jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zilin Song | Francesco Trozzi | Hao Tian | Chao Yin | Peng Tao
Source: ACS Physical Chemistry Au (article, 2022-05-18)
Field: Materials Science — Materials Chemistry

Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways With the increasing popularity of machine learning (ML) applications, the demand for explainable artificial intelligence techniques to explain ML models developed for computational chemistry has also emerged. In this study, we present the development of the Boltzmann-weighted cumulative integrated gradients (BCIG) approach for effective explanation of mechanistic insights into ML models trained on high-level quantum mechanical and molecular mechanical (QM/MM) minimum energy pathways. Using the acylation reactions of the Toho-1 β-lactamase and two antibiotics (ampicillin and cefalexin) as the model systems, we show that the BCIG approach could quantitatively attribute the energetic contribution in one system and the relative reactivity of individual steps across different systems to specific chemical processes such as the bond making/breaking and proton transfers. The proposed BCIG contribution attribution method quantifies chemistry-interpretable insights in terms of contributions from each elementary chemical process, which is in agreement with the validating QM/MM calculations and our intuitive mechanistic understandings of the model reactions. Quantum ; Chemistry ; Quantum chemistry ; Reactivity (psychology) ; Computer science ; Computational chemistry ; Catalysis ; Reaction mechanism ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Alexander L. Bowler | Samet Ozturk | Ahmed Rady | Nicholas J. Watson
Source: Sensors (article, 2022-09-24)
Field: Chemistry — Analytical Chemistry

Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy The addition of incorrect agri-food powders to a production line due to human error is a large safety concern in food and drink manufacturing, owing to incorporation of allergens in the final product. This work combines near-infrared spectroscopy with machine-learning models for early detection of this problem. Specifically, domain adaptation is used to transfer models from spectra acquired under stationary conditions to moving samples, thereby minimizing the volume of labelled data required to collect on a production line. Two deep-learning domain-adaptation methodologies are used: domain-adversarial neural networks and semisupervised generative adversarial neural networks. Overall, accuracy of up to 96.0% was achieved using no labelled data from the target domain moving spectra, and up to 99.68% was achieved when incorporating a single labelled data instance for each material into model training. Using both domain-adaptation methodologies together achieved the highest prediction accuracies on average, as did combining measurements from two near-infrared spectroscopy sensors with different wavelength ranges. Ensemble methods were used to further increase model accuracy and provide quantification of model uncertainty, and a feature-permutation method was used for global interpretability of the models. Interpretability ; Artificial intelligence ; Computer science ; Artificial neural network ; Chemometrics ; Domain (mathematical analysis) ; Machine learning ; Pattern recognition (psychology) ; Feature (linguistics) ; Adaptation (eye) ; Mathematics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Takeshi Kobayashi | Da‐Jiang Liu | Frédéric A. Perras
Source: Chemical Communications (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Spatial arrangement of dynamic surface species from solid-state NMR and machine learning-accelerated MD simulations The surface arrangement of motional organic functionalities is explored by experimental dipolar coupling measurements and the prediction of motionally-averaged coupling constant from molecular dynamics simulations. The use of machine learning potentials was key to reaching the timescale required. The distance between dynamic surface species are important in cooperative heterogeneous catalysis. Chemical physics ; Coupling (piping) ; Surface (topology) ; Molecular dynamics ; Coupling constant ; Dipole ; Solid-state nuclear magnetic resonance ; Materials science ; Biological system ; Chemistry ; Nuclear magnetic resonance ; Physics ; Computational chemistry Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: David F. Nippa | Kenneth Atz | Remo Hohler | Alex T. Müller | Andreas Marx | Christian Bartelmus | Georg Wuitschik | Irene Marzuoli | Vera Jost | Jens Wolfard | Martin Binder | Antonia F. Stepan | David B. Konrad | Uwe Grether | Rainer E. Martin | Gisbert Schneider
Source: ChemRxiv (preprint, 2022-10-17)
Field: Materials Science — Materials Chemistry

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning Late-stage functionalization (LSF) is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, an LSF platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in LSF, the computational model correctly predicted the reactivity of 81% of novel substrates, while reaction yields for diverse reaction conditions were predicted with a mean absolute error margin of 4–5%. The regioselectivity of the major products was accurately captured in up to 90% of the cases studied. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and quantum mechanical information on model performance was quantified and a new comprehensive simple user-friendly reaction format (SURF) is introduced which proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation (HTE) for LSF. Computer science ; Diversification (marketing strategy) ; Throughput ; Artificial intelligence ; Deep learning ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Lan Fang | Jian Liu | Dandan Han | Zhenguo Gao | Junbo Gong
Source: Powder Technology (article, 2022-11-07)
Field: Materials Science — Materials Chemistry

Revealing the role of polymer in the robust preparation of the 2,4-dichlorophenoxyacetic acid metastable crystal form by AI-based image analysis Metastability ; Crystal (programming language) ; Nucleation ; Dissolution ; Polymer ; Materials science ; Crystallization ; Crystal engineering ; Crystal structure ; Crystallography ; Chemistry ; Organic chemistry ; Computer science ; Composite material ; Supramolecular chemistry Crystallization and Solubility Studies ; Thermal and Kinetic Analysis ; Injection Molding Process and Properties
Authors: LeeAnn M. Sager-Smith | David A. Mazziotti
Source: Journal of the American Chemical Society (article, 2022-10-04)
Field: Materials Science — Materials Chemistry

Reducing the Quantum Many-Electron Problem to Two Electrons with Machine Learning An outstanding challenge in chemical computation is the many-electron problem where computational methodologies scale prohibitively with system size. The energy of any molecule can be expressed as a weighted sum of the energies of two-electron wave functions that are computable from only a two-electron calculation. Despite the physical elegance of this extended "aufbau" principle, the determination of the distribution of weights─geminal occupations─for general molecular systems has remained elusive. Here we introduce a new paradigm for electronic structure where approximate geminal-occupation distributions are "learned" via a convolutional neural network. We show that the neural network learns the <i>N</i>-representability conditions, constraints on the distribution for it to represent an <i>N</i>-electron system. By training on hydrocarbon isomers with only 2-7 carbon atoms, we are able to predict the energies for isomers of octane as well as hydrocarbons with 8-15 carbons. The present work demonstrates that machine learning can be used to reduce the many-electron problem to an effective two-electron problem, opening new opportunities for accurately predicting electronic structure. Geminal ; Chemistry ; Electron ; Artificial neural network ; Convolutional neural network ; Computation ; Quantum ; Statistical physics ; Wave function ; Computational chemistry ; Quantum mechanics ; Artificial intelligence ; Computer science ; Physics ; Algorithm ; Stereochemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Meenakshi Ahirwar | B.K. Behera
Source: Journal of the Textile Institute (article, 2022-11-30)
Field: Materials Science — Polymers and Plastics

Prediction of tear strength of bed sheet fabric using machine learning based artificial neural network Fabric tear strength is an important parameter in textile materials since it is linked to the fabric’s life. When it comes to bed sheets, a consumer’s main concerns are mechanical comfort and durability. The fabric’s tear strength has a direct impact on its durability. In this study, a novel algorithm was developed using machine learning based neural network approach taking fabric parameters as input and warp and weft tear strength as output. Different state-of-the-art algorithms were used among which the XGboost model (Extreme Gradient Boosting) gave the best results. The training accuracy of the machine learning model was approximately 99.99% and mean absolute error was 0.61 for the weft model and 1.78 for the warp model. Correlation between various fabric parameters and tear strength was determined. The model has the potential to be beneficial to textile industries since it can reduce the number of production efforts for a high-quality bed sheet fabric, saving both time and money. Artificial neural networks are used to forecast fabric performance in textile manufacturing. Tests show that these artificial neural networks are good at predicting future difficulties. Artificial neural network ; Durability ; Textile ; Artificial intelligence ; Woven fabric ; Engineering ; Boosting (machine learning) ; Gradient boosting ; Tear resistance ; Machine learning ; Computer science ; Composite material ; Materials science ; Ultimate tensile strength ; Operations management Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Johannes Niskanen | Anton Vladyka | Johannes Kettunen | Christoph J. Sahle
Source: Journal of Electron Spectroscopy and Related Phenomena (article, 2022-08-22)
Field: Materials Science — Materials Chemistry

Machine learning in interpretation of electronic core-level spectra Electronic transitions involving core-level orbitals offer a localized, atomic-site and element specific peek window into statistical systems such as molecular liquids. Although formally understood, the complex relation between structure and spectrum – and the effect of statistical averaging of highly differing spectra of individual structures – render the analysis of an ensemble-averaged core-level spectrum complicated. We explore the applicability of machine learning for molecular structure — core-level spectrum interpretation. We focus on the electronic Hamiltonian using the H2O molecule in the classical-nuclei approximation as our test system. For a systematic view we studied both predicting structures from spectra and, vice versa, spectra from structures, using polynomial approaches and neural networks. We find predicting spectra easier than predicting structures, where a tighter grid (even unphysical) of the spectrum improves prediction, possibly inviting for over-interpretation of the model. The accuracy of the structure prediction worsens when moving outwards from the center of mass of the training set in the structural parameter space, which cannot be overcome by model selection based on generalizability. Spectral line ; Interpretation (philosophy) ; Statistical physics ; Electronic structure ; Computer science ; Core (optical fiber) ; Artificial intelligence ; Algorithm ; Machine learning ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Nathan C. Frey | Dan Zhao | Simon Axelrod | Michael Jones | David Bestor | Vijay Gadepally | Rafael Gómez‐Bombarelli | Siddharth Samsi
Source: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (article, 2022-05-01)
Field: Materials Science — Materials Chemistry

Energy-aware neural architecture selection and hyperparameter optimization Artificial Intelligence (AI) and Deep Learning in particular have increasing computational requirements, with a corresponding increase in energy consumption. There is a tremendous opportunity to reduce the computational cost and environmental impact of deep learning by accelerating neural network architecture search and hyperparameter optimization, as well as explicitly designing neural architectures that optimize for both energy efficiency and performance. Here, we introduce a framework called training performance estimation (TPE), which builds upon existing techniques for training speed estimation in order to monitor energy consumption and rank model performance-without training models to convergence-saving up to 90% of time and energy of the full training budget. We benchmark TPE in the computationally intensive, well-studied domain of computer vision and in the emerging field of graph neural networks for machine-learned inter-atomic potentials, an important domain for scientific discovery with heavy computational demands. We propose variants of early stopping that generalize this common regularization technique to account for energy costs and study the energy costs of deploying increasingly complex, knowledge-informed architectures for AI-accelerated molecular dynamics and image classification. Our work enables immediate, significant energy savings across the entire pipeline of model development and deployment and suggests new research directions for energy-aware, knowledge-informed model architecture development. Computer science ; Artificial intelligence ; Energy consumption ; Artificial neural network ; Machine learning ; Deep learning ; Benchmark (surveying) ; Efficient energy use ; Hyperparameter ; Domain knowledge ; Pipeline (software) ; Engineering Machine Learning in Materials Science ; Advanced Neural Network Applications ; Machine Learning and Data Classification
Authors: Guofeng Qian | Karnpiwat Tantratian | Lei Chen | Zhen Hu | Michael D. Todd
Source: Scientific Reports (article, 2022-12-03)
Field: Materials Science — Metals and Alloys

A probabilistic computational framework for the prediction of corrosion-induced cracking in large structures Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework. Corrosion ; Probabilistic logic ; Materials science ; Stress corrosion cracking ; Computer science ; Pitting corrosion ; Nonlinear autoregressive exogenous model ; Structural engineering ; Artificial intelligence ; Metallurgy ; Artificial neural network ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Ganesan Raman
Source: Microporous and Mesoporous Materials (article, 2022-11-20)
Field: Chemistry — Inorganic Chemistry

Identifying extra-large pore structures in zeolites with a machine learning approach and its deployment into production Zeolite ; Heuristic ; Computer science ; Artificial intelligence ; Process (computing) ; Software deployment ; Machine learning ; Materials science ; Biological system ; Chemistry ; Catalysis ; Organic chemistry ; Software engineering Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Marius Herbold | Jörg Behler
Source: The Journal of Chemical Physics (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

A Hessian-based assessment of atomic forces for training machine learning interatomic potentials In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PESs) with close to first-principles accuracy. Most current MLPs rely on atomic energy contributions given as a function of the local chemical environments. Frequently, in addition to total energies, atomic forces are also used to construct the potentials, as they provide detailed local information about the PES. Since many systems are too large for electronic structure calculations, obtaining reliable reference forces from smaller subsystems, such as molecular fragments or clusters, can substantially simplify the construction of the training sets. Here, we propose a method to determine structurally converged molecular fragments, providing reliable atomic forces based on an analysis of the Hessian. The method, which serves as a locality test and allows us to estimate the importance of long-range interactions, is illustrated for a series of molecular model systems and the metal-organic framework MOF-5 as an example for a complex organic-inorganic hybrid material. Hessian matrix ; Computer science ; Range (aeronautics) ; Construct (python library) ; Function (biology) ; Potential energy ; Statistical physics ; Energy (signal processing) ; Locality ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Aerospace engineering ; Applied mathematics ; Engineering Machine Learning in Materials Science ; Crystallography and molecular interactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Carlos León | Roderick Melnik
Source: Bioengineering (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering Shape memory materials have been playing an important role in a wide range of bioengineering applications. At the same time, recent developments of graphene-based nanostructures, such as nanoribbons, have demonstrated that, due to the unique properties of graphene, they can manifest superior electronic, thermal, mechanical, and optical characteristics ideally suited for their potential usage for the next generation of diagnostic devices, drug delivery systems, and other biomedical applications. One of the most intriguing parts of these new developments lies in the fact that certain types of such graphene nanoribbons can exhibit shape memory effects. In this paper, we apply machine learning tools to build an interatomic potential from DFT calculations for highly ordered graphene oxide nanoribbons, a material that had demonstrated shape memory effects with a recovery strain up to 14.5% for 2D layers. The graphene oxide layer can shrink to a metastable phase with lower constant lattice through the application of an electric field, and returns to the initial phase through an external mechanical force. The deformation leads to an electronic rearrangement and induces magnetization around the oxygen atoms. DFT calculations show no magnetization for sufficiently narrow nanoribbons, while the machine learning model can predict the suppression of the metastable phase for the same narrower nanoribbons. We can improve the prediction accuracy by analyzing only the evolution of the metastable phase, where no magnetization is found according to DFT calculations. The model developed here allows also us to study the evolution of the phases for wider nanoribbons, that would be computationally inaccessible through a pure DFT approach. Moreover, we extend our analysis to realistic systems that include vacancies and boron or nitrogen impurities at the oxygen atomic positions. Finally, we provide a brief overview of the current and potential applications of the materials exhibiting shape memory effects in bioengineering and biomedical fields, focusing on data-driven approaches with machine learning interatomic potentials. Graphene ; Metastability ; Materials science ; Graphene nanoribbons ; Nanotechnology ; Magnetization ; Oxide ; Condensed matter physics ; Magnetic field ; Physics Graphene research and applications ; Advanced Memory and Neural Computing ; Machine Learning in Materials Science