jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dávid Péter Kovács | William McCorkindale | Alpha A. Lee
Source: ChemRxiv (preprint, 2020-10-08)
Field: Materials Science — Materials Chemistry

Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Organic synthesis remains a stumbling block in drug discovery. Although a plethora of machine learning models have been proposed as solutions in the literature, they suffer from being opaque black-boxes. It is neither clear if the models are making correct predictions because they inferred the salient chemistry, nor is it clear which training data they are relying on to reach a prediction. This opaqueness hinders both model developers and users. In this paper, we quantitatively interpret the Molecular Transformer, the state-of-the-art model for reaction prediction. We develop a framework to attribute predicted reaction outcomes both to specific parts of reactants, and to reactions in the training set. Furthermore, we demonstrate how to retrieve evidence for predicted reaction outcomes, and understand counterintuitive predictions by scrutinising the data. Additionally, we identify ”Clever Hans” predictions where the correct prediction is reached for the wrong reason due to dataset bias. We present a new debiased dataset that provides a more realistic assessment of model performance, which we propose as the new standard benchmark for comparing reaction prediction models.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Computer science ; Benchmark (surveying) ; Artificial intelligence ; Machine learning ; Counterintuitive ; Salient ; Interpretation (philosophy) ; Training set ; Synthetic data ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Pierre-Paul De Breuck | Geoffroy Hautier | Gian‐Marco Rignanese
Source: arXiv (Cornell University) (preprint, 2020-04-30)
Field: Materials Science — Materials Chemistry

Machine learning materials properties for small datasets In order to make accurate predictions of material properties, current machine-learning approaches generally require large amounts of data, which are often not available in practice. In this work, a novel all-round framework is presented which relies on a feedforward neural network and the selection of physically-meaningful features. Next to being faster in terms of training time, this approach is shown to outperform current graph-network models on small datasets. In particular, the vibrational entropy at 305 K of crystals is predicted with a mean absolute test error of 0.01 meV/K/atom (four times lower than previous studies). Furthermore, the proposed framework enables the prediction of multiple properties, such as temperature functions, by using joint-transfer learning. Finally, the selection algorithm highlights the most important features and thus helps understanding the underlying physics. Computer science ; Artificial neural network ; Graph ; Machine learning ; Entropy (arrow of time) ; Artificial intelligence ; Feedforward neural network ; Current (fluid) ; Selection (genetic algorithm) ; Transfer entropy ; Feed forward ; Principle of maximum entropy ; Theoretical computer science ; Physics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: David Tatis | Heidy Sierra | Emmanuel Arzuaga
Source:  (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Residual Neural Network Architectures to Improve Prediction Accuracy of Properties of Materials Properties in material composition and crystal structures have been explored by density functional theory (DFT) calculations, using databases such as the Open Quantum Materials Database (OQMD). Databases like these have been used currently for the training of advanced machine learning and deep neural network models, the latter providing higher performance when predicting properties of materials. However, current alternatives have shown a deterioration in accuracy when increasing the number of layers in their architecture (over-fitting problem). As an alternative method to address this problem, we have implemented residual neural network architectures based on Merge and Run Networks, IRNet and UNet to improve performance while relaxing the observed network depth limitation. The evaluation of the proposed architectures include a 9:1 ratio to train and test as well as 10 fold cross validation. In the experiments we found that our proposed architectures based on IRNet and UNet are able to obtain a lower Mean Absolute Error (MAE) than current strategies. The full implementation (Python, Tensorflow and Keras) and the trained networks will be available online for community validation and advancing the state of the art from our findings. Residual ; Merge (version control) ; Artificial neural network ; Computer science ; Python (programming language) ; Artificial intelligence ; Residual neural network ; Machine learning ; Network architecture ; Mean squared error ; Data mining ; Algorithm ; Parallel computing ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Srilok Srinivasan | Rohit Batra | Duan Luo | Troy D. Loeffler | Sukriti Manna | Henry Lik‐Yuen Chan | Liuxiang Yang | Wenge Yang | Jianguo Wen | Pierre Darancet | Subramanian K. R. S. Sankaranarayanan
Source: arXiv (Cornell University) (preprint, 2020-04-19)
Field: Materials Science — Materials Chemistry

Machine Learning the Metastable Phase Diagram of Materials Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram generation involves experimentation to provide an initial estimate of thermodynamically accessible phases, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to inaccessible regions. Such an approach, combined with first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases albeit at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local free energy minimum, that may exhibit desirable properties. Mapping these metastable phases and their thermodynamic behavior is highly desirable but currently lacking. Here, we introduce an automated workflow that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases of a given elemental composition. Using a representative material, carbon, with a vast number of metastable phases without parent in equilibrium, we demonstrate automatic mapping of hundreds of metastable states ranging from near equilibrium to those far-from-equilibrium. Moreover, we incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for construction of metastable phase diagrams. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy are used to validate our metastable phase predictions. Our introduced approach is general and broadly applicable to single and multi-component systems. Metastability ; Phase diagram ; Thermodynamic equilibrium ; Thermodynamic process ; Phase (matter) ; Statistical physics ; Thermodynamic state ; Equilibrium thermodynamics ; Phase transition ; CALPHAD ; Thermodynamics ; Computer science ; Material properties ; Materials science ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Protein Structure and Dynamics
Authors: Sihan Yong | Zhuoyuan Zheng | Pingfeng Wang | Yumeng Li
Source:  (article, 2020-11-16)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Design for Active Cathode Materials Abstract The traditional way of designing materials, including experimental measurement and computational simulation, are not efficient. Machine learning is considered a promising solution for material design in the recent years. By observing from previous data, machine learning finds patterns, learns from the patterns and predict the material properties. In this study, machine learning methods are used for discovering new cathode with better properties, includes crystal system learning and the property prediction. K-Folder cross-validation is used for finding the best training data with a limited dataset, nevertheless increasing the percentage of training data would ultimately result in better performance on prediction. It is found that, random forest gives the highest average accuracy in crystal system classification, meanwhile, extra randomized tree algorithm provides a higher averaged coefficient of determination and lower mean squared error in the regression model predicting electrical properties of cathodes. The random forest algorithm is chosen from a wide range of machine learning algorithms with the implementation of Monte Carlo validation. Based on the feature importance evaluation, oxygen contents are found to have the highest effects in determining capacity gravity and volume change in properties prediction. Random forest ; Machine learning ; Computer science ; Artificial intelligence ; Monte Carlo method ; Active learning (machine learning) ; Range (aeronautics) ; Tree (set theory) ; Extreme learning machine ; Feature (linguistics) ; Mean squared error ; Algorithm ; Data mining ; Mathematics ; Artificial neural network ; Materials science ; Statistics Machine Learning in Materials Science
Authors: Bing Huang | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-12-14)
Field: Materials Science — Materials Chemistry

Ab initio machine learning in chemical compound space Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first principles based virtual sampling of this space, for example in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest sub-sets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an {\em ab initio} view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics. Ab initio ; Chemical space ; Computer science ; Universality (dynamical systems) ; Quantum ; Statistical physics ; Artificial intelligence ; Theoretical computer science ; Quantum mechanics ; Physics ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Pankaj Rajak | Kuang Liu | Aravind Krishnamoorthy | Rajiv K. Kalia | Aiichiro Nakano | Kenichi Nomura | Subodh Tiwari | Priya Vashishta
Source:  (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Neural Network Molecular Dynamics at Scale Neural network molecular dynamics (NNMD) simulations could revolutionize atomistic modeling of materials with quantum-mechanical accuracy at a fraction of computational cost. However, popular NNMD frameworks are generally implemented for a single computing node, and conventional energy-based NN models still suffer from large time-to-solution (T2S), prohibiting the application of NNMD to challenging materials simulations encompassing large spatiotemporal scales. Consequently, no leadership-scale NNMD simulation has thus far been reported. Here, we present a scalable parallel NNMD software (RXMD-NN) based on our scalable reactive molecular dynamics simulation engine named RXMD. RXMD-NN has achieved high scalability up to 786,432 IBM BlueGene/Q cores involving 1.7 billion atoms. Furthermore, we have achieved 4.6-fold reduction of T2S by using a novel network that directly predicts atomic forces from feature vectors. Reduced T2S has for the first time allowed the study of large-scale off-stoichiometry effect in a widely used phase change material, Ge <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Se <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Te <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> , thereby resolving its “firstsharp diffraction peak mystery”. Scalability ; Computer science ; Artificial neural network ; Molecular dynamics ; Scale (ratio) ; IBM ; Computational science ; Artificial intelligence ; Physics ; Computational chemistry ; Database ; Chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Jiakun Zhao | Shibo Cong
Source: 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

Research on Property Prediction of Materials Based on Machine Learning In this paper, three feature selection methods and three machine learning regression models are used to select the best feature subset from the feature set to predict compound energy performance. By comparing the matching performance of different feature selection methods and machine learning models, the experimental results show that Ant Colony Optimization is used for feature selection and the Support Vector Regression model is used for the best prediction effect. The research in this paper can provide references for the prediction of new material properties in the future. Feature selection ; Computer science ; Artificial intelligence ; Machine learning ; Support vector machine ; Feature (linguistics) ; Property (philosophy) ; Matching (statistics) ; Ant colony optimization algorithms ; Selection (genetic algorithm) ; Set (abstract data type) ; Regression ; Data mining ; Pattern recognition (psychology) ; Mathematics ; Statistics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Mineral Processing and Grinding
Authors: Jorge Arturo Hernandez Zeledon | A. Romero | Pengju Ren | Xiaodong Wen | Yongwang Li | James P. Lewis
Source: Journal of Applied Physics (article, 2020-06-03)
Field: Materials Science — Materials Chemistry

The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C7O2H2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (AlxGayInz)2O3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features. Curse of dimensionality ; Feature (linguistics) ; Computer science ; Feature vector ; Artificial intelligence ; Space (punctuation) ; Focus (optics) ; Machine learning ; Dimensionality reduction ; Pattern recognition (psychology) ; Data mining ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Sergei V. Kalinin | Ondrej Dyck | Ayana Ghosh | Bobby G. Sumpter | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2020-10-19)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning Discovery of Chemical Transformation Pathways from Atomically-Resolved Imaging Data We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. The approach encodes the information contained in STEM image sequences using a small number of latent variables, allowing the exploration of chemical transformation pathways by tracing the evolution of atoms in the latent space of the system. The results suggest that atomically resolved STEM data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods in the physical sciences, and will stimulate the development of more sophisticated ways to encode physical constraints in the encoder-decoder architectures, and generative physical laws and causal relationships in the latent space of VAEs. Autoencoder ; Unsupervised learning ; ENCODE ; Artificial intelligence ; Computer science ; Data-driven ; Generative model ; Invariant (physics) ; Physical law ; Transformation (genetics) ; Pattern recognition (psychology) ; Chemical space ; Latent variable ; Generative grammar ; Machine learning ; Deep learning ; Mathematics ; Physics ; Chemistry ; Biology ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Electron Microscopy Techniques and Applications
Authors: Ryan Murdock | Steven Kauwe | Anthony Wang | Taylor D. Sparks
Source: ChemRxiv (preprint, 2020-02-21)
Field: Materials Science — Materials Chemistry

Is Domain Knowledge Necessary for Machine Learning Materials Properties? New methods for describing materials as vectors in order to predict their properties using machine learning are common in the field of material informatics. However, little is known about the comparative efficacy of these methods. This work sets out to make clear which featurization methods should be used across various circumstances. Our findings include, surprisingly, that simple one-hot encoding of elements can be as effective as traditional and new descriptors when using large amounts of data. However, in the absence of large datasets or data that is not fully representative we show that domain knowledge offers advantages in predictive ability. Computer science ; Domain (mathematical analysis) ; Field (mathematics) ; Machine learning ; Domain knowledge ; Simple (philosophy) ; Artificial intelligence ; Encoding (memory) ; Informatics ; Data mining ; Data science ; Mathematics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Sergei V. Kalinin | Ondrej Dyck | Ayana Ghosh | Yongtao Liu | Roger Proksch | Bobby G. Sumpter | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2020-10-19)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data We show that unsupervised machine learning can be used to learn physical and chemical transformation pathways from the observational microscopic data, as demonstrated for atomically resolved images in Scanning Transmission Electron Microscopy (STEM) and ferroelectric domain structures in Piezoresponse Force Microscopy (PFM). To enable this analysis in STEM, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. In PFM, we assumed the uniquely-defined relationship between the measured signal and polarization distribution. With only these postulates, we developed a machine learning method leveraging a rotationally-invariant variational autoencoder (rVAE) that can identify the existing structural units observed within a material. The approach encodes the information contained in image sequences using a small number of latent variables, allowing the exploration of chemical and physical transformation pathways via the latent space of the system. The results suggest that the high-veracity imaging data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods for physical sciences and will stimulate the development of new ways to encode physical constraints in the encoder-decoder architectures, and generative physical laws, topological invariances, and causal relationships in the latent space of VAEs. Autoencoder ; Unsupervised learning ; Artificial intelligence ; Computer science ; Data-driven ; Physical law ; Invariant (physics) ; Generative model ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Generative grammar ; Physics ; Deep learning Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Computational Drug Discovery Methods