jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vincent Blay | Jie Dong | Andrés Moyá
Source: Biofuels Bioproducts and Biorefining (article, 2021-08-27)
Field: Materials Science — Materials Chemistry

Machine learning study of the molecular drivers of natural product prices Abstract The price of chemicals is a very complex variable. It can be impacted by production costs but also by market and managerial factors, which may have complex relationships with molecular characteristics and the state of technology and society. In this work, we explore the extent to which molecular characteristics can help explain natural product prices with the aid of machine learning tools. We interpret models trained on molecular descriptors and molecular fingerprints. These models can explain a notable proportion of the variation in prices, suggesting that production and separation costs are a major contributor to current natural product prices. Some molecular properties stand out as key price drivers across the chemical space, including hydrophobicity and the presence of certain heteroatoms. On the other hand, we demonstrate how the application of cliff analysis to prices allows the identification of small chemical transformations that have a remarkable impact on prices. Overall, the work suggests that machine learning could help achieve more consistent and fairer pricing and provides specific examples of chemical transformations in which synthetic biology could add significant value. © 2021 Society of Chemical Industry and John Wiley &amp; Sons, Ltd Product (mathematics) ; Value (mathematics) ; Chemical space ; Production (economics) ; Biochemical engineering ; Variable (mathematics) ; Work (physics) ; Natural product ; Artificial intelligence ; Machine learning ; Computer science ; Economics ; Econometrics ; Microeconomics ; Chemistry ; Mathematics ; Engineering ; Drug discovery Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: D. S. Vdovin | A. Yu. Abramochkin | А. С. Бородулин | V. A. Nelyub
Source: MATEC Web of Conferences (article, 2021-01-01)
Field: Materials Science — General Materials Science

Method of Predicting the Polymer Composites’ Properties Using Neural Network Modeling A neural network modelling technique and its training to diagnose polymer composite materials based on tomography data is introduced. As an object of study, carbon fiber made by vacuum infusion technology using an epoxy binder is considered. X-ray microtomography was used to analyze its structure and the provided images were used as a database for creating a neural network. A neural network modelling technique and its training was developed, including an algorithm for converting tomograph images into data on the structure of the phase composition and the physical and mechanical properties of the object under study. Artificial neural network ; Epoxy ; Materials science ; Composite material ; Composite number ; Polymer ; Tomography ; Computed tomography ; Computer science ; Artificial intelligence ; Optics Material Properties and Applications ; Industrial Engineering and Technologies ; Polymer Science and Applications
Authors: S. Pandey | Jiaxing Qu | Vladan Stevanović | Peter C. St. John | Prashun Gorai
Source: ChemRxiv (preprint, 2021-04-16)
Field: Materials Science — Materials Chemistry

A Graph Neural Network for Predicting Energy and Stability of Known and Hypothetical Crystal Structures The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerating the screening for new materials over vast chemical spaces. Here, we develop a unique graph neural network model to accurately predict the total energy of both GS and higher-energy hypothetical structures. We use ~16,500 density functional theory calculated total energy from the NREL Materials Database and ~11,000 in-house generated hypothetical structures to train our model, thus making sure that the model is not biased towards either GS or higher-energy structures. We also demonstrate that our model satisfactorily ranks the structures in the correct order of their energies for a given composition. Furthermore, we present a thorough error analysis to explain several failure modes of the model, which highlights both prediction outliers and occasional inconsistencies in the training data. By peeling back layers of the neural network model, we are able to derive chemical trends by analyzing how the model represents learned structures and properties. Artificial neural network ; Outlier ; Stability (learning theory) ; Computer science ; Energy (signal processing) ; Graph ; Graph theory ; Artificial intelligence ; Machine learning ; Data mining ; Theoretical computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Ross Drummond | Mathew C. Turner | Stephen R. Duncan
Source: arXiv (Cornell University) (preprint, 2021-02-18)
Field: Materials Science — Materials Chemistry

Reduced-Order Neural Network Synthesis with Robustness Guarantees In the wake of the explosive growth in smartphones and cyberphysical systems, there has been an accelerating shift in how data is generated away from centralised data towards on-device generated data. In response, machine learning algorithms are being adapted to run locally on board, potentially hardware limited, devices to improve user privacy, reduce latency and be more energy efficient. However, our understanding of how these device orientated algorithms behave and should be trained is still fairly limited. To address this issue, a method to automatically synthesize reduced-order neural networks (having fewer neurons) approximating the input/output mapping of a larger one is introduced. The reduced-order neural network's weights and biases are generated from a convex semi-definite programme that minimises the worst-case approximation error with respect to the larger network. Worst-case bounds for this approximation error are obtained and the approach can be applied to a wide variety of neural networks architectures. What differentiates the proposed approach to existing methods for generating small neural networks, e.g. pruning, is the inclusion of the worst-case approximation error directly within the training cost function, which should add robustness. Numerical examples highlight the potential of the proposed approach. The overriding goal of this paper is to generalise recent results in the robustness analysis of neural networks to a robust synthesis problem for their weights and biases. Robustness (evolution) ; Computer science ; Artificial neural network ; Function approximation ; Approximation error ; Algorithm ; Machine learning Machine Learning in Materials Science ; Machine Learning and ELM ; Neural Networks and Applications
Authors: Pin Chen | Jianwen Chen | Hui Yan | MO Qing | Zexin Xu | Jinyu Liu | Wenqing Zhang | Yuedong Yang | Yutong Lu
Source: arXiv (Cornell University) (preprint, 2021-12-29)
Field: Materials Science — Materials Chemistry

Leveraging Large-scale Computational Database and Deep Learning for Accurate Prediction of Material Properties Accurately predicting the physical and chemical properties of materials remains one of the most challenging tasks in material design, and one effective strategy is to construct a reliable data set and use it for training a machine learning model. In this study, we constructed a large-scale material genome database (Matgen) containing 76,463 materials collected from experimentally-observed database, and computed their bandgap properties through the Density functional theory (DFT) method with Perdew-Burke-Ernzehof (PBE) functional. We verified the computation method by comparing part of our results with those from the open Material Project (MP) and Open Quantum Materials Database (OQMD), all with PBE computations, and found that Matgen achieved the same computation accuracy based on both measured and computed bandgap properties. Based on the computed properties of our comprehensive dataset, we have developed a new graph-based deep learning model, namely CrystalNet, through our recently developed Communicative Message Passing Neural Network (CMPNN) framework. The model was shown to outperform other state-of-the-art prediction models. A further fine-tuning on 1716 experimental bandgap values (CrystalNet-TL) achieved a superior performance with mean absolute error (MAE) of 0.77 eV on independent test, which has outperformed the pure PBE (1.14~1.45 eV). Moreover, the model was proven applicable to hypothetical materials with MAE of 0.77 eV as referred by computations from HSE, a highly accurate quantum mechanics (QM) method, consist better than PBE (MAE=1.13eV). We also made material structures, computed properties by PBE, and the CrystalNet models publically available at https://matgen.nscc-gz.cn. Computation ; Computer science ; Band gap ; Density functional theory ; Artificial intelligence ; Test set ; Artificial neural network ; Scale (ratio) ; Set (abstract data type) ; Deep learning ; Graph ; Machine learning ; Algorithm ; Materials science ; Theoretical computer science ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: N. I. Baurova | A. Yu. Konoplin
Source: Russian Metallurgy (Metally) (article, 2021-12-01)
Field: Materials Science — General Materials Science

Estimation of the Dynamics of Changing the Properties of Materials Using Neural Network Modeling The algorithms developed to describe the behavior of polymer composite materials under various loading conditions using simulation and neural network modeling are described. The main stages of these processes are described, and their advantages and disadvantages are summarized. The major input and output parameters, which should be reasonably applied for development of models intended for estimating the life of polymer composite materials, are listed. Estimation ; Artificial neural network ; Dynamics (music) ; Computer science ; Econometrics ; Artificial intelligence ; Mathematics ; Engineering ; Psychology ; Systems engineering Material Properties and Applications ; Advanced Materials and Semiconductor Technologies ; Material Science and Thermodynamics
Authors: Boyu Zhang | Musen Zhou | Jian Wu | Fuchang Gao
Source: arXiv (Cornell University) (preprint, 2021-02-16)
Field: Materials Science — Materials Chemistry

Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors Accurate prediction of physical properties is critical for discovering and designing novel materials. Machine learning technologies have attracted significant attention in the materials science community for their potential for large-scale screening. Graph Convolution Neural Network (GCNN) is one of the most successful machine learning methods because of its flexibility and effectiveness in describing 3D structural data. Most existing GCNN models focus on the topological structure but overly simplify the three-dimensional geometric structure. However, in materials science, the 3D-spatial distribution of atoms is crucial for determining the atomic states and interatomic forces. This paper proposes an adaptive GCNN with a novel convolution mechanism that simultaneously models atomic interactions among all neighbor atoms in three-dimensional space. We apply the proposed model to two distinctly challenging problems on predicting material properties. The first is Henry's constant for gas adsorption in Metal-Organic Frameworks (MOFs), which is notoriously difficult because of its high sensitivity to atomic configurations. The second is the ion conductivity in solid-state crystal materials, which is difficult because of few labeled data available for training. The new model outperforms existing graph-based models on both data sets, suggesting that the critical three-dimensional geometric information is indeed captured. Computer science ; Graph ; Convolutional neural network ; Artificial neural network ; Convolution (computer science) ; Invariant (physics) ; Theoretical computer science ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Edward Elson Kosasih | Joaquín Cabezas | Xavier Sumba | Piotr Bielak | Kamil Tagowski | Kelvin Idanwekhai | Benedict Aaron Tjandra | Arian R. Jamasb
Source: arXiv (Cornell University) (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The PCQM4M-LSC dataset defines a molecular HOMO-LUMO property prediction task on about 3.8M graphs. In this short paper, we show our current work-in-progress solution which builds an ensemble of three graph neural networks models based on GIN, Bayesian Neural Networks and DiffPool. Our approach outperforms the provided baseline by 7.6%. Moreover, using uncertainty in our ensemble's prediction, we can identify molecules whose HOMO-LUMO gaps are harder to predict (with Pearson's correlation of 0.5181). We anticipate that this will facilitate active learning. Benchmark (surveying) ; Computer science ; Artificial intelligence ; Machine learning ; Graph ; Artificial neural network ; Property (philosophy) ; Ensemble learning ; Scale (ratio) ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xinyao Liu | Kasra Amini | Aurelien Sanchez | Blanca Belsa | Tobias Steinle | Jens Biegert
Source: arXiv (Cornell University) (preprint, 2021-10-12)
Field: Chemistry — Spectroscopy

Machine learning for laser-induced electron diffraction imaging of molecular structures Ultrafast diffraction imaging is a powerful tool to retrieve the geometric structure of gas-phase molecules with combined picometre spatial and attosecond temporal resolution. However, structural retrieval becomes progressively difficult with increasing structural complexity, given that a global extremum must be found in a multi-dimensional solution space. Worse, pre-calculating many thousands of molecular configurations for all orientations becomes simply intractable. As a remedy, here, we propose a machine learning algorithm with a convolutional neural network which can be trained with a limited set of molecular configurations. We demonstrate structural retrieval of a complex and large molecule, Fenchone (C$_{10}$H$_{16}$O), from laser-induced electron diffraction (LIED) data without fitting algorithms or ab initio calculations. Retrieval of such a large molecular structure is not possible with other variants of LIED or ultrafast electron diffraction. Combining electron diffraction with machine learning presents new opportunities to image complex and larger molecules in static and time-resolved studies. Ultrafast electron diffraction ; Electron diffraction ; Attosecond ; Diffraction ; Phase retrieval ; Computer science ; Convolutional neural network ; Ultrashort pulse ; Set (abstract data type) ; Gas electron diffraction ; Electron ; Molecule ; Artificial intelligence ; Algorithm ; Optics ; Physics ; Laser ; Reflection high-energy electron diffraction ; Quantum mechanics ; Fourier transform Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Laser-Matter Interactions and Applications
Authors: Simon Johansson | Hampus Gummesson Svensson | Esben Jannik Bjerrum | Alexander Schliep | Morteza Haghir Chehreghani | Christian Tyrchan | Ola Engkvist
Source: ChemRxiv (preprint, 2021-06-30)
Field: Materials Science — Materials Chemistry

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction Computer aided synthesis planning is a rapidly growing field for suggesting synthetic routes for molecules of interest. The methods used are usually dependent on access to large datasets for training, but with a finite experimental budget there are limitations on how much data can be obtained from experiments. Active learning, which has been used in recent studies with success, is a strategy to identify which data points impact model accuracy the most. However, little has been done to explore the robustness of the methods predicting reaction yield. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined accuracy (AUROC) faster than using passive learning. Feature importance analysis of the trained machine learning models suggested active learning had larger influence on the model accuracy when only a few features were important for the model prediction. Machine learning ; Computer science ; Artificial intelligence ; Active learning (machine learning) ; Robustness (evolution) ; Margin (machine learning) ; Support vector machine ; Yield (engineering) Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Authors: N. E. Babushkina | A. A. Lyapin
Source: Journal of Physics Conference Series (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Solving the problem of classification of material properties using a neural network Abstract The article sets the task of classifying various materials and determining their belonging to a specified group using a recurrent neural network. The practical significance of the article is to obtain the results of the neural network, confirming the possibility of classifying materials by the hardness parameter using a neural network. As part of the study, a number of experimental measurements were carried out. The structure of the neural network and its main components are described. The statistical parameters of the experimental data are estimated. Artificial neural network ; Artificial intelligence ; Computer science ; Probabilistic neural network ; Machine learning ; Pattern recognition (psychology) ; Task (project management) ; Time delay neural network ; Data mining ; Engineering Structural mechanics and materials ; Engineering and Environmental Studies ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Gregory H. Teichert | Anirudh Raju Natarajan | Sambit Das | Muratahan Aykol | Chirranjeevi Balaji Gopal | Vikram Gavini | Anton Van der Ven | Krishna Garikipati
Source:  (preprint, 2021-09-10)
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 theories of phase transformations and microstructure evolution.It encodes the thermodynamic coupling between different fields, such as mechanics and chemistry, within continuum descriptions of non-equilibrium materials phenomena.In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains results in a free energy description that occupies a high-dimensional space.Scale bridging between the electronic structure of a solid and continuum descriptions of its non-equilibrium behavior can be realized with integrable deep neural networks (IDNN) that are trained to free energy derivative data generated by first-principles statistical mechanics simulations and then analytically integrating to recover a free energy density function.Here we combine the IDNN with an active learning workflow to ensure well-distributed sampling of the free energy derivative data in high-dimensional input spaces, thereby enabling true scale bridging between first-principles statistical mechanics and continuum phase field models.As a prototypical material system we focus on Ni-Al.Cahn-Hilliard and Allen-Cahn 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.This work advances the treatment of scale bridging, starting with electronic structure calculations and proceeding through statistical mechanics to continuum physics.Its coupling of Cahn-Hilliard and Allen-Cahn phase field descriptions with nonlinear elasticity through the free energy density ensures a rigorous treatment of phase transformation phenomena. Bridging (networking) ; Integrable system ; Workflow ; Scale (ratio) ; Artificial neural network ; Function (biology) ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics ; Biology ; Mathematical physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Peng Wang | Jinshuo Mei | Yingjie Lang | Shu Li
Source: Journal of Physics Conference Series (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Regression Algorithm Predicts Multi-component Crystal Configuration Energy Abstract Some machine learning algorithm tools, such as neural networks and Gaussian process regression, are increasingly being applied to the exploration of materials. Here, we have developed a form to use this nonlinear interpolation tool to describe properties that depend on the degrees of freedom in multi-component solids. A symmetrically adapted clustering function is used to distinguish different atomic order degrees. These features are used as the input of neural networks, Gaussian process regression and other algorithmic models, and some inherent properties of materials, such as formation energy, can be reproduced by the trained machine algorithm model. We use this technique to reproduce the expansion Hamiltonian of a synthetic cluster with multi-body interaction, and calculate the formation energy of ZrO based on first principles. The form proposed in this paper and the results shown that complex multi-body interactions can be approximated by nonlinear models involving smaller clusters. The training models used in this paper to predict energy include neural networks, Gaussian process regression, random forests, and support vectors regression, using MSE and coefficient of determination to evaluate the prediction results, and adding genetic algorithms in the feature selection process can remove some redundant features and improve the prediction efficiency and accuracy. The results show that the neural network is the best algorithm model which selected in this article, the prediction effect of support vector regression is relatively inferior. Artificial neural network ; Support vector machine ; Kriging ; Computer science ; Gaussian process ; Artificial intelligence ; Algorithm ; Machine learning ; Cluster analysis ; Nonlinear regression ; Component (thermodynamics) ; Nonlinear system ; Regression ; Random forest ; Regression analysis ; Gaussian ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods