jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sana LOUSSAIEF | Yerlan Akhmetov | Melanie Groisil | Daisuke Takahashi | Masaki Kuhara | Yoshiki Isobe | Peter Mas
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2021-09-21)
Field: Materials Science — Materials Chemistry

Three-Way Catalyst Modelling Using Physical and Machine Learning Methods for Engine Control Design Purposes &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Gasoline engine control strategies ensure a combustion control around stoichiometry. That is because the three-way catalytic converter allows CO and HC oxidation under lean operating conditions while ensuring NO&lt;sub&gt;x&lt;/sub&gt; reduction for rich mixtures.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;In case of engine malfunction, the controller must adapt to compensate for potential torque loss and other critical attributes, potentially leading to significant deviation of the fuel-air mixture richness from stoichiometry and higher emission levels. Therefore, during development of the engine fault diagnostics, the impact on the pollutant emissions must be considered.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;In this paper, a model-based development process is proposed. It is based on system simulation modelling techniques, where a complete exhaust line is represented in order to predict tail-pipe emissions under stoichiometric, lean and rich conditions, for engine control design purposes. Two different modelling approaches are applied and evaluated in this paper. First, a physics-based modelling approach where thermal and chemical aspects of the pollutant conversion phenomena are considered. In this study, the focus is on the chemical reaction’s selection and kinetic parameters calibration. Second, a machine learning approach based on neural networks to represent the pollutant conversion process and monolith thermal dynamics is employed. Our main contribution to this method is the selection of an optimal neural network architecture and application of a convenient training process.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The study successfully resulted in a validated gasoline exhaust line model for both approaches. Simulation results were validated using experimental data from driving cycle tests under stoichiometric, 10% lean and 10% rich operating conditions; the average error over a large set of evaluation cycles is below 11% for the physics-based model and below 6% for the neural network model.&lt;/div&gt;&lt;/div&gt; Computer science ; Control (management) ; Control engineering ; Artificial intelligence ; Engineering Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Monika Gešvandtnerová | Dario Rocca | Tomáš Bučko
Source: ChemRxiv (preprint, 2021-02-03)
Field: Chemistry — Inorganic Chemistry

Methanol Carbonylation over Acid Mordenite: Insights from Ab Initio Molecular Dynamics and Machine Learning Thermodynamic Perturbation Theory In this work we present a detailed \textit{ab initio} study of the carbonylation reaction of methoxy groups in the zeolite mordenite, as it is the rate determining step in a series of elementary reactions leading to ethanol. For the first time we employ full molecular dynamics simulations to evaluate free energies of activation for the reactions in side pockets and main channels. Results show that the reaction in the side pocket is preferred and, when dispersion interactions are taken into account, this preference becomes even stronger. This conclusion is confirmed using multiple levels of density functional theory approximations with (PBE-D2, PBE-MBD, and vdW-DF2-B86R) or without (PBE, HSE06) dispersion corrections. These calculations, that in principle would require several demanding molecular dynamics simulations, were made possible at a minimal computational cost by using a newly developed approach that combines thermodynamic perturbation theory with machine learning. Ab initio ; Molecular dynamics ; Density functional theory ; Carbonylation ; Mordenite ; Perturbation theory (quantum mechanics) ; Computational chemistry ; Chemistry ; Methanol ; Thermodynamics ; Physics ; Zeolite ; Catalysis ; Quantum mechanics ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Shidang Xu | Pengfei Cai | Jiali Li | Xianhe Zhang | Xianglong Liu | Xiaonan Wang | Bin Liu
Source: ChemRxiv (preprint, 2021-09-15)
Field: Materials Science — Materials Chemistry

Accelerated Design of Near-Infrared-II Molecular Fluorophores via First-Principles Understanding and Machine Learning Organic molecular fluorophores in the second near-infrared window (NIR-II) have attracted much attention in the recent decade due to their great potentials in both fundamental research and practical applications. This is especially true for biomedical research, owing to their deep light penetration depth and low bioluminescence background at the long wavelength. However, the fluorescence quantum yields (QY) of most NIR-II materials are very low, which are not ideal for practical applications. Although there is a growing need to discover new NIR-II fluorophores, most of them were designed based on experience, and the structures were limited to few molecular motifs. Herein, we report the design of high QY NIR-II fluorophores in solutions based on enhancing the rigidity of the conjugated backbones, which could be quantified by the Seminario method. A deep neural network was trained to predict the HOMO-LUMO energy gaps for a chemical library of NIR-II backbone structures. Hundreds of new NIR-II cores with low energy gap were discovered, and eight of them across different acceptor cores are found to have relatively rigid conjugated backbones. With further molecular processing or formulation, the proposed new fluorophores should boost the development of NIR-II materials for applications in a wide range of fields. Near-infrared spectroscopy ; Conjugated system ; Fluorescence ; Nanotechnology ; HOMO/LUMO ; Materials science ; Quantum yield ; Molecular engineering ; Chemistry ; Molecule ; Physics ; Polymer ; Optics ; Organic chemistry Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Richa R. Naik | Armi Tiihonen | Janak Thapa | Clio Batali | Zhe Liu | Shijing Sun | Tonio Buonassisi
Source: arXiv (Cornell University) (preprint, 2021-06-21)
Field: Materials Science — Materials Chemistry

Discovering Equations that Govern Experimental Materials Stability under Environmental Stress using Scientific Machine Learning While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, inductive reasoning and knowledge extraction remain elusive tasks, in part because of the difficulty extracting fungible knowledge representations from experimental data. In this manuscript, we use ML to infer the underlying dynamical differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). We apply a sparse regression algorithm that automatically identifies the differential equation describing the dynamics from time-series data. We find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85°C is described minimally with three terms (specifically, a second-order polynomial), and not a simple single-order reaction (i.e. 0th, 1st, or 2nd-order reaction). We demonstrate how computer-derived results can aid the researcher to develop profound mechanistic insights. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous in functional form to autocatalytic or self-propagating reactions, suggesting future strategies to suppress MAPI degradation. We examine the robustness of our conclusions to experimental luck-of-the-draw variance and Gaussian noise using a combination of experiment and simulation, and describe the experimental limits within which this methodology can be applied. Our study demonstrates the application of scientific ML in experimental chemical and materials systems, highlighting the promise and challenges associated with ML-aided scientific discovery. Computer science ; Experimental data ; Robustness (evolution) ; Stability (learning theory) ; Autocatalysis ; Artificial intelligence ; Machine learning ; Biological system ; Algorithm ; Mathematics ; Chemistry ; Catalysis Machine Learning in Materials Science
Authors: Simon Axelrod | Eugene I. Shakhnovich | Rafael Gómez‐Bombarelli
Source: RePEc: Research Papers in Economics (preprint, 2021-08-10)
Field: Materials Science — Materials Chemistry

Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential Light-induced chemical processes are ubiquitous in nature and have widespread technological applications. For example, photoisomerization can allow a drug with a photo-switchable scaffold such as azobenzene to be activated with light. In principle, photoswitches with desired photophysical properties like high isomerization quantum yields can be identified through virtual screening with reactive simulations. In practice, these simulations are rarely used for screening, since they require hundreds of trajectories and expensive quantum chemical methods to account for non-adiabatic excited state effects. Here we introduce a diabatic artificial neural network (DANN) based on diabatic states to accelerate such simulations for azobenzene derivatives. The network is six orders of magnitude faster than the quantum chemistry method used for training. DANN is transferable to azobenzene molecules outside the training set, predicting quantum yields for unseen species that are correlated with experiment. We use the model to virtually screen 3,100 hypothetical molecules, and identify novel species with extremely high predicted quantum yields. The model predictions are confirmed using high accuracy non-adiabatic dynamics. Our results pave the way for fast and accurate virtual screening of photoactive compounds. Photoisomerization ; Diabatic ; Azobenzene ; Adiabatic process ; Quantum ; Excited state ; Surface hopping ; Molecule ; Isomerization ; Quantum chemistry ; Computer science ; Chemical physics ; Chemistry ; Physics ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Machine Learning in Materials Science ; Nonlinear Optical Materials Studies
Authors: Ke Chen | Christian Künkel | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2021-11-24)
Field: Materials Science — Materials Chemistry

Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening The molecular reorganization energy $\lambda$ strongly influences the charge carrier mobility of organic semiconductors and is therefore an important target for molecular design. Machine learning (ML) models generally have the potential to strongly accelerate this design process (e.g. in virtual screening studies) by providing fast and accurate estimates of molecular properties. While such models are well established for simple properties (e.g. the atomization energy), $\lambda$ poses a significant challenge in this context. In this paper, we address the questions of how ML models for $\lambda$ can be improved and what their benefit is in high-throughput virtual screening (HTVS) studies. We find that, while improved predictive accuracy can be obtained relative to a semiempirical baseline model, the improvement in molecular discovery is somewhat marginal. In particular, the ML enhanced screenings are more effective in identifying promising candidates but lead to a less diverse sample. We further use substructure analysis to derive a general design rule for organic molecules with low $\lambda$ from the HTVS results. Lambda ; Virtual screening ; Computer science ; Context (archaeology) ; Organic semiconductor ; Organic molecules ; Machine learning ; Physics ; Molecule ; Artificial intelligence ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Tianze Zheng | Weihao Gao | Chong Wang
Source: arXiv (Cornell University) (preprint, 2021-11-30)
Field: Materials Science — Materials Chemistry

Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks Molecular dynamics (MD) simulation predicts the trajectory of atoms by solving Newton's equation of motion with a numeric integrator. Due to physical constraints, the time step of the integrator need to be small to maintain sufficient precision. This limits the efficiency of simulation. To this end, we introduce a graph neural network (GNN) based model, MDNet, to predict the evolution of coordinates and momentum with large time steps. In addition, MDNet can easily scale to a larger system, due to its linear complexity with respect to the system size. We demonstrate the performance of MDNet on a 4000-atom system with large time steps, and show that MDNet can predict good equilibrium and transport properties, well aligned with standard MD simulations. Integrator ; Computer science ; Graph ; Molecular dynamics ; Artificial neural network ; Trajectory ; Algorithm ; Control theory (sociology) ; Statistical physics ; Physics ; Theoretical computer science ; Artificial intelligence Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Topic Modeling
Authors: Xintong Zhao | Steven A. Lopez | Semion K. Saikin | Xiaohua Hu | Jane Greenberg
Source: arXiv (Cornell University) (preprint, 2021-09-27)
Field: Materials Science — Materials Chemistry

Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning Scientific literature is one of the most significant resources for sharing knowledge. Researchers turn to scientific literature as a first step in designing an experiment. Given the extensive and growing volume of literature, the common approach of reading and manually extracting knowledge is too time consuming, creating a bottleneck in the research cycle. This challenge spans nearly every scientific domain. For the materials science, experimental data distributed across millions of publications are extremely helpful for predicting materials properties and the design of novel materials. However, only recently researchers have explored computational approaches for knowledge extraction primarily for inorganic materials. This study aims to explore knowledge extraction for organic materials. We built a research dataset composed of 855 annotated and 708,376 unannotated sentences drawn from 92,667 abstracts. We used named-entity-recognition (NER) with BiLSTM-CNN-CRF deep learning model to automatically extract key knowledge from literature. Early-phase results show a high potential for automated knowledge extraction. The paper presents our findings and a framework for supervised knowledge extraction that can be adapted to other scientific domains. Bottleneck ; Computer science ; Knowledge extraction ; Domain knowledge ; Reading (process) ; Deep learning ; Data science ; Question answering ; Information extraction ; Artificial intelligence ; Domain (mathematical analysis) ; Information retrieval ; Natural language processing Machine Learning in Materials Science ; Topic Modeling ; Advanced Text Analysis Techniques
Authors: O. V. Uvarova | С. И. Уваров
Source: Russian Microelectronics (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Machine-Learning Based Interatomic Potential for Studying the Properties of Crystal Structures In the process of modeling multilayer semiconductor nanostructures, an important role is played by the rapid acquisition of accurate values of the characteristics of the structure under consideration. One of these characteristics is the value of the interaction energy of atoms within the structure. The energy value is also important for obtaining other quantities, such as the bulk modulus of elasticity of the structure and the shear modulus. The paper discusses a method for obtaining the energy of the interaction between two atoms, based on machine learning methods. A model built based on the Gaussian Approximation Potential (GAP) is trained on a previously prepared sample and allows predicting the energy values of pairs of atoms for the test data. The values of the coordinates of interacting atoms, the distance between the atoms, the value of the lattice constant of the structure, an indication of the type of interacting atoms, and a value describing the environment of the atoms are used as attributes. The computational experiment is carried out with the participation of one-component compounds, such as Si, Ge, and C. The rate of obtaining the energy of interacting atoms and the accuracy of the obtained value are estimated. The speed and accuracy characteristics are compared with the values obtained using the multiparticle interatomic potential, the Tersoff potential. Interatomic potential ; Materials science ; Crystal (programming language) ; Computer science ; Chemical physics ; Molecular dynamics ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Gogulan Karunanithy | Tairan Yuwen | Lewis E. Kay | D. Flemming Hansen
Source: ChemRxiv (preprint, 2021-12-15)
Field: Chemistry — Spectroscopy

Towards autonomous analysis of Chemical Exchange Saturation Transfer experiments using Deep Neural Networks Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structure of the conformers that are involved. However, analysis of the NMR data that report on exchanging macromolecules often hinges on complex least-squares fitting procedures as well as human experience and intuition, which, in some cases, limits the widespread use of the methods. The applications of deep neural networks (DNNs) and artificial intelligence have increased significantly in the sciences, and recently, specifically, within the field of biomolecular NMR, where DNNs are now available for tasks such as the reconstruction of sparsely sampled spectra, peak picking, and virtual decoupling. Here we present a DNN for the analysis of chemical exchange saturation transfer (CEST) data reporting on two- or three-site chemical exchange involving sparse state lifetimes of between approximately 3 - 60 ms, the range most frequently observed via experiment. The work presented here focuses on the 1H CEST class of methods that are further complicated, in relation to applications to other nuclei, by anti-phase features. The developed DNNs accurately predict the chemical shifts of nuclei in the exchanging species directly from anti-phase 1HN CEST profiles, along with an uncertainty associated with the predictions. The performance of the DNN was quantitatively assessed using both synthetic and experimental anti-phase CEST profiles. The assessments show that the DNN accurately determines chemical shifts and their associated uncertainties. The DNNs developed here do not contain any parameters for the end-user to adjust and the method therefore allows for autonomous analysis of complex NMR data that report on conformational exchange. Computer science ; Artificial neural network ; Biological system ; Decoupling (probability) ; Artificial intelligence ; Chemical shift ; Magnetization transfer ; Machine learning ; Statistical physics ; Pattern recognition (psychology) ; Chemistry ; Physics ; Physical chemistry Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies ; Lanthanide and Transition Metal Complexes
Authors: Nan Xu | Chen Li | Mandi Fang | Qing Shao | Yingying Lü | Yao Shi | Yi He
Source: arXiv (Cornell University) (preprint, 2021-03-07)
Field: Materials Science — Materials Chemistry

Training Data Set Refinement for the Machine Learning Potential of Li-Si Alloys via Structural Similarity Analysis Machine learning potential enables molecular dynamics simulations of systems beyond the capability of classical force fields. The traditional approach to develop structural sets for training machine learning potential typically generate a great number of redundant configurations, which will result in unnecessary computational costs. This work investigates the possibility of reducing redundancy in an initial data set containing 6183 configurations for a Li-Si machine learning potential. Starting from the initial data set, we constructed a series of subsets ranging from 25 to 1500 configurations by combining a structural similarity analysis algorithm and the farthest point sampling method. Results show that the machine learning potential trained from a data set containing 400 configurations can achieve an accuracy comparable to the one developed from the initial data set of 6183 configurations in describing potential energies, atomic forces, and structural properties of Li-Si systems. In addition, the redundancy reducing approach also demonstrates advantages over the classic stochastic method for constructing a concise training data set for Li-Si systems. Redundancy (engineering) ; Computer science ; Similarity (geometry) ; Artificial intelligence ; Set (abstract data type) ; Machine learning ; Algorithm ; Training set ; Data point Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Michael Ashby | Jenna A. Bilbrey
Source: arXiv (Cornell University) (preprint, 2021-06-24)
Field: Materials Science — Materials Chemistry

Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks We apply a temporal edge prediction model for weighted dynamic graphs to predict time-dependent changes in molecular structure. Each molecule is represented as a complete graph in which each atom is a vertex and all vertex pairs are connected by an edge weighted by the Euclidean distance between atom pairs. We ingest a sequence of complete molecular graphs into a dynamic graph neural network (GNN) to predict the graph at the next time step. Our dynamic GNN predicts atom-to-atom distances with a mean absolute error of 0.017 Å, which is considered ``chemically accurate'' for molecular simulations. We also explored the transferability of a trained network to new molecular systems and found that finetuning with less than 10% of the total trajectory provides a mean absolute error of the same order of magnitude as that when training from scratch on the full molecular trajectory. Vertex (graph theory) ; Euclidean distance ; Euclidean geometry ; Artificial neural network ; Computer science ; Graph ; Molecular dynamics ; Transferability ; Enhanced Data Rates for GSM Evolution ; Algorithm ; Artificial intelligence ; Mathematics ; Chemistry ; Theoretical computer science ; Computational chemistry ; Geometry ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics