jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Baochuan Wang | Tangjie Gu | Yijun Lu | Bo Yang
Source: Molecular Catalysis (article, 2020-10-24)
Field: Materials Science — Materials Chemistry

Prediction of energies for reaction intermediates and transition states on catalyst surfaces using graph-based machine learning models Artificial neural network ; Graph ; Catalysis ; Transition state ; Potential energy surface ; Computer science ; Chemistry ; Computational chemistry ; Biological system ; Molecule ; Artificial intelligence ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Kobi Felton | Jan G. Rittig | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2020-09-11)
Field: Materials Science — Materials Chemistry

Summit: Benchmarking Machine Learning Methods for Reaction Optimisation &lt;p&gt;In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past experimental data, but knowing which machine learning strategy to apply in a particular case is still difficult. Here, we develop chemically-motivated virtual benchmarks for reaction optimisation and compare several strategies on these benchmarks. The benchmarks and strategies are encompassed in an open source framework named Summit. The results of our tests show that Bayesian optimisation strategies perform very well across the types of problems faced in chemical reaction optimisation, while many strategies commonly used in reaction optimisation fail&lt;/p&gt; &lt;p&gt;to find optimal solutions.&lt;/p&gt; Benchmarking ; Summit ; Intuition ; Computer science ; Machine learning ; Bayesian probability ; Artificial intelligence ; Economics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jun Zhang | Yao-Kun Lei | Yi Yang | Yi Qin Gao
Source: The Journal of Chemical Physics (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

Deep learning for variational multiscale molecular modeling Molecular simulations are widely applied in the study of chemical and bio-physical problems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper, we propose a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their crosstalks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations through deep generative learning; in turn, FG simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method defines a variational and adaptive training objective, which allows end-to-end training of parametric molecular models using deep neural networks. Through multiple experiments, we show that our method is efficient and flexible and performs well on challenging chemical and bio-molecular systems. Computer science ; Artificial intelligence ; Deep learning ; Reinforcement learning ; Artificial neural network ; Molecular dynamics ; Generative grammar ; Machine learning ; Deep neural networks ; Parametric statistics ; Chemistry ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Liwang Sun | Hong Li | Xiaoqin Zhang | He-Bei Gao | Meng‐Bo Luo
Source: Chinese Journal of Polymer Science (article, 2020-06-30)
Field: Materials Science — Materials Chemistry

Identifying Conformation States of Polymer through Unsupervised Machine Learning Autoencoder ; Artificial neural network ; Unsupervised learning ; Artificial intelligence ; Feature (linguistics) ; Computer science ; Cluster (spacecraft) ; Self-organizing map ; Polymer ; Code (set theory) ; Surface (topology) ; Boltzmann machine ; Pattern recognition (psychology) ; Materials science ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Theoretical and Computational Physics
Authors: Liyuan Huang | Ling Chen
Source: Journal of Applied Physics (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Practicing deep learning in materials science: An evaluation for predicting the formation energies Deep learning in recent years has entered the chemistry and materials research arsenal with many successful accomplishments in tasks considered to be intractable using traditional means. However, the widespread application of this data-driven technology is still challenged by the requirement of large training data, poor model interpretability, and hard-to-detect errors that undermine the soundness of conclusion. Here, we performed a systematic study for the modeling of the formation energies of inorganic compounds using deep learning. Our results proved the advantage of deep learning methods over several non-deep learning methods in this specific task and demonstrated the abstraction of knowledge using deep learning, which was a unique ability compared to non-deep learning methods. Several aspects that critically affected the conclusion were also highlighted, including the importance to rigorously compare model performance with the same dataset, the design of input representation, and the careful selection of model architecture. Findings from the current study demonstrate the capabilities of deep learning solving complicated problems in materials research and serve as new guidelines for future practicing of deep learning in this field. Deep learning ; Interpretability ; Artificial intelligence ; Computer science ; Machine learning ; Data science ; Field (mathematics) ; Abstraction ; Task (project management) ; Systems engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Shimin Su | Yuyao Yang | Hanlin Gan | Shuangjia Zheng | Feng Long Gu | Chao Zhao | Jun Xu
Source: Journal of Chemical Information and Modeling (article, 2020-02-03)
Field: Chemistry — Organic Chemistry

Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism The copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC) reaction, a major click chemistry reaction, is widely employed in drug discovery and chemical biology. However, the success rate of the CuAAC reaction is not satisfactory as expected, and in order to improve its performance, we developed a recurrent neural network (RNN) model to predict its feasibility. First, we designed and synthesized a structurally diverse library of 700 compounds with the CuAAC reaction to obtain experimental data. Then, using reaction SMILES as input, we generated a bidirectional long-short-term memory with a self-attention mechanism (BiLSTM-SA) model. Our best prediction model has total accuracy of 80%. With the self-attention mechanism, adverse substructures responsible for negative reactions were recognized and derived as quantitative descriptors. Density functional theory investigations were conducted to provide evidence for the correlation between bromo-α-C hybrid types and the success rate of the reaction. Quantitative descriptors combined with RDKit descriptors were fed to three machine learning models, a support vector machine, random forest, and logistic regression, and resulted in improved performance. The BiLSTM-SA model for predicting the feasibility of the CuAAC reaction is superior to other conventional learning methods and advances heuristic chemical rules. Cycloaddition ; Alkyne ; Mechanism (biology) ; Azide ; Artificial neural network ; Computer science ; Support vector machine ; Click chemistry ; Artificial intelligence ; Reaction mechanism ; Random forest ; Machine learning ; Molecular descriptor ; Biological system ; Chemistry ; Combinatorial chemistry ; Catalysis ; Quantitative structure–activity relationship ; Organic chemistry ; Biology Click Chemistry and Applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Ritaban Dutta | D. Renshaw | Cherry Chen | Daniel Liang
Source: Array (article, 2020-08-08)
Field: Materials Science — Polymers and Plastics

Machine learning based approach for shape memory polymer behavioural characterization In this article we aim to combine video data analysis techniques, scalable machine learning, and Shape memory polymers (SMPs) materials to develop a model-based architecture for the advancement of rapid characterization of a novel material. Although artificially intelligent machines, e.g. soft robotics systems, with high flexibility have conquered the production line and other controlled, predictable environments, their use in complex real-world scenarios has to date remained limited. Newly discovered and experimented SMPs are increasingly being used for application solutions in automotive, aerospace, construction and commercial field. But being a nascent field there is little knowledge on the shape recovery behaviour of laminates with a SMP film and there are only methods reported in literature for quantifying the material behaviour. Through various experimental data gathering and predictive modelling it was established that proposed methodology can rapidly characterize novel materials. The proposed modelling workflow showed accuracy of 90% with 92% sensitivity and 94% specificity while predicting recovery behaviour of SMP body, showcasing high potential for data driven rapid characterisation of shape memory materials. Workflow ; Flexibility (engineering) ; Field (mathematics) ; Computer science ; Shape-memory polymer ; Scalability ; Artificial intelligence ; Automotive industry ; Soft robotics ; Characterization (materials science) ; Robotics ; Machine learning ; Aerospace ; Robot ; Shape-memory alloy ; Engineering ; Aerospace engineering ; Materials science ; Nanotechnology ; Database Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Mitchell C. Groenenboom | Rachel M. Anderson | James A. Wollmershauser | Derek J. Horton | Steven A. Policastro | John A. Keith
Source: The Journal of Physical Chemistry C (article, 2020-06-18)
Field: Materials Science — Materials Chemistry

Combined Neural Network Potential and Density Functional Theory Study of TiAl<sub>2</sub>O<sub>5</sub>Surface Morphology and Oxygen Reduction Reaction Overpotentials Titanium alloys, such as Ti-6Al-4V, are used in a variety of applications due to their high strength-to-weight ratio and corrosion resistance. Despite resisting corrosion, Ti-6Al-4V facilitates the galvanic corrosion of less noble metals when they are in contact. Atmospheric galvanic corrosion is limited by the rate of cathodic reduction reactions, such as the oxygen reduction reaction (ORR). To better understand the factors that make a material a poor ORR catalyst in these conditions, we use an in silico procedure to predict how the ORR overpotentials of TiAl2O5 (a possible oxide present on the Ti-6Al-4V surface) surface sites are impacted by surface morphology and the presence of metal dopants. We trained Behler–Parrinello neural networks to reproduce the Kohn–Sham density functional theory energy and forces of TiAl2O5 structures and used these neural networks to create a variety of defective and amorphous surface models. We calculated and compared the ORR overpotentials of these TiAl2O5 surfaces with density functional theory. Our calculations show that ORR activity can be modulated by the presence of metal dopants in the oxide. Some dopants are consistently poor ORR catalyst sites (Si4+, Ga3+, and Sn4+), while others depend on the surface and the magnitude of solvation (Co2+, Nb5+, and Mn2+). This modulation may reduce the ORR activity on the oxide surface and therefore improve the corrosion resistance of a material in atmospheric conditions. Corrosion ; Density functional theory ; Dopant ; Materials science ; Oxide ; Galvanic corrosion ; Catalysis ; Galvanic cell ; Metal ; Metallurgy ; Chemical engineering ; Inorganic chemistry ; Chemistry ; Doping ; Computational chemistry ; Organic chemistry ; Optoelectronics Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Victor Venturi | Holden L Parks | Zeeshan Ahmad | Venkatasubramanian Viswanathan
Source: Machine Learning Science and Technology (article, 2020-06-25)
Field: Materials Science — Materials Chemistry

Machine learning enabled discovery of application dependent design principles for two-dimensional materials Abstract The unique electronic and mechanical properties of two-dimensional (2D) materials make them promising next-generation candidates for a variety of applications. Large-scale searches for high-performing 2D materials are limited to calculating descriptors with computationally demanding first-principles density functional theory. In this work, we alleviate this issue by extending and generalizing crystal graph convolutional neural networks to systems with planar periodicity and train an ensemble of models to predict thermodynamic, mechanical and electronic properties. We carry out a screening of nearly 45,000 structures for two separate applications: mechanical strength and photovoltaics. By collecting statistics of the screened candidates, we investigate structural and compositional design principles that impact the properties of the structures surveyed. Our approach recovers some well-accepted design rules: hybrid organic-inorganic perovskites with lead and tin tend to be good candidates for solar cell applications and titanium based MXenes usually have high stiffness coefficients. Interestingly, other members of the group 4 elements also contribute to increasing the mechanical strength of MXenes. For all-inorganic perovskites, we discover some compositions that have not been deeply studied in the field of photovoltaics and thus open up paths for further investigation. We open-source the code-base to spur further development in this space. MXenes ; Photovoltaics ; Stiffness ; Convolutional neural network ; Field (mathematics) ; Design elements and principles ; Variety (cybernetics) ; Mechanical strength MXene and MAX Phase Materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Natalie S. Eyke | William H. Green | Klavs F. Jensen
Source: ChemRxiv (preprint, 2020-06-18)
Field: Materials Science — Materials Chemistry

Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening High-throughput reaction screening has emerged as a useful means of rapidly identifying the influence of key reaction variables on reaction outcomes. We show that active machine learning can further this objective by eliminating dependence on complete screens through iterative selection of maximally informative experiments from the subset of all possible experiments in the domain. To demonstrate our approach, we conduct retrospective analyses of the preexisting results of high-throughput reaction screening experiments. We compare the test set errors of models trained on actively-selected reactions to models trained on reactions selected at random from the same domain. We find that the degree to which models trained on actively-selected data outperform models trained on randomly-selected data depends on the domain being modeled, with it being possible to achieve very low test set errors when the dataset is heavily skewed in favor of low- or zero-yielding reactions. Our results confirm that the active learning algorithm is a useful experiment planning tool that can change the reaction screening paradigm, by allowing discovery and process chemists to focus their reaction screening efforts on the generation of a small amount of high-quality data. Computer science ; Machine learning ; Set (abstract data type) ; Throughput ; Domain (mathematical analysis) ; Artificial intelligence ; Focus (optics) ; Test set ; Process (computing) ; Mathematics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Software Engineering Research
Authors: Julia Fischer | Michelle A. Hunter | Marlies Hankel | Debra J. Searles | Amanda Parker | Amanda S. Barnard
Source: ChemCatChem (article, 2020-06-02)
Field: Materials Science — Materials Chemistry

Accurate prediction of binding energies for two‐dimensional catalytic materials using machine learning Abstract The binding energy of small molecules on two‐dimensional (2D) single atom catalysts influences their reaction efficiency and suitability for different applications. In this study, the binding energy on single metal atoms to N‐doped graphene defects was predicted using random forest regression based on approximately 1700 previously generated density functional theory simulations of catalytic reactions. Three different structural feature groups containing hundreds of individual structural features were created and used to characterise the active sites. This approach was found to be accurate and reliable using either fully relaxed output structures or pre‐simulation input structures, with coefficients of determination of =0.952 and =0.865, respectively. The ability to predict optimal 2D‐catalysts before undertaking expensive quantum chemical calculations is an attractive basis for future research, and could be extended to other 2D‐materials. Catalysis ; Density functional theory ; Binding energy ; Atom (system on chip) ; Graphene ; Molecule ; Quantum chemical ; Materials science ; Computational chemistry ; Feature (linguistics) ; Chemical physics ; Nanotechnology ; Chemistry ; Biological system ; Computer science ; Physics ; Atomic physics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Xuebang Wu | Yuxuan Wang | Kan-ni He | Xiangyan Li | Wei Liu | Yange Zhang | Yichun Xu | C.S. Liu
Source: Materials (article, 2020-01-01)
Field: Materials Science — Metals and Alloys

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to uncover the physical mechanisms and design novel materials with enhanced properties. In this work, to accurately predict and uncover the key features in determining the strengthening energies, three machine learning methods were used to model and predict strengthening energies of solutes in different metallic GBs. In addition, 142 strengthening energies from previous density functional theory calculations served as our dataset to train three machine learning models: support vector machine (SVM) with linear kernel, SVM with radial basis function (RBF) kernel, and artificial neural network (ANN). Considering both the bond-breaking effect and atomic size effect, the nonlinear kernel based SVR model was found to perform the best with a correlation of <i>r</i><sup>2</sup> ~ 0.889. The size effect feature shows a significant improvement to prediction performance with respect to using bond-breaking effect only. Moreover, the mean impact value analysis was conducted to quantitatively explore the relative significance of each input feature for improving the effective prediction. Support vector machine ; Machine learning ; Artificial neural network ; Artificial intelligence ; Embrittlement ; Materials science ; Kernel (algebra) ; Feature (linguistics) ; Nonlinear system ; Radial basis function kernel ; Grain boundary ; Computer science ; Algorithm ; Kernel method ; Mathematics ; Metallurgy ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Cristiano Fragassa | Matej Babič | Elizaldo Domingues dos Santos
Source: Tribology in Industry (article, 2020-03-15)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches to Predict the Hardness of Cast Iron The accurate prediction of the mechanical properties of foundry alloys is a rather complex task given the substantial variability of metallurgical conditions that can be created during casting even in the presence of minimal variations in the constituents and in the process parameters. In this study an application of different intelligent methods of classification, based on the machine learning, to the estimation of the hardness of a traditional spheroidal cast iron and of a less common compact graphite cast iron is proposed. Microstructures are used as inputs to train the neural networks, while hardness is obtained as outputs. As general result, it is possible to admit that ‘light’ open source self-learning algorithms, combined with databases consisting of about 20-30 measures are already able to predict hardness properties with errors below 15 %. Cast iron ; Artificial intelligence ; Computer science ; Machine learning ; Metallurgy ; Materials science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: Guanhua Zhang | Jianmei Yuan | Yuliang Mao | Yunqing Huang
Source: Computational Materials Science (article, 2020-08-18)
Field: Materials Science — Materials Chemistry

Two-dimensional Janus material MoS2(1-x)Se2x (0 &lt; x &lt; 1) for photovoltaic applications: A machine learning and density functional study Density functional theory ; Mean squared error ; Pearson product-moment correlation coefficient ; Correlation coefficient ; Linear regression ; Janus ; Materials science ; Absorption (acoustics) ; Machine learning ; Artificial intelligence ; Mathematics ; Algorithm ; Chemistry ; Computer science ; Optics ; Statistics ; Physics ; Nanotechnology ; Computational chemistry 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques