jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lagnajit Pattanaik | John Ingraham | Colin A. Grambow | William H. Green
Source: ChemRxiv (preprint, 2020-05-15)
Field: Materials Science — Materials Chemistry

Genereting Transition States of Isomerization Reactions with Deep Learning Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions. Isomerization ; Transition state ; Stochastic matrix ; Artificial neural network ; Computer science ; Workflow ; Scope (computer science) ; Quantum ; Graph ; Product (mathematics) ; Algorithm ; Chemistry ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Machine learning ; Catalysis ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Protein Structure and Dynamics
Authors: Mingyuan Xu | Tong Zhu | John ZH Zhang
Source: ChemRxiv (preprint, 2020-11-16)
Field: Materials Science — Materials Chemistry

Ab Initio Molecular Dynamics Simulation of Zinc metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network &lt;p&gt;Artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. In this work, we developed ab initio based neural network potential (NN/MM-RESP-MBG) to perform molecular dynamics study for metalloproteins. The interaction energy, atomic forces, and atomic charges of metal binding group in NN/MM-RESP-MBG are described by a neural network potential trained with energies and forces generated from density functional calculations. Here, we used our recently proposed E-SOI-HDNN model to achieve the automatic construction of reference dataset of metalloproteins and the active learning of neural network potential functions. The predicted energies and atomic forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we can perform long time AIMD simulations and structure refinement MD simulation for metalloproteins. In 1 ns AIMD simulation of four common coordination mode of zinc-containing metalloproteins, the statistical average structure is in good agreement with statistic value of PDB Bank database. The neural network approach used in this study can be applied to construct potentials to metalloproteinase catalysis, ligand binding and other important biochemical processes and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other biomacromolecule system. &lt;/p&gt; Artificial neural network ; Ab initio ; Molecular dynamics ; Chemistry ; Metalloprotein ; Biological system ; Computational chemistry ; Chemical physics ; Computer science ; Metal ; Machine learning Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Fuel Cells and Related Materials
Authors: Rudolf Jason Kwaria | Rudolf Jason
Source: Institutional Repositories DataBase (IRDB) (article, 2020-09-01)
Field: Materials Science — Surfaces, Coatings and Films

Prediction of Material Properties of Model Organic Biomaterials by Machine Learning In this study, I discuss the possibility of application of a machine learning algorithm which is called artificial neural network (ANN) algorithm to predict properties of self-assembled monolayers (SAMs) (i.e. water contact angle and fibrinogen adsorption) as well as deriving a quantitative guide to design SAMs exhibiting specific material properties. By using data of WCA and fibrinogen adsorption of SAMs gathered from various sources to train the ANN algorithm, I was able to predict WCA and fibrinogen adsorption accurately. Moreover, the quantitative guide derived using ANN algorithm revealed importance of each structural parameter for WCA and fibrinogen adsorption which provides essential information for material design. The quantitative guide agrees with the well-known empirical guide, confirming the validity of this result. Furthermore, I demonstrated the predictive capability of ANN by predicting properties of several SAMs not existed in dataset used to train the ANN algorithm. Artificial neural network ; Adsorption ; Experimental data ; Material properties ; Monolayer ; Empirical modelling Polymer Surface Interaction Studies ; Advanced Physical and Chemical Molecular Interactions ; Hydrogels: synthesis, properties, applications
Authors: Dauren Zhexebay | S.A. Khokhlov | Y.T. Kozhalgulov
Source: Recent Contributions to Physics (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Prediction of parameters and classification of molecular outflows using convolutional neural networks Machine learning is gaining popularity in modern astrophysics for its incredibly powerful ability to make predictions or make assumptions over large amounts of data. We describe the application of machine learning to regression of molecular outflow parameters (mass, momentum, kinetic energy, and dynamic time) and classification of bipolar outflow using convolutional neural networks. The size of our training sample is ~ 125 sources of molecular outflow for classification, that is, 80% of the total amount of data, where 67 sources are bipolar outflow and ~ 75 sources of bipolar outflow for regression. The results show that the use of CNN improves the image classification accuracy up to 97%. The regression model predicts molecular outflow parameters with an average absolute percentage error of 37.7% for the training data and with an average absolute error of 88.0 (mass), 1237.7 (momentum), 193.3 (kinetic energy), and 3.0 (dynamic time) for test data. The machine learning algorithm reduces data processing time for predictions and classification, and this methodology has a broad prospect for future studies of astrophysics problems. Outflow ; Convolutional neural network ; Artificial intelligence ; Computer science ; Kinetic energy ; Artificial neural network ; Regression ; Machine learning ; Momentum (technical analysis) ; Pattern recognition (psychology) ; Physics ; Mathematics ; Statistics Spectroscopy and Laser Applications ; Scientific Measurement and Uncertainty Evaluation ; Atmospheric Ozone and Climate
Authors: Richard L. Apodaca
Source: Front Matter (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

A Workbench for Machine Learning in Chemistry Machine learning has made major inroads into molecular property prediction over the last few years. But with the deluge of techniques and tools comes an increased need to recast what works well into a form usable by non-experts. DeepChem is a toolchain designed to do just that. This article presents a short, simple code example demonstrating how to use DeepChem to train and use a deep convolutional network to predict aqueous solubility. Toolchain ; Workbench ; USable ; Property (philosophy) ; Simple (philosophy) ; Code (set theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Richard L. Apodaca
Source: Front Matter (preprint, 2020-10-26)
Field: Chemistry — Physical and Theoretical Chemistry

A Workbench for Machine Learning in Chemistry Machine learning has made major inroads into molecular property prediction over the last few years. But with the deluge of techniques and tools comes an increased need to recast what works well into a form usable by non-experts. DeepChem is a toolchain designed to do just that. This article presents a short, simple code example demonstrating how to use DeepChem to train and use a deep convolutional network to predict aqueous solubility. Workbench ; Computer science ; Chemistry ; Artificial intelligence Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Tawfik, Shreif Abdulkader | Isayev, Olexander | Spencer, Michelle | Winkler, David
Source: La Trobe University (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Predicting Thermal Properties of Crystals Using Machine Learning Calculating vibrational properties of crystals using quantum mechanical (QM) methods is a challenging problem in computational material science. This problem is solved using complementary machine learning methods that rapidly and reliably recapitulate entropy, specific heat, effective polycrystalline dielectric function, and a non-vibrational property (band gap) for materials calculated by accurate but lengthy QM methods. The materials are described mathematically using property-labeled materials fragment descriptors. The machine learning models predict the QM properties with root mean square errors of 0.31 meV per atom per K for entropy, 0.18 meV per atom per K for specific heat, 4.41 for the trace of the dielectric tensor, and 0.5 eV for band gap. These models are sufficiently accurate to allow rapid screening of large numbers of crystal structures to accelerate material discovery. Dielectric ; Thermal ; Atom (system on chip) ; Crystal (programming language) ; TRACE (psycholinguistics) ; Property (philosophy) ; Crystallite Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Thermal Expansion and Ionic Conductivity
Authors: O.A. Egorova | Roohollah Hafizi | David C. Woods | Graeme M. Day
Source: ChemRxiv (preprint, 2020-06-02)
Field: Materials Science — Materials Chemistry

Multi-fidelity Statistical Machine Learning for Molecular Crystal Structure Prediction The prediction of crystal structures from first principles requires highly accurate energies for large numbers of putative crystal structures. The accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or more structures can be present in the low energy region of interest, so that the associated computational costs are prohibitive. Here, we apply statistical machine learning to predict expensive hybrid functional DFT (PBE0) calculations using a multi-fidelity approach to re-evalute the energies of crystal structures predicted with an inexpensive force field. The method uses an autoregressive Gaussian process, making use of less expensive GGA DFT (PBE) calculations to bridge the gap between the force field and PBE0 energies. The method is benchmarked on the crystal structure landscapes of three small, hydrogen bonding organic molecules and shown to produce accurate predictions of energies and crystal structure ranking using small numbers of the most expensive calculations; the PBE0 energies can be predicted with errors of less than 1 kJ/mol with between 4.2-6.8% of the cost of the full calculations. As the model that we have developed is probabilistic, we discuss how the uncertainties in predicted energies impact on assessment of the energetic ranking of crystal structures. Crystal structure prediction ; Density functional theory ; Crystal (programming language) ; Computer science ; Statistical physics ; Algorithm ; Crystal structure ; Fidelity ; Probabilistic logic ; Molecule ; Machine learning ; Physics ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Gregory H. Teichert | Anirudh Raju Natarajan | Anton Van der Ven | Krishna Garikipati
Source: arXiv (Cornell University) (preprint, 2020-01-30)
Field: Materials Science — Materials Chemistry

Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys The free energy plays a fundamental role in descriptions of many systems in continuum physics. Notably, in multiphysics applications, it encodes thermodynamic coupling between different fields. It thereby gives rise to driving forces on the dynamics of interaction between the constituent phenomena. In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains can render the free energy to be reasonably high-dimensional. In proposing the free energy as a paradigm for scale bridging, we have previously exploited neural networks for their representation of such high-dimensional functions. Specifically, we have developed an integrable deep neural network (IDNN) that can be trained to free energy derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover a free energy density function. The motivation comes from the statistical mechanics formalism, in which certain free energy derivatives are accessible for control of the system, rather than the free energy itself. Our current work combines the IDNN with an active learning workflow to improve sampling of the free energy derivative data in a high-dimensional input space. Treated as input-output maps, machine learning accommodates role reversals between independent and dependent quantities as the mathematical descriptions change with scale bridging. As a prototypical system we focus on Ni-Al. Phase field simulations using the resulting IDNN representation for the free energy density of Ni-Al demonstrate that the appropriate physics of the material have been learned. To the best of our knowledge, this represents the most complete treatment of scale bridging, using the free energy for a practical materials system, that starts with electronic structure calculations and proceeds through statistical mechanics to continuum physics. Bridging (networking) ; Statistical mechanics ; Integrable system ; Artificial neural network ; Multiphysics ; Statistical physics ; Computer science ; Thermodynamic integration ; Artificial intelligence ; Energy (signal processing) ; Physics ; Theoretical computer science ; Quantum mechanics ; Finite element method Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: R.I. Zubatyuk | Justin S. Smith | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev
Source: ChemRxiv (preprint, 2020-07-28)
Field: Materials Science — Materials Chemistry

Teaching a Neural Network to Attach and Detach Electrons from Molecules &lt;p&gt;&lt;/p&gt;&lt;p&gt;Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry. In particular, interatomic potentials derived with Machine Learning algorithms such as Deep Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. The applicability domain of DNN potentials is usually limited by the type of training data. As such, transferable models are aimed to be extensible in the description of chemical and conformational diversity of organic molecules. However, most DNN potentials, such as the AIMNet model we proposed previously, were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we extend machine learning framework toward open-shell anions and cations. We introduce AIMNet-NSE (Neural Spin Equilibration) architecture, which being properly trained, could predict atomic and molecular properties for an arbitrary combination of molecular charge and spin multiplicity. This model explores a new dimension of transferability by adding the charge-spin space. The AIMNet-NSE model is capable of reproducing reference QM energies for cations, neutrals, and anions with errors of about 2-3 kcal/mol, compared to the reference QM simulations. The spin-charges have errors ~0.01 electrons for small organic molecules containing nine chemical elements {H, C, N, O, F, Si, P, S and Cl}. &lt;a&gt;The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions with a speed up to 10&lt;sup&gt;4&lt;/sup&gt; molecules per second on a single modern GPU.&lt;/a&gt; We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.&lt;/p&gt;&lt;p&gt;&lt;/p&gt; Artificial neural network ; Electron ; Ion ; Ionization ; Molecule ; Chemical space ; Physics ; Transferability ; Molecular dynamics ; Charge (physics) ; Computer science ; Chemistry ; Atomic physics ; Chemical physics ; Artificial intelligence ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Chao Liu | Yifei Wang
Source: Academic Journal of Materials & Chemistry (article, 2020-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning of C-H Activation Reaction of Indoles Aiming at developing the most suitable method based on machine learning to predict the results of C-H activation reaction of indoles (CARI), we constructed the database by using ortho-substitution and meta-substitution to collect 2000 different CARI. DFT method is employed to calculate energy barrier and selectivity of all reactions.7 algorithms are involved -Random forest (RF), Kernel Ridge Regression (KRR), K Nearest Neighbor (KNN), Support Vector Regression (SVR), Neural Network (NN), Gaussian Process (GP) and LASSO Regression (LASSO)-to make predictions. Meanwhile, the results of the reactions give us the guidance to predict the energy barrier and selectivity of new reactions for experimentalists. According to the data, Random forest is the most suitable way to make predictions. Superior to random forest, KNN and NN also demonstrates good performance. Random forest ; Support vector machine ; Artificial neural network ; Lasso (programming language) ; Computer science ; Regression ; Artificial intelligence ; Machine learning ; Gaussian process ; Kernel (algebra) ; Gaussian ; Chemistry ; Mathematics ; Computational chemistry ; Statistics Machine Learning in Materials Science
Authors: Gregory H. Teichert | Anirudh Raju Natarajan | Anton Van der Ven | Krishna Garikipati
Source:  (article, 2020-01-30)
Field: Materials Science — Materials Chemistry

Active learning workflows and integrable deep neural networks for representing the free energy functions of alloy. The free energy plays a fundamental role in descriptions of many systems in continuum physics. Notably, in multiphysics applications, it encodes thermodynamic coupling between different fields, such as mechanics and chemistry. It thereby gives rise to driving forces on the dynamics of interaction between the constituent phenomena. In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains can render the free energy to be reasonably high-dimensional. In proposing free energy functions as a paradigm for scale bridging, we have previously exploited neural networks for their representation of such high-dimensional functions. Specifically, we have developed an integrable deep neural network (IDNN) that can be trained to free energy derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover a free energy function. The motivation comes from the statistical mechanics formalism, in which certain free energy derivatives are accessible for control of the system, rather than the free energy itself in its entirety. Our current work combines the IDNN with an active learning workflow to improve sampling of the free energy derivative data in a high-dimensional input space. Treated as input-output maps, machine learning representations accommodate role reversals between independent and dependent quantities as the mathematical descriptions change across scale boundaries. As a prototypical material system we focus on Ni-Al. Phase field simulations using the resulting IDNN representation for the free energy of Ni-Al demonstrates that the appropriate physics of the material have been learned. Statistical mechanics ; Artificial neural network ; Integrable system ; Computer science ; Thermodynamic integration ; Bridging (networking) ; Multiphysics ; Statistical physics ; Workflow ; Representation (politics) ; Energy (signal processing) ; Artificial intelligence ; Theoretical computer science ; Physics ; Quantum mechanics ; Finite element method Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Mingyuan Xu | Tong Zhu | John ZH Zhang
Source: ChemRxiv (preprint, 2020-11-16)
Field: Materials Science — Materials Chemistry

Ab Initio Molecular Dynamics Simulation of Zinc metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network Artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. In this work, we developed ab initio based neural network potential (NN/MM-RESP-MBG) to perform molecular dynamics study for metalloproteins. The interaction energy, atomic forces, and atomic charges of metal binding group in NN/MM-RESP-MBG are described by a neural network potential trained with energies and forces generated from density functional calculations. Here, we used our recently proposed E-SOI-HDNN model to achieve the automatic construction of reference dataset of metalloproteins and the active learning of neural network potential functions. The predicted energies and atomic forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we can perform long time AIMD simulations and structure refinement MD simulation for metalloproteins. In 1 ns AIMD simulation of four common coordination mode of zinc-containing metalloproteins, the statistical average structure is in good agreement with statistic value of PDB Bank database. The neural network approach used in this study can be applied to construct potentials to metalloproteinase catalysis, ligand binding and other important biochemical processes and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other biomacromolecule system. Artificial neural network ; Molecular dynamics ; Ab initio ; Chemistry ; Metalloprotein ; Biological system ; Computational chemistry ; Chemical physics ; Computer science ; Physics ; Metal ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics