jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziyao Li | Shu-Wen Yang | Guojie Song | Lingsheng Cai
Source: arXiv (Cornell University) (preprint, 2021-05-08)
Field: Materials Science — Materials Chemistry

HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning. Whereas incorporating 3D geometry of molecules (i.e. conformations) in their representations seems beneficial, current 3D algorithms are still in infancy. In this paper, we propose a novel molecular representation algorithm which preserves 3D conformations of molecules with a Molecular Hamiltonian Network (HamNet). In HamNet, implicit positions and momentums of atoms in a molecule interact in the Hamiltonian Engine following the discretized Hamiltonian equations. These implicit coordinations are supervised with real conformations with translation- &amp; rotation-invariant losses, and further used as inputs to the Fingerprint Generator, a message-passing neural network. Experiments show that the Hamiltonian Engine can well preserve molecular conformations, and that the fingerprints generated by HamNet achieve state-of-the-art performances on MoleculeNet, a standard molecular machine learning benchmark. Representation (politics) ; Artificial neural network ; Hamiltonian (control theory) ; Computer science ; Mathematics ; Artificial intelligence ; Mathematical optimization ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Marco Werner
Source: ACS Macro Letters (article, 2021-10-11)
Field: Materials Science — Materials Chemistry

Decoding Interaction Patterns from the Chemical Sequence of Polymers Using Neural Networks The relation between chemical sequences and the properties of polymers is considered using artificial neural networks with a low-dimensional bottleneck layer of neurons. These encoder-decoder architectures may compress the input information into a meaningful set of physical variables that describe the correlation between distinct types of data. In this work, neural networks were trained to translate a sequence of hydrophilic and hydrophobic segments into the effective free energy landscape of a copolymer interacting with a lipid membrane. The training data were obtained by the sampling of coarse-grained polymer conformations in a given membrane density field. Neural networks that were split into separate channels have learned to decompose the free energy into independent components that are explainable by known concepts from polymer physics. The semantic information in the hidden layers was employed to predict polymer translocation events through a membrane for a more detailed dynamic model via a transfer learning procedure. The search for minimal translocation times in the compressed chemical space underlined that nontrivial sequence motifs may lead to optimal properties. Sequence (biology) ; Decoding methods ; Polymer ; Artificial neural network ; Materials science ; Biological system ; Computer science ; Artificial intelligence ; Chemistry ; Algorithm ; Composite material ; Biology ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Jinzhe Zeng | Timothy J. Giese | Şölen Ekesan | Darrin M. York
Source: ChemRxiv (preprint, 2021-02-26)
Field: Materials Science — Materials Chemistry

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution We develop a new Deep Potential - Range Correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of 6 non-enzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free energy profiles generated from a target QM model. We perform comparisons using the MNDO/d and DFTB2 semiempirical models because they produce free energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model's initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce 4 different reactions and yielded good agreement with the free energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free energy surfaces and 1D free energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs, but was sped up almost 100-fold when using an NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free energy applications ranging from drug discovery to enzyme design. Range (aeronautics) ; Replica ; Statistical physics ; Quantum ; Energy (signal processing) ; Artificial neural network ; Computer science ; Cutoff ; Chemistry ; Physics ; Artificial intelligence ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: N. Ian Rinehart | Andrew F. Zahrt | Scott E. Denmark
Source: CHIMIA International Journal for Chemistry (article, 2021-08-17)
Field: Materials Science — Materials Chemistry

Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past it is intrinsically limited and inefficient. To address this problem, our laboratory has developed a fully informatics guided workflow to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and optimization of any class of catalyst for any transformation. This approach is mechanistically agnostic, but also serves as a discovery platform to identify high performing catalysts that can be subsequently investigated with physical organic methods to identify the origins of selectivity. Enantioselective synthesis ; Workflow ; Leverage (statistics) ; Computer science ; Artificial intelligence ; Catalysis ; Machine learning ; Chemistry ; Organic chemistry ; Database Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Fanbin Meng | Yan Li | Dunyou Wang
Source: The Journal of Chemical Physics (article, 2021-11-27)
Field: Materials Science — Materials Chemistry

Predicting atomic-level reaction mechanisms for SN2 reactions via machine learning Identifying atomic-level reaction mechanisms is an essential step in chemistry. In this study, we develop a joint-voting model based on three parallel machine-learning algorithms to predict atomic-level and dynamical mechanisms trained with 1700 trajectories. Three predictive experiments are carried out with the training trajectories divided into ten, seven, and five classes. The results indicate that, as the number of trajectories in each class increases from the ten- to five-class model, the five-class model converges the fastest and the prediction success rate increases. The number of trajectories in each experiment to get the predictive models converged is 100, 100, and 70, respectively. The prediction accuracy increases from 88.3% for the ten-class experiment, to 91.0% for the seven-class, and to 92.0% for the five-class. Our study demonstrates that machine learning can also be used to predict elementary dynamical processes of structural evolution along time, that is, atomic-level reaction mechanisms. SN2 reaction ; Class (philosophy) ; Machine learning ; Artificial intelligence ; Computer science ; Algorithm ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ganesan Raman
Source: ChemistrySelect (article, 2021-10-18)
Field: Chemistry — Inorganic Chemistry

Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning Abstract Small‐pore zeolites possess pores that are constructed of eight tetrahedral atoms. ZSM‐43 is a small‐pore zeolite with two‐dimensional eight‐ring channels. The preparation of ZSM‐43 is influenced by the molar concentration of hydroxide ions, choline based organic structure‐directing agents (OSDA) and inorganic structure‐directing agents (ISDA) such as sodium, potassium and cesium. The synthetic conditions yield a range of products such as ZSM‐43, amorphous, UZM‐15, and other zeolites. There is a significant challenge in relating synthetic descriptors to their implications in zeolite phase formation. As a proof‐of‐concept study, we correlated the type of product formed with the gel molar composition in complex ZSM‐43 synthesis using machine learning algorithms. Seven different supervised machine learning algorithms have demonstrated an accuracy of &gt;95 percent and an F1 score of &gt;95 percent. The decision tree algorithm (DT) demonstrates the relationship between what type of phase is produced by what concentration of ISDA and hydroxide ions, as well as the effects of changing these parameters on phase transformation. DT provides structural and physicochemical insights into zeolite chemistry. From the experimental data of ZSM‐43, it is difficult to obtain detailed information regarding the chemistry of all phase formations. However, machine learning algorithms aid in recognizing hidden patterns in the data, facilitating deeper understanding of zeolite chemistry and phase transformations. Zeolite ; ZSM-5 ; Phase (matter) ; Chemistry ; Hydroxide ; Potassium hydroxide ; Aluminosilicate ; Machine learning ; Algorithm ; Materials science ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Computer science ; Catalysis Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Bing Bai | Xu Han | Han Cao | Xinfu He | Changyi Zhang | Wen Yang
Source: Journal of Nuclear Science and Technology (article, 2021-12-02)
Field: Materials Science — Materials Chemistry

Composition optimization of radiation resistance ODS alloy with high strength and ductility for advanced reactor based on machine learning Oxide dispersion strengthened (ODS) alloy is a candidate material for cladding in the fast reactor and the first wall in fusion reactor. Its radiation resistance is still a key problem restricting its application in the field of fast reactor and fusion reactor. By collecting, cleaning and screening the data which includes the composition, process, irradiation conditions, test conditions and yield strength of ODS alloy, the correlation between key components and irradiation hardening of ODS alloy were established by use of deep neural network, and the irradiation hardening prediction of ODS alloy was obtained. The results show that the irradiation hardening decreases significantly when the amount of Y2O3 is about 0.2%. In addition, it is found that when the addition of Y2O3 is greater than 0.3 wt%, the addition of Ti and Al has little effect on the irradiation hardening, while when the addition of Y2O3 is less than 0.2 wt%, the addition of a certain amount of Ti or Al will effectively inhibit the irradiation hardening of ODS alloy. According to the above results, combined with the high strength and ductility ODS alloy design obtained in the previous work, the composition optimization of ODS alloy with both strengthening and toughening and irradiation hardening resistance is further obtained. Ductility (Earth science) ; Alloy ; Radiation resistance ; Materials science ; Metallurgy ; Radiation ; Nuclear engineering ; Creep ; Engineering ; Nuclear physics ; Physics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Thijs Stuyver | Connor W. Coley
Source: arXiv (Cornell University) (preprint, 2021-07-22)
Field: Materials Science — Materials Chemistry

Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability and interpretability There is a perceived dichotomy between structure-based and descriptor-based molecular representations used for predictive chemistry tasks. Here, we study the performance, generalizability, and interpretability of the recently proposed quantum mechanics-augmented graph neural network (ml-QM-GNN) architecture as applied to the prediction of regioselectivity (classification) and of activation energies (regression). In our hybrid QM-augmented model architecture, structure-based representations are first used to predict a set of atom- and bond-level reactivity descriptors derived from density functional theory (DFT) calculations. These estimated reactivity descriptors are combined with the original structure-based representation to make the final reactivity prediction. We demonstrate that our model architecture leads to significant improvements over structure-based GNNs in not only overall accuracy, but also in generalization to unseen compounds. Even when provided training sets of only a couple hundred labeled data points, the ml-QM-GNN outperforms other state-of-the-art model architectures that have been applied to these tasks. Further, because the predictions of our model are grounded in (but not restricted to) QM descriptors, we are able to relate predictions to the conceptual frameworks commonly used to gain qualitative insights into reactivity phenomena. This effort results in a productive synergy between theory and data science, wherein our QM-augmented models provide a data-driven confirmation of previous qualitative analyses, and these analyses in their turn facilitate insights into the decision-making process occurring within ml-QM-GNNs. Interpretability ; Generalizability theory ; Generalization ; Computer science ; Representation (politics) ; Artificial intelligence ; Machine learning ; Artificial neural network ; Reactivity (psychology) ; QM/MM ; Data mining ; Chemistry ; Computational chemistry ; Mathematics ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Maria João Regufe | Vinícius V. Santana | Márcio Marques Martins | Alexandre Ferreira | José M. Loureiro | Alírio E. Rodrigues‬ | Ana M. Ribeiro | Idelfonso B. R. Nogueira
Source: IFAC-PapersOnLine (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Adsorption material composition and process optimization, a systematical approach based on Deep Learning The material screening is a preliminary step while designing an adsorption process. This step is carried out with a limited view of what concerns the material used. It usually focuses only on the materials’ properties and not on their behavior while employed in the separation process. Furthermore, there is a lack of a systematic approach that uses an available materials database to identify the best material in a given process application. This leaves an open issue in the literature, which is getting attention with the advance of computer sciences. Hence, this work addresses this topic by proposing a systematic approach based on Deep Learning and a meta-heuristic optimization for simultaneous adsorbent screening and process optimization. This approach is developed with the main goal to make available a methodology for process optimization with material design that can be run at any time that the process needs to be reconfigured, without exhaustive simulations. As a case study, it is presented the carbon dioxide capture by Electric Swing Adsorption. The results show that the proposed methodology can identify the optimal material composition while providing the optimal process operating conditions. Process (computing) ; Computer science ; Heuristic ; Process engineering ; Adsorption ; Process optimization ; Artificial intelligence ; Engineering ; Chemistry Machine Learning in Materials Science ; Process Optimization and Integration ; Probabilistic and Robust Engineering Design
Authors: Johannes Vater | Florian Gruber | Wulf Grählert | Sebastian Schneider | Alois Knoll
Source: Coatings (article, 2021-11-14)
Field: Chemistry — Analytical Chemistry

Prediction of Coating Adhesion on Laser-Cleaned Metal Surfaces of Battery Cells Using Hyperspectral Imaging and Machine Learning Electric vehicles are shaping the future of the automotive industry. The traction battery is one of the most important components of electric cars. To ensure that the battery operates safely, it is essential to physically and electrically separate the cells facing each other. Coating a cell with varnish helps achieve this goal. Current studies use a destructive method on a sampling basis, the cross-cut test, to investigate the coating quality. In this paper, we present a fast, nondestructive and inline alternative based on hyperspectral imaging and artificial intelligence. Therefore, battery cells are measured with hyperspectral cameras in the visible and near-infrared (VNIR and NIR) parts of the electromagnetic spectrum before and after cleaning then coated and finally subjected to cross-cut test to estimate coating adhesion. During the cross-cut test, the cell coating is destroyed. This work aims to replace cross-cut tests with hyperspectral imaging (HSI) and machine learning to achieve continuous quality control, protect the environment, and save costs. Therefore, machine learning models (logistic regression, random forest, and support vector machines) are used to predict cross-cut test results based on hyperspectral data. We show that it is possible to predict with an accuracy of ~75% whether problems with coating adhesion will occur. Hyperspectral measurements in the near-infrared part of the spectrum yielded the best results. The results show that the method is suitable for automated quality control and process control in battery cell coating, but still needs to be improved to achieve higher accuracies. Hyperspectral imaging ; Coating ; Computer science ; VNIR ; Artificial intelligence ; Support vector machine ; Automotive engineering ; Materials science ; Engineering ; Nanotechnology Spectroscopy and Chemometric Analyses ; Thermography and Photoacoustic Techniques ; Industrial Vision Systems and Defect Detection
Authors: James S. Peerless | Albert L. Kwansa | Branden S. Hawkins | Ralph C. Smith | Yaroslava G. Yingling
Source: Journal of Chemical Information and Modeling (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification and Sensitivity Analysis of Partial Charges on Macroscopic Solvent Properties in Molecular Dynamics Simulations with a Machine Learning Model The molecular dynamics (MD) simulation technique is among the most broadly used computational methods to investigate atomistic phenomena in a variety of chemical and biological systems. One of the most common (and most uncertain) parametrization steps in MD simulations of soft materials is the assignment of partial charges to atoms. Here, we apply uncertainty quantification and sensitivity analysis calculations to assess the uncertainty associated with partial charge assignment in the context of MD simulations of an organic solvent. Our results indicate that the effect of partial charge variance on bulk properties, such as solubility parameters, diffusivity, dipole moment, and density, measured from MD simulations is significant; however, measured properties are observed to be less sensitive to partial charges of less accessible (or buried) atoms. Diffusivity, for example, exhibits a global sensitivity of up to 22 × 10<sup>-5</sup> cm<sup>2</sup>/s per electron charge on some acetonitrile atoms. We then demonstrate that machine learning techniques, such as Gaussian process regression (GPR), can be effective and rapid tools for uncertainty quantification of MD simulations. We show that the formulation and application of an efficient GPR surrogate model for the prediction of responses effectively reduces the computational time of additional sample points from hours to milliseconds. This study provides a much-needed context for the effect that partial charge uncertainty has on MD-derived material properties to illustrate the benefit of considering partial charges as distributions rather than point-values. To aid in this treatment, this work then demonstrates methods for rapid characterization of resulting sensitivity in MD simulations. Partial charge ; Context (archaeology) ; Molecular dynamics ; Sensitivity (control systems) ; Statistical physics ; Uncertainty quantification ; Kriging ; Partial least squares regression ; Dipole ; Chemistry ; Charge (physics) ; Thermal diffusivity ; Parametrization (atmospheric modeling) ; Gaussian ; Moment (physics) ; Biological system ; Chemical physics ; Computational chemistry ; Thermodynamics ; Physics ; Computer science ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics