jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manav Ramprasad | Chiho Kim
Source: arXiv (Cornell University) (preprint, 2019-08-06)
Field: Materials Science — Materials Chemistry

Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures The success of the Materials Genome Initiative has led to opportunities for data-driven approaches for materials discovery. The recent development of Polymer Genome (PG), which is a machine learning (ML) based data-driven informatics platform for polymer property prediction, has significantly increased the efficiency of polymer design. Nevertheless, continuous expansion of the "training data" is necessary to improve the robustness, versatility and accuracy of the ML predictions. In order to test the performance and transferability of the predictive models presently available in PG (which were previously trained on a dataset of 450 polymers), we have carefully collected additional experimental glass transition temperature (Tg) data for 871 polymers from multiple data sources. The Tg values predicted by the present PG models for the polymers in the newly collected dataset were compared directly with the experimental Tg to estimate the accuracy of the present model. Using the full dataset of 1321 polymers, a new ML model for Tg was built following past work. The RMSE of prediction for the extended dataset, when compared to the earlier one, decreased to 27 K from 57 K. To further improve the performance of the Tg prediction model, we are continuing to accumulate new data and exploring new ML approaches. Polymer ; Transferability ; Glass transition ; Robustness (evolution) ; Predictive modelling ; Machine learning ; Computer science ; Mean squared error ; Informatics ; Artificial intelligence ; Materials science ; Chemistry ; Mathematics ; Engineering ; Statistics ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kai Yang | Xinyi Xu | Benjamin Yang | Brian J. Cook | Herbert Ramos | Mathieu Bauchy
Source: arXiv (Cornell University) (preprint, 2019-01-27)
Field: Materials Science — Ceramics and Composites

Prediction of Silicate Glasses' Stiffness by High-Throughput Molecular\n Dynamics Simulations and Machine Learning The development by machine learning of models predicting materials'\nproperties usually requires the use of a large number of consistent data for\ntraining. However, quality experimental datasets are not always available or\nself-consistent. Here, as an alternative route, we combine machine learning\nwith high-throughput molecular dynamics simulations to predict the Young's\nmodulus of silicate glasses. We demonstrate that this combined approach offers\nexcellent predictions over the entire compositional domain. By comparing the\nperformance of select machine learning algorithms, we discuss the nature of the\nbalance between accuracy, simplicity, and interpretability in machine learning.\n Interpretability ; Throughput ; Machine learning ; Computer science ; Artificial intelligence ; Silicate glass ; Silicate ; Simplicity ; Materials science ; Engineering Glass properties and applications ; Geochemistry and Geologic Mapping ; Geological and Geochemical Analysis
Authors: Milad Omrani Tamrin | Sébastien Henwood | Jean-Francois Dubois | Jean‐Jules Brault | Saad Chidami | Samuel-Jean Bassetto
Source:  (article, 2019-06-01)
Field: Materials Science — Materials Chemistry

Using deep learning approaches to overcome limited dataset issues within semiconductor domain Today, in semiconductor manufacturing, wafer failures are frequent problems with the production lines. To increase the production yield, images are the most important pieces of data used to detect defect-free wafers. However, there are many tools that can be installed specifically to monitor production lines, inspect mapped defects and detect the main causes of die failures by using wafers images during semiconductor manufacturing process. The underlying objective is to overcome the need for a physical check on the wafer which are in most cases too long. Thus, the need to have a design that will measure and detect these visual faults in an automated fashion is a big challenge for the industry. Recently, deep learning approaches have proven to be a suitable way to overcome this issue. However, they rely on the availability of sufficiently representative datasets which is not our case: data on anomalies is scarce. The goal of this paper is to evaluate state of the art deep learning methodology such as GAN and VAE to overcome this challenge. Implementation results show that the GAN architecture achieves a convincing image generation in a limited sample setting, while the VAE architecture fails to converge at training time. Computer science ; Deep learning ; Domain (mathematical analysis) ; Artificial intelligence ; Machine learning ; Data science ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Semiconductor materials and devices
Authors: Romit Maulik | Rajeev Surendran Array | Prasanna Balaprakash
Source: arXiv (Cornell University) (preprint, 2019-09-18)
Field: Materials Science — Materials Chemistry

Site-specific graph neural network for predicting protonation energy of oxygenate molecules Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems from the fact that a liquid phase upgrading using mineral acid is a cost-effective chemical transformation. In this chemical upgrading process, adding a proton (positively charged atomic hydrogen) to an oxygen atom is a central step. The protonation energies of oxygen atoms in a molecule determine the thermodynamic feasibility of the reaction and likely chemical reaction pathway. A quantum chemical model based on coupled cluster theory is used to compute accurate thermochemical properties such as the protonation energies of oxygen atoms and the feasibility of protonation-based chemical transformations. However, this method is too computationally expensive to explore a large space of chemical transformations. We develop a graph neural network approach for predicting protonation energies of oxygen atoms of hundreds of bioxygenate molecules to predict the feasibility of aqueous acidic reactions. Our approach relies on an iterative local nonlinear embedding that gradually leads to global influence of distant atoms and a output layer that predicts the protonation energy. Our approach is geared to site-specific predictions for individual oxygen atoms of a molecule in comparison with commonly used graph convolutional networks that focus on a singular molecular property prediction. We demonstrate that our approach is effective in learning the location and magnitudes of protonation energies of oxygenated molecules. Protonation ; Molecule ; Chemistry ; Chemical physics ; Hydrogen ; Chemical space ; Computational chemistry ; Chemical reaction ; Oxygen ; Graph ; Computer science ; Organic chemistry ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: WooSeok Jeong | Samuel J. Stoneburner | Daniel King | Ruye Li | Andrew Walker | Roland Lindh | Laura Gagliardi
Source: ChemRxiv (preprint, 2019-12-20)
Field: Materials Science — Materials Chemistry

Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation &lt;div&gt;Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.&lt;/div&gt; Chemical space ; Complete active space ; Computer science ; Atomic orbital ; Chemical bond ; Space (punctuation) ; Density functional theory ; Dissociation (chemistry) ; Bond order ; Bond length ; Chemistry ; Computational chemistry ; Quantum mechanics ; Physics ; Electron ; Molecule ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Mahmood Khan | Muhammad Afaq | Ihtesham Ul Islam | Javed Iqbal | Muhammad Shoaib
Source: Transactions on Emerging Telecommunications Technologies (article, 2019-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Energy loss prediction in nonoriented materials using machine learning techniques: A novel approach Abstract Traditional extrapolation performed by machine designers for energy loss estimation results in the decrease of the overall efficiency of electrical machines. Therefore, state‐of‐the‐art techniques need to be developed in order to accurately predict the energy loss in electrical machines for their improved performance. To this end, machine learning techniques have been employed to predict accurate energy loss at different frequencies and induction levels under rotational conditions. Such types of flux exist near the teeth of the stator in synchronous machines. In transformers, rotational flux arises at the bends and corners of the stators. It was observed that the random forest machine learning algorithm has the least mean square error and as such is the most suited algorithm, which can be used for the accurate prediction of energy loss in nonoriented materials. Extrapolation ; Stator ; Energy (signal processing) ; Computer science ; Mean squared error ; Random forest ; Transformer ; Artificial intelligence ; Engineering ; Mechanical engineering ; Mathematics ; Electrical engineering ; Statistics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Power Transformer Diagnostics and Insulation
Authors: Mohammad Atif Faiz Afzal | Aditya Sonpal | Mojtaba Haghighatlari | Andrew J. Schultz | Johannes Hachmann
Source: ChemRxiv (preprint, 2019-07-08)
Field: Materials Science — Materials Chemistry

A Deep Neural Network Model for Packing Density Predictions and its Application in the Study of 1.5 Million Organic Molecules The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density.We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations. Artificial neural network ; Process (computing) ; Computer science ; Organic molecules ; Work (physics) ; Molecular dynamics ; Artificial intelligence ; Machine learning ; Statistical physics ; Biological system ; Molecule ; Computational chemistry ; Chemistry ; Physics ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Lixue Cheng | Nikola Kovachki | Matthew Welborn | Thomas F. Miller
Source: arXiv (Cornell University) (preprint, 2019-09-04)
Field: Materials Science — Materials Chemistry

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized geometries of 7211 organic molecules of up to seven heavy atoms, both implementations reach chemical accuracy (1 kcal/mol error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time. Cluster analysis ; Bottleneck ; Computer science ; Regression ; Ground-penetrating radar ; Artificial intelligence ; Kriging ; Machine learning ; Pattern recognition (psychology) ; Linear regression ; Classifier (UML) ; Algorithm ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Siddhartha Laghuvarapu | Yashaswi Pathak | U. Deva Priyakumar
Source: ChemRxiv (preprint, 2019-09-06)
Field: Materials Science — Materials Chemistry

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies. Energy minimization ; Robustness (evolution) ; Density functional theory ; Molecular geometry ; Computer science ; Transferability ; Geometry ; Quantum ; Molecule ; Statistical physics ; Physics ; Algorithm ; Artificial intelligence ; Chemistry ; Machine learning ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: JCS Kadupitiya | Geoffrey Fox | Vikram Jadhao
Source: arXiv (Cornell University) (preprint, 2019-10-31)
Field: Materials Science — Materials Chemistry

Machine Learning for Parameter Auto-tuning in Molecular Dynamics\n Simulations: Efficient Dynamics of Ions near Polarizable Nanoparticles Simulating the dynamics of ions near polarizable nanoparticles (NPs) using\ncoarse-grained models is extremely challenging due to the need to solve the\nPoisson equation at every simulation timestep. Recently, a molecular dynamics\n(MD) method based on a dynamical optimization framework bypassed this obstacle\nby representing the polarization charge density as virtual dynamic variables,\nand evolving them in parallel with the physical dynamics of ions. We highlight\nthe computational gains accessible with the integration of machine learning\n(ML) methods for parameter prediction in MD simulations by demonstrating how\nthey were realized in MD simulations of ions near polarizable NPs. An\nartificial neural network based regression model was integrated with MD\nsimulation and predicted the optimal simulation timestep and optimization\nparameters characterizing the virtual system with $94.3\\%$ success. The\nML-enabled auto-tuning of parameters generated accurate dynamics of ions for\n$\\approx 10$ million steps while improving the stability of the simulation by\nover an order of magnitude. The integration of ML-enhanced framework with\nhybrid OpenMP/MPI parallelization techniques reduced the computational time of\nsimulating systems with thousands of ions and induced charges from thousands of\nhours to tens of hours, yielding a maximum speedup of $\\approx 3$ from ML-only\nacceleration and a maximum speedup of $\\approx 600$ from the combination of ML\nand parallel computing methods. Extraction of ionic structure in concentrated\nelectrolytes near oil-water emulsions demonstrates the success of the method.\nThe approach can be generalized to select optimal parameters in other MD\napplications and energy minimization problems.\n Molecular dynamics ; Speedup ; Computer science ; Ion ; Polarizability ; Kriging ; Multiphysics ; Computational science ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Parallel computing ; Machine learning ; Finite element method ; Quantum mechanics Machine Learning in Materials Science ; Electrostatics and Colloid Interactions
Authors: John S. Schreck | Connor W. Coley | Kyle J. M. Bishop
Source: arXiv (Cornell University) (preprint, 2019-01-19)
Field: Materials Science — Materials Chemistry

Learning retrosynthetic planning through self-play The problem of retrosynthetic planning can be framed as one player game, in which the chemist (or a computer program) works backwards from a molecular target to simpler starting materials though a series of choices regarding which reactions to perform. This game is challenging as the combinatorial space of possible choices is astronomical, and the value of each choice remains uncertain until the synthesis plan is completed and its cost evaluated. Here, we address this problem using deep reinforcement learning to identify policies that make (near) optimal reaction choices during each step of retrosynthetic planning. Using simulated experience or self-play, we train neural networks to estimate the expected synthesis cost or value of any given molecule based on a representation of its molecular structure. We show that learned policies based on this value network outperform heuristic approaches in synthesizing unfamiliar molecules from available starting materials using the fewest number of reactions. We discuss how the learned policies described here can be incorporated into existing synthesis planning tools and how they can be adapted to changes in the synthesis cost objective or material availability. Retrosynthetic analysis ; Computer science ; Heuristic ; Reinforcement learning ; Plan (archaeology) ; Value (mathematics) ; Artificial intelligence ; Representation (politics) ; Space (punctuation) ; Machine learning ; Operations research ; Engineering Machine Learning in Materials Science ; Process Optimization and Integration ; Innovative Microfluidic and Catalytic Techniques Innovation