jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Reinhard J. Maurer
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery Atomistic simulation based on quantum mechanics (QM) is currently being revolutionized by machine-learning (ML) methods. Many existing approaches use ML to predict molecular properties from quantum chemical calculations. This has enabled molecular property prediction within vast chemical compound spaces and the high-dimensional parametrization of energy landscapes for the efficient molecular simulation of measurable observables. However, as all properties derive from the QM wave function, an ML model that is able to predict the wave function also has the potential to predict all other molecular properties. In this talk, I will explore ML approaches that directly represent wave functions and QM Hamiltonians and their derivatives for developing methods that use ML and QM in synergy. [1] Using examples from molecular dynamics [1] and heterogeneous catalysis, [2,3] I will discuss the challenges associated with encoding physical symmetries and invariance properties into deep learning models. Upon overcoming these challenges, integrated ML-QM methods offer the combined benefits of big-data-driven parametrization and first-principles-based methods. I will discuss several opportunities associated with building ML-augmented quantum chemical methods, including Inverse Chemical Design based on ML-predicted wave functions and the development of efficient and accurate semi-empirical methods to study hybrid metal-organic materials. [4] [1] KT SchÃ¼tt, M Gastegger, A Tkatchenko, K-R MÃ¼ller &amp; RJ Maurer, Nature Communications 10, 5024 (2019). [2] Y Zhang, RJ Maurer, and B Jiang, J. Phys. Chem. C 124, 186-195 (2020); [3] Y Zhang, RJ Maurer, H Guo, and B Jiang, Chem. Sci. 10, 1089-1097 (2019). [4] M Gastegger, A McSloy, M Luya, KT SchÃ¼tt, RJ Maurer, J. Chem. Phys. 153, 044123 (2020). Parametrization (atmospheric modeling) ; Wave function ; Observable ; Function (biology) ; Quantum chemical ; Quantum ; Quantum chemistry ; Molecular dynamics ; Deep learning ; Computer science ; Statistical physics ; Chemistry ; Quantum mechanics ; Physics ; Computational chemistry ; Molecule ; Artificial intelligence Machine Learning in Materials Science
Authors: Edirisuriya M. Dilanga Siriwardane | Steph-Yves Louis | Rajendra P. Joshi | Neeraj Kumar | Jianjun Hu
Source: ChemRxiv (preprint, 2021-08-02)
Field: Materials Science — Materials Chemistry

ACCURATE PREDICTION OF VOLTAGE OF BATTERY ELECTRODE MATERIALS USING ATTENTION BASED GRAPH NEURAL NETWORKS Performing first principle calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use pre-defined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed method, which combines both atomic composition and atomic coordinates in 3D-space, improves the accuracy in voltage prediction by 17% when compared to composition based ML models. The first model directly learns the chemical reaction of electrodes and metal-ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (Eform) to compute their average voltage. Our models demonstrates improved accuracy in transferability from our subset of learned metal-ions to other metal-ions. Transferability ; Voltage ; Convolutional neural network ; Electrode ; Computer science ; Graph ; Artificial neural network ; Ion ; Battery (electricity) ; Chemical space ; Artificial intelligence ; Algorithm ; Biological system ; Machine learning ; Materials science ; Theoretical computer science ; Chemistry ; Electrical engineering ; Physics ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies
Authors: Santiago Vargas | Matthew R. Hennefarth | Zhihao Liu | Anastassia N. Alexandrova
Source: ChemRxiv (preprint, 2021-06-03)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict Diels-Alder Reaction Barriers from the Reactant State Electron Density Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry, that countless variants, with or without catalysts, have been studied and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous, and can be used effectively for prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels-Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels-Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels-Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes in kcat. We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments. Alder ; Diels–Alder reaction ; State (computer science) ; Electron ; Chemistry ; Computational chemistry ; Chemical physics ; Computer science ; Organic chemistry ; Ecology ; Catalysis ; Physics ; Biology ; Nuclear physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Natural Products and Biosynthesis
Authors: Sékou-Oumar Kaba | Benjamin Groleau-Paré | Marc-Antoine Gauthier | A.–M. S. Tremblay | Simon Verret | C. Gauvin-Ndiaye
Source: arXiv (Cornell University) (preprint, 2021-11-29)
Field: Materials Science — Materials Chemistry

Prediction of Large Magnetic Moment Materials With Graph Neural Networks and Random Forests Magnetic materials are crucial components of many technologies that could drive the ecological transition, including electric motors, wind turbine generators and magnetic refrigeration systems. Discovering materials with large magnetic moments is therefore an increasing priority. Here, using state-of-the-art machine learning methods, we scan the Inorganic Crystal Structure Database (ICSD) of hundreds of thousands of existing materials to find those that are ferromagnetic and have large magnetic moments. Crystal graph convolutional neural networks (CGCNN), materials graph network (MEGNet) and random forests are trained on the Materials Project database that contains the results of high-throughput DFT predictions. For random forests, we use a stochastic method to select nearly one hundred relevant descriptors based on chemical composition and crystal structure. This gives results that are comparable to those of neural networks. The comparison between these different machine learning approaches gives an estimate of the errors for our predictions on the ICSD database. Validating our final predictions by comparisons with available experimental data, we found 15 materials that are likely to have large magnetic moments and have not been yet studied experimentally. Random forest ; Computer science ; Artificial neural network ; Magnetic moment ; Artificial intelligence ; Graph ; Convolutional neural network ; Machine learning ; Ferromagnetism ; Random graph ; Statistical physics ; Condensed matter physics ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Mingjian Wen | Samuel M. Blau | Xiaowei Xie | Shyam Dwaraknath | Kristin A. Persson
Source: ChemRxiv (preprint, 2021-11-23)
Field: Materials Science — Materials Chemistry

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem—classifying reactions into distinct families—and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets. In addition to reaction classification, the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels. Computer science ; Artificial intelligence ; Leverage (statistics) ; Overfitting ; Machine learning ; Chemical space ; Graph ; Pattern recognition (psychology) ; Artificial neural network ; Chemistry ; Theoretical computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Changhae Andrew Kim | Nathan D. Ricke | Troy Van Voorhis
Source: ChemRxiv (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

Machine Learning Dynamic Correlation in Chemical Kinetics The kinetics of surface reactions are often described using a lattice model. Since it is expensive to propagate the configuration probabilities of the entire lattice, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation (CME). Unfortunately, simple closures, such as the mean-field (MF) and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics, using the lattice Lotka-Volterra model (LLVM) as a model system. We trained feed-forward neural networks (FFNNs) to estimate the three-site occupation probabilities, using kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the machine learning moment closure (MLMC) gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pre-trained closures that would enable researchers to extract useful insight at a fraction of the computational cost. Moment closure ; Lattice (music) ; Statistical physics ; Monte Carlo method ; Moment (physics) ; Closure (psychology) ; Computer science ; Artificial neural network ; Machine learning ; Mathematics ; Artificial intelligence ; Algorithm ; Physics ; Thermodynamics ; Classical mechanics ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: JCS Kadupitiya | Nasim Anousheh | Vikram Jadhao
Source: arXiv (Cornell University) (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

Designing Machine Learning Surrogates using Outputs of Molecular Dynamics Simulations as Soft Labels Molecular dynamics simulations are powerful tools to extract the microscopic mechanisms characterizing the properties of soft materials. We recently introduced machine learning surrogates for molecular dynamics simulations of soft materials and demonstrated that artificial neural network based regression models can successfully predict the relationships between the input material attributes and the simulation outputs. Here, we show that statistical uncertainties associated with the outputs of molecular dynamics simulations can be utilized to train artificial neural networks and design machine learning surrogates with higher accuracy and generalizability. We design soft labels for the simulation outputs by incorporating the uncertainties in the estimated average output quantities, and introduce a modified loss function that leverages these soft labels during training to significantly reduce the surrogate prediction error for input systems in the unseen test data. The approach is illustrated with the design of a surrogate for molecular dynamics simulations of confined electrolytes to predict the complex relationship between the input electrolyte attributes and the output ionic structure. The surrogate predictions for the ionic density profiles show excellent agreement with the ground truth results produced using molecular dynamics simulations. The high accuracy and small inference times associated with the surrogate predictions provide quick access to quantities derived using the number density profiles and facilitate rapid sensitivity analysis. Computer science ; Molecular dynamics ; Generalizability theory ; Artificial neural network ; Artificial intelligence ; Inference ; Machine learning ; Sensitivity (control systems) ; Surrogate model ; Biological system ; Mathematics ; Chemistry ; Engineering ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrostatics and Colloid Interactions
Authors: Kazuya Hiraide | Kenta Hirayama | Katsuhiro Endo | Mayu Muramatsu
Source: The Proceedings of Conference of Kanto Branch (article, 2021-01-01)
Field: Materials Science — General Materials Science

Development of a Deep Learning for an Inverse Design of Phase Separation Structure in Polymer Alloy By using some machine learning methods, in this study, we develop a framework that deals with forward analysis to predict a property from a polymer alloy’s phase separation structure and inverse design to generate the structure from the property. We consider only Young’s modulus as the property in this study. In our framework, the forward analysis is performed by using a convolutional neural network (CNN) and the inverse design is realized by a random search toward a model combining a generative adversarial network (GAN) and a CNN. This framework is applicable to various properties at a low computational cost, and the latent variables belonging to the GAN are useful for feature extraction. Inverse ; Computer science ; Convolutional neural network ; Generative grammar ; Property (philosophy) ; Phase (matter) ; Feature (linguistics) ; Deep learning ; Key (lock) ; Artificial intelligence ; Artificial neural network ; Algorithm ; Mathematical optimization ; Mathematics ; Physics ; Geometry Material Properties and Applications
Authors: Noritaka Usami
Source: ECS Transactions (article, 2021-05-07)
Field: Materials Science — Materials Chemistry

(Invited) Application of Machine Learning for High-Performance Multicrystalline Materials We report on our recent attempt to pioneer “multicrystalline informatics” through collaboration of experiments, theory, computation, and machine learning to establish universal guideline how we can obtain high-performance multicrystalline materials. We employ silicon as a model material, and develop various useful machine learning models. One example is a neural network to predict distribution of crystal orientations in a large-area sample from multiple optical images. Transfer learning of pre-trained image classifier could predict spatial distribution of probability of dislocations generation from photoluminescence images. Extracted regions with high probability of dislocations generation could be characterized by multiscale experiments as well as computation using artificial-neural-network interatomic potential to disclose physics behind. The obtained knowledge could be useful for process development of high-performance multicrystalline materials. Artificial intelligence ; Artificial neural network ; Computation ; Materials science ; Machine learning ; Silicon ; Computer science ; Materials informatics ; Classifier (UML) ; Algorithm ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Jun Jiang
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

AI3SD Video: The Application of Machine Learning in Molecular Spectroscopy Study Optical-spectroscopy provides powerful toolkits to decipher molecular structures and their configuration evolutions. However, the theoretical analysis of spectroscopic signals and connecting them with structural detail is a challenging task. Moreover, the intrinsic complexity of spectroscopic signals of molecular systems makes it difficult to correlate spectral characteristics with the underlying molecular structure and dynamics. Herein, we have developed data-driven machine learning (ML) protocols that can predict infrared (IR), ultraviolet/visible (UV/Vis) and Raman spectra of molecule systems with 3 to 5 orders of magnitude reduced computation cost compared to direct quantum chemistry calculations. A convolutional neural network (CNN) model was trained and tested on a dataset consisting 87993 spectra computed from protein peptide segments with Î±-helical, Î²-sheet, and other typical secondary structures. The secondary structure classification accuracy reached near 100% and over 98.7% on spectra sets of new segments extracted from the same and homologous proteins, respectively. Importantly, we demonstrate the ML protocol to realize cost-effective relations between spectra, structure, and chemical properties, i.e. spectra determination/prediction from structural information, and configuration or chemical properties determination/recognition from spectroscopic signals.<br><br>1. S. Ye, K. Zhong, J.X. Zhang, W. Hu, J. Hirst, G.Z. Zhang, S. Mukamel, J. Jiang*, A Machine Learning Protocol for Predicting Protein Infrared Spectra, J. Am. Chem. Soc. 142 (2020) 19071-19077.<br>2. X.J. Wang, S. Ye, W. Hu, E. Sharman, R. Liu, Y. Liu, Y. Luo, J. Jiang*, Electric Dipole Descriptor for Machine Learning Prediction of Catalyst Surface-Molecular Adsorbate Interactions, J. Am. Chem. Soc. 142 (2020) 7737-7743.<br>3. S. Ye, W. Hu, X. Li, J.X. Zhang, K. Zhong, G.Z. Zhang, Y. Luo, S. Mukamel*, J. Jiang*, A Neural Network Protocol for Electronic excitations of N-Methylacetamide, Proc Natl Acad Sci USA. 116 (2019) 11612-11617.<br>4. W. Hu, S. Ye, Y.J Zhang, T.D. Li, G.Z. Zhang, Y. Luo, S. Mukamel, J. Jiang*, Machine Learning Protocol for Surface-Enhanced Raman Spectroscopy, J. Phys. Chem. Lett. 10 (2019) 6026-6031. Chemistry ; Raman spectroscopy ; Spectroscopy ; Spectral line ; Artificial intelligence ; Infrared spectroscopy ; Artificial neural network ; Computational chemistry ; Machine learning ; Computer science ; Physics ; Optics ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Water Quality Monitoring and Analysis
Authors: Kenneth Atz | Clemens Isert | Markus N. A. Böcker | José Jiménez-Luna | Gisbert Schneider
Source: ChemRxiv (preprint, 2021-09-02)
Field: Materials Science — Materials Chemistry

Open-source Δ-quantum machine learning for medicinal chemistry Certain molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like compounds currently makes large-scale applications of quantum chemistry challenging. In order to mitigate this problem, we developed DelFTa, an open-source toolbox for predicting small-molecule electronic properties at the density functional (DFT) level of theory, using the Δ-machine learning principle. DelFTa employs state-of-the-art E(3)-equivariant graph neural networks that were trained on the QMugs dataset of QM properties. It provides access to a wide array of quantum observables by predicting approximations to ωB97X-D/def2-SVP values from a GFN2-xTB semiempirical baseline. Δ-learning with DelFTa was shown to outperform direct DFT learning for most of the considered QM endpoints. The software is provided as open-source code with fully-documented command-line and Python APIs. Computer science ; Toolbox ; Python (programming language) ; Density functional theory ; Quantum ; Open source ; Quantum chemistry ; Source code ; Observable ; Artificial intelligence ; Software ; Computational science ; Theoretical computer science ; Machine learning ; Computational chemistry ; Chemistry ; Molecule ; Quantum mechanics ; Programming language ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: So Takamoto | Chikashi Shinagawa | D. Motoki | Kosuke Nakago | Wenwen Li | Iori Kurata | Taku Watanabe | Yoshihiro Yayama | Hiroki Iriguchi | Yusuke Asano | Tasuku Onodera | Takafumi Ishii | Takao Kudo | Hideki Ono | Ryohto Sawada | Ryuichiro Ishitani | Marc Ong | Taiki Yamaguchi | Toshiki Kataoka | Akihide Hayashi | Takeshi Ibuka
Source: arXiv (Cornell University) (preprint, 2021-06-28)
Field: Materials Science — Materials Chemistry

PFP: Universal Neural Network Potential for Material Discovery Computational material discovery is under intense research because of its power to explore the vast space of chemical systems. Neural network potentials (NNPs) and their corresponding datasets have been shown to be particularly effective in performing atomistic simulations for such purposes. However, existing NNPs are generally designed for rather narrow target materials and domains, making them unsuitable for broader applications in material discovery. To overcome this issue, we have developed a universal NNP named PFP capable of handling any combination of 45 elements under a wide range of conditions. Particular emphasis is placed on the generation of training datasets, which include a diverse set of virtual structures to attain the universality of the NNP. In addition, the NNP architecture has been designed to be able to train this diverse dataset. We demonstrated the applicability of PFP, in selected domains: lithium diffusion in LiFeSO${}_4$F, molecular adsorption in metal-organic frameworks, order-disorder transition of Cu-Au alloys, and material discovery for a Fischer-Tropsch catalyst. These examples showcase the power of PFP, and this technology provides a highly useful tool for material discovery. Chemical space ; Universality (dynamical systems) ; Computer science ; Virtual screening ; Scientific discovery ; Artificial neural network ; Drug discovery ; Nanotechnology ; Materials science ; Artificial intelligence ; Chemistry ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography