jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fabio Le Piane | Matteo Baldoni | Francesco Mercuri
Source: arXiv (Cornell University) (preprint, 2020-07-29)
Field: Materials Science — Materials Chemistry

Predicting the properties of molecular materials: multiscale simulation workflows meet machine learning Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological relevance, the properties of molecular materials are related to the link between molecular structure and phenomena occurring across a wide set of spatial scales, from the nanoscale to the macroscale. Here, we describe an approach for predicting the properties of molecular aggregates based on multiscale simulations and machine learning. Workflow ; Computer science ; Relevance (law) ; Set (abstract data type) ; Molecular dynamics ; Machine learning ; Artificial intelligence ; Chemistry ; Database ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: B. Miller
Source: Refubium (Universitätsbibliothek der Freien Universität Berlin) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

SE(3) Equivariant Neural Networks for Regression on Molecular Properties e3nn is an artificial neural network which operates on atomic coordinates and achieves equivariance to the special euclidean group in three dimensions by using spherical harmonics as features. The main experiment is to benchmark the model against a standard chemical data set called QM9, on which e3nn achieves state of the art performance on three of twelve regression targets. Along with empirical results, this thesis presents theoretical argumentation for why e3nn outperforms its closest relatives, SchNet and Cormorant, on some regression targets. Significant background regarding machine learning, quantum chemistry, and the special euclidean group is also presented. Regression ; Benchmark (surveying) ; Equivariant map ; Artificial neural network ; Artificial intelligence ; Computer science ; Statistics ; Machine learning ; Mathematics ; Geography ; Cartography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Mingjian Wen | Ellad B. Tadmor
Source: npj Computational Materials (article, 2020-08-14)
Field: Materials Science — Materials Chemistry

Uncertainty quantification in molecular simulations with dropout neural network potentials Abstract Machine learning interatomic potentials (IPs) can provide accuracy close to that of first-principles methods, such as density functional theory (DFT), at a fraction of the computational cost. This greatly extends the scope of accurate molecular simulations, providing opportunities for quantitative design of materials and devices on scales hitherto unreachable by DFT methods. However, machine learning IPs have a basic limitation in that they lack a physical model for the phenomena being predicted and therefore have unknown accuracy when extrapolating outside their training set. In this paper, we propose a class of Dropout Uncertainty Neural Network (DUNN) potentials that provide rigorous uncertainty estimates that can be understood from both Bayesian and frequentist statistics perspectives. As an example, we develop a DUNN potential for carbon and show how it can be used to predict uncertainty for static and dynamical properties, including stress and phonon dispersion in graphene. We demonstrate two approaches to propagate uncertainty in the potential energy and atomic forces to predicted properties. In addition, we show that DUNN uncertainty estimates can be used to detect configurations outside the training set, and in some cases, can serve as a predictor for the accuracy of a calculation. Computer science ; Uncertainty quantification ; Frequentist inference ; Artificial neural network ; Dropout (neural networks) ; Set (abstract data type) ; Statistical physics ; Bayesian probability ; Artificial intelligence ; Machine learning ; Bayesian inference ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Model Reduction and Neural Networks
Authors: M. F. Gafarov | К.П. Павлова | E.A. Gafarova
Source: 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) (article, 2020-10-06)
Field: Materials Science — Materials Chemistry

Development of a Material Selection Model for Rolls Rolling Mills Using Neural Networks The mathematical model of optimization of selection materials for rolls rolling mills based on neural network modeling was developed. The analysis of neural network models to determine the most appropriate architecture and other parameters for solving the problem was performed. Specialized software for designing neural network models based on the Keras deep learning library had been created. A neural network with a multilayer perceptron (MLP) structure was developed, the adequacy of the resulting neural network was checked. The resulting neural network is able to predict the amount of roll wear from the specified parameters. A special algorithm for the selection of the roll material which under the specified conditions would have acceptable wear at minimal cost of repair rolls was developed. Artificial neural network ; Selection (genetic algorithm) ; Computer science ; Artificial intelligence ; Multilayer perceptron ; Material selection ; Machine learning ; Engineering ; Materials science Metal Alloys Wear and Properties ; Industrial Engineering and Technologies ; Diverse Industrial Engineering Technologies
Authors: Zhongyu Wan | Quan de Wang | Jinhu Liang
Source:  (preprint, 2020-06-05)
Field: Chemistry — Organic Chemistry

Accurate prediction of standard enthalpy of formation based on semiempirical quantum chemistry methods with artificial neural network and molecular descriptors This work investigates possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods for the prediction of standard enthalpy of formation (Δ_f H^o) through the use of artificial neural network (ANN) with molecular descriptors. A total number of 142 organic compounds with enough structural diversity has been considered in the training set. Standard enthalpy of formation for the selected compounds at the semiempirical PM3 and PM6 quantum chemistry methods is collected from literature, and is calculated by using semiempirical PM7 method in this work. The multiple stepwise regression is first employed to screen effective molecular descriptors, which are highly correlated with the error terms of the standard enthalpy of formation compared with experimental values. The obtained 7 effective molecular descriptors are then used as input set to establish three 7-11-1 neural network-based correction models to improve the accuracy of SQC methods. By using the developed correction models, the mean absolute errors (MAE) for Δ_f H^oof PM3, PM6, and PM7 methods are reduced from 22.36, 18.60, 17.27to 9.86, 9.83, 8.95, respectively in kJ/mol. Meanwhile, the results of the test set show that the neural network does not have the problem of over-fitting. Detailed analysis of the 7 effective molecular descriptors indicates that the major source to the correction models is from the electron withdrawing effect. The developed ANN models for the three selected SQC methods provide an efficient method for the quick and accurate prediction of thermodynamic properties. Artificial neural network ; Enthalpy ; Standard enthalpy of formation ; Chemistry ; Molecular descriptor ; Standard enthalpy change of formation ; Quantitative structure–activity relationship ; Thermodynamics ; Biological system ; Artificial intelligence ; Computer science ; Physical chemistry ; Physics ; Stereochemistry Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Linh Le Pham Van | Quang Tran | Tien Lam Pham | Long Tran Quoc
Source:  (article, 2020-11-12)
Field: Materials Science — Materials Chemistry

Node-aware convolution in Graph Neural Networks for Predicting molecular properties Molecular property prediction is a challenging task which aims to solve various issues of science namely drug discovery, materials discovery. It focuses on understanding the structure-property relationship between atoms in a molecule. Previous approaches have to face difficulties dealing with the various structure of the molecule as well as heavy computational time. Our model, in particular, utilizes the idea of message passing neural network and Schnet on the molecular graph with enhancement by adding the Node-aware Convolution and Edge Update layer in order to acquire the local information of the graph and to propagate interaction between atoms. Through experiments, our model has been shown the outperformance with previous deep learning methods in predicting quantum mechanical, calculated molecular properties in the QM9 dataset and magnetic interaction of two atoms in molecules approaches. Computer science ; Node (physics) ; Graph ; Convolution (computer science) ; Theoretical computer science ; Artificial neural network ; Molecular graph ; Property (philosophy) ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Amanda Parker | George Opletal | Amanda S. Barnard
Source: ChemRxiv (preprint, 2020-05-21)
Field: Materials Science — Materials Chemistry

Classification of Platinum Nanoparticle Catalysts using Machine Learning Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximise the performance on a given application. This can be inconsistent with experimental observations that measure the collective properties of entire samples of structures that contain distributions or mixture of structures, even when synthesized and processed with care. Metallic nanoparticle catalysts are an important example. In this study we have used a multi-stage machine learning workflow to identify the correct structure/property relationships of Pt nanoparticles relevant to oxygen reduction (ORR), hydrogen oxidation (HOR) and hydrogen evolution (HER) reactions. By including classification prior to regression we identified two distinct classes of nanoparticles, and subsequently generate the class-specific models based on experimentally relevant criteria that are consistent with observations. These multi-structure/multi-property relationships, predicting properties averaged over a large sample of structures, provide a more accessible way to transfer data-driven predictions into the lab. Workflow ; Nanoparticle ; Property (philosophy) ; Computer science ; Artificial intelligence ; Catalysis ; Transfer of learning ; Machine learning ; Materials science ; Nanotechnology ; Chemistry ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Derek P. Metcalf | Andy Jiang | Steven A. Spronk | Daniel L. Cheney | C. David Sherrill
Source: ChemRxiv (preprint, 2020-09-21)
Field: Materials Science — Materials Chemistry

Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom-typing or relatively expensive quantum mechanical methods to generate a density to be partitioned. Most machine learning efforts in this domain ignore total molecular charges, relying on overfitting and arbitrary rescaling in order to match the total system charge. Here we introduce the electron-passing neural network (EPNN), a fast, accurate neural network atomic charge partitioning model that conserves total molecular charge by construction. EPNNs predict atomic charges very similar to those obtained by partitioning quantum mechanical densities, but at such a small fraction of the cost that they can be easily computed for large biomolecules. Charges from this method may be used directly for molecular mechanics, as features for cheminformatics, or as input to any neural network potential. Artificial neural network ; Charge (physics) ; Atomic charge ; Computer science ; Overfitting ; Quantum ; Molecular dynamics ; Statistical physics ; Heuristic ; Electron ; Charge density ; Physics ; Algorithm ; Quantum mechanics ; Artificial intelligence ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Krupal P. Jethava | Jonathan Fine | Yingqi Chen | Ahad Hossain | Gaurav Chopra
Source: ChemRxiv (preprint, 2020-04-13)
Field: Materials Science — Materials Chemistry

Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines Towards Fast Multicomponent Reactions Predicting the outcome of chemical reactions using machine learning models has emerged as a promising research area in chemical science. However, the use of such models to prospectively test new reactions by interpreting chemical reactivity is limited. We have developed a new fast and one-pot multicomponent reaction of N-sulfonylimines with heterogenous reactivity. Fast reaction times (<5 min) for both acyclic and cyclic sulfonylimine encouraged us to investigate plausible reaction mechanisms using quantum mechanics to identify intermediates and transition states. The heterogeneous reactivity of N-sulfonylimine lead us to develop a human-interpretable machine learning model using positive and negative reaction profiles. We introduce chemical reactivity flowcharts to help chemists interpret the decisions made by the machine learning model for understanding heterogeneous reactivity of N-sulfonylimines. The model learns chemical patterns to accurately predict the reactivity of N-sulfonylimine with different carboxylic acids and can be used to suggest new reactions to elucidate the substrate scope of the reaction. We believe our human-interpretable machine learning approach is a general strategy that is useful to understand chemical reactivity of components for any multicomponent reaction to enhance synthesis of drug-like libraries. Reactivity (psychology) ; Mechanism (biology) ; Scope (computer science) ; Chemistry ; Reaction mechanism ; Biochemical engineering ; Chemical reaction ; Computer science ; Artificial intelligence ; Machine learning ; Computational chemistry ; Organic chemistry ; Catalysis ; Epistemology ; Philosophy Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Steven M. Maley | Doo‐Hyun Kwon | Nick E. Rollins | Johnathan Stanley | Orson L. Sydora | Steven M. Bischof | Daniel H. Ess
Source: ChemRxiv (preprint, 2020-07-26)
Field: Materials Science — Materials Chemistry

Quantum-Mechanical Transition-State Model Combined with Machine Learning Provides Catalyst Design Features for Selective Cr Olefin Oligomerization The use of data science tools to provide the emergence of nontrivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene:1- octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr–N distance, Cr–α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene Selectivity ; Catalysis ; Olefin fiber ; Transition state ; Phosphine ; Transition metal ; Chemistry ; Computational chemistry ; Materials science ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Р. Р. Исмагилов
Source: arXiv (Cornell University) (preprint, 2020-11-25)
Field: Chemistry — Spectroscopy

Processing slightly resolved ro-vibrational spectra during chemical vapor deposition of carbon materials: machine learning approach for plasma thermometry A fast optical spectroscopic method for determination rotational ($T_{rot}$) and vibrational ($T_{vib}$) temperatures in two-temperature Boltzmann distribution of the excited state by using machine learning approach is presented. The method is applied to estimate molecular gas temperatures in a direct current glow discharge in hydrogen-methane gas mixture during plasma-enhanced chemical vapor deposition of carbon film materials. Slightly resolved ro-vibrational optical emission spectrum of the $C_2$ ($ν'=0 \to ν''=0$) Swan band system was used for local temperature measurements in plasma ball. Random Forest algorithm of machine learning was explored for determination of temperature distribution maps. In addition to the $T_{rot}$ , $T_{vib}$ maps, distribution maps and their gradients for electron temperature ($T_e$) and for the emission intensity of the spectral line 516,5$nm$ corresponding to $C_2$ species is presented and is discussed in detail. Chemical vapor deposition ; Boltzmann distribution ; Plasma ; Vibrational temperature ; Excited state ; Analytical Chemistry (journal) ; Spectral line ; Hydrogen ; Emission spectrum ; Methane ; Electron temperature ; Atomic physics ; Chemistry ; Materials science ; Nanotechnology ; Physics ; Thermodynamics ; Environmental chemistry Mass Spectrometry Techniques and Applications ; Spectroscopy and Laser Applications ; Laser-induced spectroscopy and plasma
Authors: Yixiao Chen | Linfeng Zhang | Handong Wang | E Weinan
Source: arXiv (Cornell University) (preprint, 2020-12-29)
Field: Materials Science — Materials Chemistry

DeePKS-kit: a package for developing machine learning-based chemically accurate energy and density functional models We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-source machine learning library, and PySCF, an ab initio computational chemistry program that provides simple and customized tools for developing quantum chemistry codes. It supports the DeePHF and DeePKS methods. In addition to explaining the details in the methodology and the software, we also provide an example of developing a chemically accurate model for water clusters. Computer science ; Software package ; Software ; Computational science ; Density functional theory ; Open source ; Ab initio ; R package ; Simple (philosophy) ; Open source software ; Energy (signal processing) ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry ; Programming language ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Kai Yang
Source: eScholarship (California Digital Library) (article, 2020-01-01)
Field: Materials Science — Ceramics and Composites

Predicting the Young’s modulus of silicate glasses by molecular dynamics simulations and machine learning Understanding the compositional dependence of properties of silicate glass is critical to design novel glasses for various technology applications. With the development in molecular dynamics simulations and machine learning techniques, a combined and fully computational approach, which is able to reveal the relationship between glass composition and its mechanical properties, can be developed and served as a guide prior to experiments and manufacturing. On one hand, machine learning is a powerful tool to predict the properties based on the existing database. On the other hand, molecular dynamics simulation cannot only produce sufficient data points for machine learning models but also provide a detailed picture of the atomic structure of glasses. This atomic-scale knowledge from molecular dynamics simulation contains an intrinsic relationship between glass compositions and their mechanical properties.Here, we first use molecular dynamics simulation to generate the dataset for calcium aluminosilicate glasses and apply different machine learning models to predict their Young’s modulus using glass compositions in Chapter 1. Next, we apply topological constraint theory to quantify the atomic structures of simulated glasses and use this knowledge to predict Young’s modulus for calcium aluminosilicate glass family in Chapter 2. Last, in Chapter 3, we propose a fully analytical model to link the network topology with glass compositions. Molecular dynamics ; Aluminosilicate ; Machine learning ; Modulus ; Artificial intelligence ; Materials science ; Computer science ; Constraint (computer-aided design) ; Silicate ; Statistical physics ; Mechanical engineering ; Physics ; Chemistry ; Composite material ; Engineering ; Computational chemistry Glass properties and applications
Authors: Rohit Batra | Subramanian K. R. S. Sankaranarayanan
Source: Journal of Physics Materials (preprint, 2020-04-22)
Field: Materials Science — Materials Chemistry

Machine learning for multi-fidelity scale bridging and dynamical simulations of materials Molecular dynamics (MD) is a powerful and popular tool for understanding the dynamical evolution of materials at the nano and mesoscopic scales. There are various flavors of MD ranging from the high fidelity albeit computationally expensive ab-initio MD to relatively lower fidelity but much more efficient classical MD such as atomistic and coarse-grained models. Each of these different flavors of MD have been independently used by materials scientists to bring about breakthroughs in materials discovery and design. A significant gulf exists between the various MD flavors, each having varying levels of fidelity. The accuracy of DFT or ab-initio MD is generally much higher than that of classical atomistic simulations which is higher than that of coarse-grained models. Multi-fidelity scale bridging to combine the accuracy and flexibility of ab-initio MD with efficiency classical MD has been a longstanding goal. The advent of big-data analytics has brought to the forefront powerful machine learning methods that can be deployed to achieve this goal. Here, we provide our perspective on the challenges in multi-fidelity scale bridging and trace the developments leading up to the use of machine learning algorithms and data-science towards addressing this grand challenge. Bridging (networking) ; Fidelity ; Computer science ; Ab initio ; Mesoscopic physics ; Flexibility (engineering) ; Molecular dynamics ; High fidelity ; Perspective (graphical) ; Data science ; Artificial intelligence ; Machine learning ; Physics ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications