jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amin Karimi Monsefi | Rana Bakhtiyarzade
Source: arXiv (Cornell University) (preprint, 2019-12-07)
Field: Materials Science — Materials Chemistry

Solving the Reaction-Diffusion equation based on analytical methods and\n deep learning algorithm; the Case study of sulfate attack to concrete The reaction-diffusion equation is one of the cornerstones equations in\napplied science and engineering. In the present study, a deep neural network\nhas been trained in order to predict the solution of the equation with\ndifferent coefficients using the numerical solution of this equation and the\nutility of deep learning. Analytical solution of the Reaction-Diffusion\nequation also has been conducted by taking advantage of the Danckwerts method.\nThe accuracy of deep learning results was compared with the analytical\nsolutions. In order to decrease the learning time and to find out similar\nequations solutions, such as pure diffusion and pure reaction, dimensional\nanalysis technique has been performed. It was demonstrated that deep learning\ncan accurately estimate the Partial Differential Equations solutionin the case\nof the reaction-diffusion equation with a constant coefficient.\n Reaction–diffusion system ; Diffusion equation ; Diffusion ; Partial differential equation ; Artificial neural network ; Deep learning ; Differential equation ; Chemical equation ; Constant (computer programming) ; Algorithm ; Computer science ; Mathematics ; Applied mathematics ; Mathematical analysis ; Artificial intelligence ; Chemistry ; Thermodynamics ; Physics ; Engineering ; Physical chemistry Material Properties and Failure Mechanisms ; Coal Combustion and Slurry Processing ; Dam Engineering and Safety
Authors: Yihang Wang | João Marcelo Lamim Ribeiro | Pratyush Tiwary
Source: arXiv (Cornell University) (preprint, 2019-09-25)
Field: Materials Science — Materials Chemistry

Machine learning approaches for analyzing and enhancing molecular dynamics simulations Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems, there remain methodological difficulties to be surmounted. First, how to make the deluge of data generated in running even a microsecond long MD simulation human comprehensible. Second, how to efficiently sample the underlying free energy surface and kinetics. In this short perspective, we summarize machine learning based ideas that are solving both of these limitations, with a focus on their key theoretical underpinnings and remaining challenges. Computer science ; Perspective (graphical) ; Focus (optics) ; Data science ; Molecular dynamics ; Key (lock) ; Artificial intelligence ; Machine learning ; Human–computer interaction ; Management science ; Chemistry ; Physics ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Peng Gao | Jun Zhang | Qian Peng | Vassiliki‐Alexandra Glezakou
Source: ChemRxiv (preprint, 2019-12-10)
Field: Chemistry — Spectroscopy

A General Protocol for the Accurate Predictions of Molecular 13C/1H NMR Chemical Shifts via Machine Learning Accurate prediction of NMR chemical shifts with affordable computational cost is of great importance for rigorous structural assignments of experimental studies. However, the most popular computational schemes for NMR calculation—based on density functional theory (DFT) and gauge-including atomic orbital (GIAO) methods—still suffer from ambiguities in structural assignments. Using state-of-the-art machine learning (ML) techniques, we have developed a DFT+ML model that is capable of predicting 13C/1H NMR chemical shifts of organic molecules with high accuracy. The input for this generalizable DFT+ML model contains two critical parts: one is a vector providing insights into chemical environments, which can be evaluated without knowing the exact geometry of the molecule; the other one is the DFT-calculated isotropic shielding constant. The DFT+ML model was trained with a dataset containing 476 13C and 270 1H experimental chemical shifts. For the DFT methods used here, the root-mean-square-derivations (RMSDs) for the errors between predicted and experimental 13C/1H chemical shifts are as small as 2.10/0.18 ppm, which is much lower than the typical DFT (5.54/0.25 ppm), or DFT+linear regression (4.77/0.23 ppm) approaches. It also has smaller RMSDs and maximum absolute errors than two previously reported NMR-predicting ML models. We test the robustness of the model on two classes of organic molecules (TIC10 and hyacinthacines), where we unambiguously assigned the correct isomers to the experimental ones. This DFT+ML model is a promising way of predicting NMR chemical shifts and can be easily adapted to calculated shifts for any chemical compound.&lt;br&gt; Chemical shift ; Density functional theory ; Carbon-13 NMR ; Isotropy ; Molecule ; Robustness (evolution) ; Computational chemistry ; Chemistry ; Computer science ; Machine learning ; Physics ; Physical chemistry ; Quantum mechanics Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Daniel T. Chang
Source: arXiv (Cornell University) (preprint, 2019-10-24)
Field: Materials Science — Materials Chemistry

Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Classification Tiered graph autoencoders provide the architecture and mechanisms for learning tiered latent representations and latent spaces for molecular graphs that explicitly represent and utilize groups (e.g., functional groups). This enables the utilization and exploration of tiered molecular latent spaces, either individually - the node (atom) tier, the group tier, or the graph (molecule) tier - or jointly, as well as navigation across the tiers. In this paper, we discuss the use of tiered graph autoencoders together with graph prediction for molecular graphs. We show features of molecular graphs used, and groups in molecular graphs identified for some sample molecules. We briefly review graph prediction and the QM9 dataset for background information, and discuss the use of tiered graph embeddings for graph prediction, particularly weighted group pooling. We find that functional groups and ring groups effectively capture and represent the chemical essence of molecular graphs (structures). Further, tiered graph autoencoders and graph prediction together provide effective, efficient and interpretable deep learning for molecular graphs, with the former providing unsupervised, transferable learning and the latter providing supervised, task-optimized learning. Pooling ; Molecular graph ; Computer science ; Graph ; Artificial intelligence ; Deep learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Aini Palizhati | Wen Zhong | Kevin Tran | Zachary W. Ulissi
Source: ChemRxiv (preprint, 2019-07-08)
Field: Materials Science — Materials Chemistry

Predicting Intermetallic Surface Energies with High-Throughput DFT and Convolutional Neural Networks Surface energy of inorganic crystals is crucial in understanding experimentally-relevant surface properties and thus important in designing materials for many applications including catalysis. Predictive methods and datasets exist for surface energies of monometallic crystals but predicting these properties for bimetallic or more complicated surfaces is an open challenge. Here we present a workflow for predicting surface energies \textit{ab initio} using high-throughput DFT and a machine learning framework. We calculate the surface energy of 3,285 intermetallic alloys with combinations of 36 elements and 47 space groups. We used this high-throughput workflow to seed a database of surface energies, which we used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model was able to predict surface energies with a mean absolute test error of 0.0082 eV/angstrom^2 and can qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into which surfaces are more stable and therefore more realistic. It allows us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalysts screening and nanomaterials synthesis. Bimetallic strip ; Intermetallic ; Workflow ; Convolutional neural network ; Materials science ; Surface (topology) ; Throughput ; Computer science ; Nanomaterials ; Artificial neural network ; Nanoparticle ; Computational science ; Catalysis ; Nanotechnology ; Machine learning ; Chemistry ; Mathematics ; Database ; Geometry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Quang Van Nguyen | Sandip De | Junhong Lin | Volkan Cevher
Source: International Journal of Quantum Chemistry (article, 2019-02-05)
Field: Materials Science — Materials Chemistry

Chemical machine learning with kernels: The impact of loss functions Abstract Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature has spent a considerable effort in designing representations that capture basic physical properties. Our work focuses on the less‐studied learning formulations in this context in order to exploit inner structures in the prediction errors. In particular, we propose to directly optimize basic loss functions of the prediction error metrics typically used in the literature, such as the mean absolute error or the worst case error. In some instances, a proper choice of the loss function can directly reduce reasonably the prediction performance in the desired metric, albeit at the cost of additional computations during training. To support this claim, we describe the statistical learning theoretic foundations, and provide supporting numerical evidence with the prediction of atomization energies for a database of small organic molecules. Metric (unit) ; Computer science ; Exploit ; Property (philosophy) ; Computation ; Context (archaeology) ; Function (biology) ; Machine learning ; Artificial intelligence ; Work (physics) ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Hyungro Lee | Heng Ma | Matteo Turilli | Debsindhu Bhowmik | Shantenu Jha | Arvind Ramanathan
Source: arXiv (Cornell University) (preprint, 2019-09-17)
Field: Materials Science — Materials Chemistry

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for\n Protein Folding Simulations of biological macromolecules play an important role in\nunderstanding the physical basis of a number of complex processes such as\nprotein folding. Even with increasing computational power and evolution of\nspecialized architectures, the ability to simulate protein folding at atomistic\nscales still remains challenging. This stems from the dual aspects of high\ndimensionality of protein conformational landscapes, and the inability of\natomistic molecular dynamics (MD) simulations to sufficiently sample these\nlandscapes to observe folding events. Machine learning/deep learning (ML/DL)\ntechniques, when combined with atomistic MD simulations offer the opportunity\nto potentially overcome these limitations by: (1) effectively reducing the\ndimensionality of MD simulations to automatically build latent representations\nthat correspond to biophysically relevant reaction coordinates (RCs), and (2)\ndriving MD simulations to automatically sample potentially novel conformational\nstates based on these RCs. We examine how coupling DL approaches with MD\nsimulations can fold small proteins effectively on supercomputers. In\nparticular, we study the computational costs and effectiveness of scaling\nDL-coupled MD workflows by folding two prototypical systems, viz., Fs-peptide\nand the fast-folding variant of the villin head piece protein. We demonstrate\nthat a DL driven MD workflow is able to effectively learn latent\nrepresentations and drive adaptive simulations. Compared to traditional\nMD-based approaches, our approach achieves an effective performance gain in\nsampling the folded states by at least 2.3x. Our study provides a quantitative\nbasis to understand how DL driven MD simulations, can lead to effective\nperformance gains and reduced times to solution on supercomputing resources.\n Molecular dynamics ; Computer science ; Folding (DSP implementation) ; Workflow ; Curse of dimensionality ; Protein folding ; Scaling ; Computational science ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Software Engineering Research
Authors: Yasuharu Okamoto
Source: ACS Omega (article, 2019-06-10)
Field: Materials Science — Materials Chemistry

Bundling in Molecular Dynamics Simulations To Improve Generalization Performance in High-Dimensional Neural Network Potentials We examined the influence of using bundling trajectories in molecular dynamics (MD) simulations for predicting energies in high-dimensional neural network potentials. In particular, we focused on the chemical transferability of gold nanoclusters, that is, how well the energy of gold clusters was estimated from the training data comprising gold clusters of different shapes and sizes. We observed that as the number of MD simulations in the training data increased, the accuracy of the predicted energies improved. Nanoclusters ; Transferability ; Molecular dynamics ; Generalization ; Artificial neural network ; Energy (signal processing) ; Statistical physics ; Chemical Dynamics ; Computer science ; Dynamics (music) ; Training set ; Chemical physics ; Materials science ; Nanotechnology ; Physics ; Artificial intelligence ; Computational chemistry ; Chemistry ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Marco Fronzi | Mutaz Abu Ghazaleh | Olexandr Isayev | David A. Winkler | Joseph G. Shapter | Michael J. Ford
Source: arXiv (Cornell University) (preprint, 2019-11-20)
Field: Materials Science — Materials Chemistry

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can be often extremely time consuming. We describe a time and resource-efficient machine learning approach to create a large dataset of structural properties of van der Waals layered structures. In particular, we focus on the interlayer energy and the elastic constant of layered materials composed of two different 2-dimensional (2D) structures, that are important for novel solid lubricant and super-lubricant materials. We show that machine learning models can recapitulate results of computationally expansive approaches (i.e. density functional theory) with high accuracy. Expansive ; Lubricant ; Computer science ; Focus (optics) ; van der Waals force ; Acceleration ; Resource (disambiguation) ; Field (mathematics) ; Computational science ; Artificial intelligence ; Machine learning ; Materials science ; Physics ; Mathematics ; Composite material Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; 2D Materials and Applications
Authors: Aravind Krishnamoorthy | Pankaj Rajak | Aiichiro Nakano | Rajiv K. Kalia | Priya Vashishta
Source: Bulletin of the American Physical Society (article, 2019-03-06)
Field: Materials Science — Materials Chemistry

Machine learning of reaction pathways in chemical vapor deposition of MoS 2 monolayers Monolayer ; Chemical vapor deposition ; Materials science ; Chemical engineering ; Deposition (geology) ; Nanotechnology ; Chemistry ; Engineering ; Geology Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Raimondas Galvelis | Stefan Doerr | João M. Damas | M J Harvey | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2019-07-16)
Field: Materials Science — Materials Chemistry

PlayMolecule Parameterize: a Scalable Molecular Force Field Parameterization Method Based on Quantum-Level Machine Learning Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FFs are not generally available for all molecules, like novel drug-like molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present Parameterize, an automated FF parameterization method based on neural network potentials, which are trained to predict QM energies. We show our method produces more accurate parameters than the general AMBER FF (GAFF2), while requiring just a fraction of time compared with an equivalent parameterization using QM calculations. We expect our method to be of critical importance in computational structure-based drug discovery. Parameterize is available online at PlayMolecule (this http URL). Force field (fiction) ; Scalability ; Computer science ; Field (mathematics) ; Quantum ; Fraction (chemistry) ; Algorithm ; Artificial neural network ; Statistical physics ; Physics ; Machine learning ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shulin Luo | Bangyu Xing | Jian Lv | Lijun Zhang
Source: Bulletin of the American Physical Society (article, 2019-03-05)
Field: Materials Science — Materials Chemistry

Crystal structure prototype database based on machine learning classification of existing inorganic material structures Computer science ; Database ; Crystal (programming language) ; Crystal structure ; Artificial intelligence ; Materials science ; Crystallography ; Chemistry ; Programming language Machine Learning in Materials Science ; Mineral Processing and Grinding ; Industrial Engineering and Technologies
Authors: Raimondas Galvelis | Stefan Doerr | João M. Damas | M J Harvey | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2019-07-16)
Field: Materials Science — Materials Chemistry

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FFs are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present an automated FF parameterization method which can utilize either DFT calculations or approximate QM energies produced by different neural network potentials (NNPs), to obtain improved parameters for molecules. We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2). We expect our method to be of critical importance in computational structure-based drug discovery. The current version is available at PlayMolecule (www.playmolecule.org) and implemented in HTMD, allowing to parameterize molecules with different QM and NNP options. Force field (fiction) ; Density functional theory ; Scalability ; Field (mathematics) ; Quantum ; Computer science ; Molecule ; Statistical physics ; Artificial neural network ; Algorithm ; Physics ; Quantum mechanics ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Michael Gastegger | Kristof T. Schütt | Huziel E. Sauceda | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: Bulletin of the American Physical Society (article, 2019-03-05)
Field: Materials Science — Materials Chemistry

Modeling Molecular Spectra with Interpretable Atomistic Neural Networks Artificial neural network ; Statistical physics ; Molecular dynamics ; Spectral line ; Computer science ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Computational chemistry ; Physics ; Chemistry ; Biology ; Quantum mechanics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies
Authors: Simon Crase | Benjamin J. Hall | Suresh N. Thennadil | Kenneth Bailey | R Brereton | K Butler | D Davies | H Cartwright | O Isayev | A Walsh | D Bouldin | Jasper Engel | Jan Gerretzen | Ewa Szymaska | Jeroen Jansen | Gerard Downey | Lionel Blanchet | M Lutgarde | Buydens | Martin Ester | Hans-Peter Kriegel | Jrg Sander | Xiaowei Xu | Anil Jain | Richard Dubes | Peter Rousseeuw
Source: El Sawah, S. (ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation. (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

Identifying intelligence links in threat networks through machine learning on explosives chemical data Improvised Explosive Devices (IEDs) and the terrorist or threat networks that employ them pose an ongoing threat in military operations. A significant challenge is identifying the intelligence linkages and relationships between the individuals that form these threat networks. However, this information is essential if these networks are to be disrupted. This paper presents a novel concept for identifying these network linkages that can complement the threat network understanding generated through traditional military intelligence means. Explosive material ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: John L. Bohn
Source: New Journal of Physics (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Work smarter, not harder: scientists use machine-learning algorithms to streamline quantum chemistry calculations Physics ; Quantum chemistry ; Work (physics) ; Quantum ; Statistical physics ; Algorithm ; Machine learning ; Data science ; Theoretical physics ; Quantum mechanics ; Computer science ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods