both ionic positions and lattice vectors. We show that models trained on the molecular dynamics simulations fail to simulate relaxations from random structures, while training on our data leads to up to two orders of magnitude decrease in error for the same task. Our model is able to find an experimentally verified structure of a stoichiometry held out from training. We find that randomly perturbing atomic positions during training improves both the accuracy and out of domain generalization of the models. Generalization ; Crystal structure ; Lattice (music) ; Computer science ; Molecular dynamics ; Crystal structure prediction ; Statistical physics ; Algorithm ; Artificial neural network ; Crystal (programming language) ; Materials science ; Physics ; Artificial intelligence ; Mathematics ; Chemistry ; Computational chemistry ; Crystallography ; Mathematical analysis Machine Learning in Materials Science
Authors: Manish Shetty | M. Alexander Ardagh | Yutong Pang | Omar Abdelrahman | Paul J. Dauenhauer
Source: ChemRxiv (preprint, 2020-04-17)
Field: Materials Science — Materials Chemistry

Electric-Field Assisted Modulation of Surface Thermochemistry &lt;p&gt;Conventional catalyst design has enhanced reactivity and product selectivity through control of surface thermochemistry by tunable surface composition and the surrounding environment (e.g., pore structure). In this work, the prospect for electric field towards controlling product selectivity and reaction networks on the Pt(111) surface was evaluated with periodic density functional theory (DFT) calculations in concert with machine learning (ML) algorithms. Linear scaling relationships (LSRs) for adsorption energies of surface species in electric field were shown to: (i) be distinct as compared to zero-field LSRs across metals, and (ii) linearly correlate with adsorption energies of H* rather than the binding element. The slope of LSRs linearly correlated with the zero-field dipole moment. A random forest ML regression algorithm predicted field-dependent adsorption energies with a mean absolute error (0.12 eV) comparable to DFT. Overall, this study identifies the path forward for electric field-assisted catalysis, specifically towards catalyst poisoning, product selectivity, and control of reaction pathways.&lt;/p&gt; Thermochemistry ; Electric field ; Selectivity ; Dipole ; Adsorption ; Catalysis ; Field (mathematics) ; Chemistry ; Density functional theory ; Physical chemistry ; Computational chemistry ; Physics ; Quantum mechanics ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Piero Gasparotto | Maria Fischer | Daniele Scopece | Maciej Oskar Liedke | Maik Butterling | A. Wagner | O. Yıldırım | Mathis Trant | Daniele Passerone | Hans J. Hug | Carlo A. Pignedoli
Source: arXiv (Cornell University) (preprint, 2020-09-28)
Field: Materials Science — Materials Chemistry

Mapping the Structure of Oxygen-Doped Wurtzite Aluminum Nitride Coatings From Ab Initio Random Structure Search and Experiments Machine learning is changing how we design and interpret experiments in materials science. In this work, we show how unsupervised learning, combined with ab initio modeling, improves our understanding of structural metastability in multicomponent alloys. We use the example case of Al-O-N alloys where the formation of aluminum vacancies in wurtzite AlN upon the incorporation of substitutional oxygen can be seen as a general mechanism of solids where crystal symmetry is reduced to stabilize defects. The ideal AlN wurtzite crystal structure occupation cannot be matched due to the presence of an aliovalent hetero-element into the structure. The traditional interpretation of the c-lattice shrinkage in sputter-deposited Al-O-N films from X-ray diffraction (XRD) experiments suggests the existence of a solubility limit at 8at.% oxygen content. Here we show that such naive interpretation is misleading. We support XRD data with a machine learning analysis of ab initio simulations and positron annihilation lifetime spectroscopy data, revealing no signs of a possible solubility limit. Instead, the presence of a wide range of non-equilibrium oxygen-rich defective structures emerging at increasing oxygen contents suggests that the formation of grain boundaries is the most plausible mechanism responsible for the lattice shrinkage measured in Al-O-N sputtered films. Wurtzite crystal structure ; Materials science ; Ab initio ; Crystal structure ; Grain boundary ; Crystallography ; Nitride ; Ab initio quantum chemistry methods ; Doping ; Positron annihilation spectroscopy ; Chemical physics ; Nanotechnology ; Microstructure ; Metallurgy ; Chemistry ; Positron ; Zinc ; Positron annihilation ; Physics ; Optoelectronics ; Layer (electronics) Machine Learning in Materials Science ; Semiconductor materials and devices ; Metal and Thin Film Mechanics
Authors: Seyed Mohamad Moosavi | Henglu Xu | Linjiang Chen | Andrew I. Cooper | Berend Smit
Source: ChemRxiv (preprint, 2020-02-07)
Field: Materials Science — Materials Chemistry

Geometric Landscapes for Material Discovery within Energy-Structure-Function Maps Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1,000s-10,000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications. Nanoporous ; Representation (politics) ; Intermolecular force ; Similarity (geometry) ; Function (biology) ; Porosity ; Materials science ; Porous medium ; Computer science ; Biological system ; Molecule ; Nanotechnology ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Chamanthi Denisha Jayaweera | Chathuranga Wickramasinghe | Mahinsasa Narayana
Source: Chemical Engineering Science (article, 2020-11-07)
Field: Materials Science — Materials Chemistry

Modeling seeded suspension polymerization of core-shell polymer particles using computational fluid dynamics Monomer ; Particle (ecology) ; Suspension polymerization ; Polymer ; Polymerization ; Suspension (topology) ; Sensitivity (control systems) ; Phase (matter) ; Materials science ; Chemistry ; Chemical engineering ; Thermodynamics ; Polymer chemistry ; Biological system ; Composite material ; Mathematics ; Physics ; Organic chemistry ; Engineering Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Rheology and Fluid Dynamics Studies
Authors: Ziyan Zhang | Aria Mansouri Tehrani | Anton O. Oliynyk | Blake Day | Jakoah Brgoch
Source: ChemRxiv (preprint, 2020-07-15)
Field: Materials Science — Materials Chemistry

Finding the Next Superhard Material through Ensemble Learning We report an ensemble machine-learning method capable of finding new superhard materials by directly predicting the load-dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load-dependent Vickers hardness data were extracted from the literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving excellent accuracy (R2 = 0.97). This new model was then tested by synthesizing and measuring the load-dependent hardness of several unreported disilicides as well as analyzing the predicted hardness of several classic superhard materials. The trained ensemble method was then employed to screen for superhard materials by examining more than 66,000 compounds in crystal structure databases, which showed that only 68 known materials surpass the superhard threshold. The hardness model was then combined with our data-driven phase diagram generation tool to expand the limited num1 ber of reported compounds. Eleven ternary borocarbide phase spaces were studied, and more than ten thermodynamically favorable compositions with superhard potential were identified, proving this ensemble model’s ability to find previously unknown superhard materials Vickers hardness test ; Boosting (machine learning) ; Materials science ; Ternary operation ; Phase diagram ; Ensemble learning ; Machine learning ; Phase (matter) ; Computer science ; Metallurgy ; Microstructure ; Physics Boron and Carbon Nanomaterials Research ; Semiconductor materials and interfaces ; MXene and MAX Phase Materials