jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Boris Kozinsky
Source: ECS Meeting Abstracts (article, 2020-05-01)
Field: Chemistry — Electrochemistry

(Invited) Understanding Dynamical Stability of Batteries and Catalysts with First-Principles and Ex-machina Computations Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes of lithium-ion batteries, but the degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemistry to investigate the oxidative decomposition that govern voltage stability of multi-component organic electrolytes, we find that electrolyte decomposition is a process involving the solvent and the salt anion and requires explicit treatment of their coupling. We find that the ionization potential of the solvent-anion system is often lower than that of the isolated solvent or the anion. This understanding of the oxidation mechanism allows to formulate a simple predictive model that explains experimentally observed trends in the onset voltages of degradation of electrolytes near the cathode. This model opens opportunities for rapid rational design of stable electrolytes for high-energy batteries. Design of materials based on dynamical and transport properties, important for batteries and catalysts, involves systematic exploration of many structural and compositional variations and hence requires very fast and accurate computations of dynamical phenomena. I will present recent progress and challenges in “ex-machina” materials design, a new paradigm in which an automated closed-loop machine learning algorithm constructs non-parametric Bayesian force fields that combine first-principles accuracy with internal quantitative uncertainty and prior information of physical symmetries. We apply this method, implemented in the FLARE framework [1], to large-scale dynamics simulations of alloys, ion conductors, catalysts and 2D materials. [1] J. Vandermause, S. B. Torrisi, S. Batzner, A. M. Kolpak, B. Kozinsky, “On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events” , (2019), arXiv:1904.02042 Electrolyte ; Computer science ; Materials science ; Chemistry ; Nanotechnology ; Physical chemistry Electrochemical Analysis and Applications ; Ionic liquids properties and applications ; Molecular Junctions and Nanostructures
Authors: Kavindri Ranasinghe | Olexandr Isayev | Justin S. Smith | Adrián E. Roitberg
Source:  (preprint, 2020-10-15)
Field: Materials Science — Materials Chemistry

Modeling and prediction of energetics and mechanism of organic chemical reactions: Transference of knowledge in deep learning Energetics ; Mechanism (biology) ; Chemistry ; Computer science ; Artificial intelligence ; Biochemical engineering ; Cognitive science ; Psychology ; Epistemology ; Engineering ; Physics ; Thermodynamics ; Philosophy Machine Learning in Materials Science
Authors: Andrew R. Shaughnessy | Xin Gu | Tao Wen | Susan L. Brantley
Source:  (preprint, 2020-10-27)
Field: Chemistry — Analytical Chemistry

Supplementary material to "Machine Learning Deciphers CO <sub>2</sub> Sequestration and SubsurfaceFlowpaths from Stream Chemistry" To employ NMF on limited datasets of stream chemistry, a bootstrapped data set was generated using a multivariate normal distribution of log-transformed stream water chemistries, similar to the procedure outlined in The bootstrapped dataset matches the measured means of the log-transformed stream water chemistries and maintains covariation between analytes. A comparison between the measured and bootstrapped data sets can be seen in Fig. All of the input features were normalized to values between 0 and 1 to not bias the model training to any one input feature. Next, the model was trained to the bootstrapped dataset using NMF algorithms in the python library scikit-learn Lastly, the trained model was applied to measured stream water samples to delineate mixing proportions. Ampere ; Chemistry ; Engineering ; Electrical engineering ; Voltage Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications
Authors: Cole, DJ | Mones, L | Csányi, G | ,
Source: Cambridge University Engineering Department Publications Database (article, 2020-05-13)
Field: Materials Science — Materials Chemistry

A machine learning based intramolecular potential for a flexible organic molecule. Quantum mechanical predictive modelling in chemistry and biology is often hindered by the long time scales and large system sizes required of the computational model. Here, we employ the kernel regression machine learning technique to construct an analytical potential, using the Gaussian Approximation Potential software and framework, that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine. Challenges linked to the high dimensionality of the configurational space of the molecule are overcome by developing an iterative training protocol and employing a representation that separates short and long range interactions. The analytical model is connected to the MCPRO simulation software, which allows us to perform Monte Carlo simulations of the small molecule bound to two proteins, p38 MAP kinase and leukotriene A4 hydrolase, as well as in water. We demonstrate that our machine learning based intramolecular model is transferable to the condensed phase, and demonstrate that the use of a faithful representation of the quantum mechanical potential energy surface can result in corrections to absolute protein-ligand binding free energies of up to 2 kcal mol-1 in the example studied here. Quantum ; Kernel (algebra) ; Curse of dimensionality ; Representation (politics) ; Potential energy surface ; Monte Carlo method ; Intramolecular force ; Computational learning theory ; Gaussian Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Physical and Chemical Molecular Interactions
Authors: Yu Shi | Carrie C. Doyle | Thomas L. Beck
Source: ChemRxiv (preprint, 2020-07-29)
Field: Materials Science — Materials Chemistry

Water Dipole and Quadrupole Moment Contributions to the Ion Hydration Free Energy by the Deep Neural Network Trained with Ab Initio Molecular Dynamics Data &lt;div&gt;We report a calculation scheme on water molecular dipole and quadrupole moments in the liquid phase through a Deep Neural Network (DNN) model. Employing the the Maximally Localized Wannier Functions (MLWF) for the valence electrons, we obtain the water moments through a post-process on trajectories from \textit{ab-initio} molecular dynamics (AIMD) simulations at the density functional theory (DFT) level. In the framework of the deep potential molecular dynamics (DPMD), we develop a scheme to train a DNN with the AIMD moments data. Applying the model, we calculate the contributions from water dipole and quadrupole moments to the electrostatic potential at the center of a cavity of radius 4.1 \AA\ as -3.87 V, referenced to the average potential in the bulk-like liquid region.&lt;/div&gt;&lt;div&gt;To unravel the ion-independent water effective local potential contribution to the ion hydration free energy, we estimate the 3rd cumulant term as -0.22 V from simulations totally over 6 ns, a time-scale inaccessible for AIMD calculations. &lt;/div&gt; Dipole ; Quadrupole ; Molecular dynamics ; Ion ; Wannier function ; Ab initio ; Atomic physics ; Physics ; Moment (physics) ; Density functional theory ; Valence (chemistry) ; Molecular physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Harish S. Bhat | Karnamohit Ranka | Christine M. Isborn
Source: International Journal of Dynamics and Control (preprint, 2020-10-06)
Field: Materials Science — Materials Chemistry

Machine learning a molecular Hamiltonian for predicting electron dynamics Hamiltonian (control theory) ; Discretization ; Solver ; Density matrix ; Statistical physics ; Molecular dynamics ; Physics ; Computer science ; Electron ; Quantum ; Applied mathematics ; Quantum mechanics ; Classical mechanics ; Mathematical optimization ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Jian Yang | Teresa Karjala | Jonathan D. Mendenhall | Valeriy V. Ginzburg | Rajen Patel | Fawzi G. Hamad | Elva L. Lugo | Pavan Valavala
Source: Bulletin of the American Physical Society (article, 2020-03-03)
Field: Materials Science — Materials Chemistry

Hybrid machine learning/materials science modeling for semi-crystalline polymer during film fabrication process Fabrication ; Materials science ; Process (computing) ; Polymer ; Nanotechnology ; Process engineering ; Computer science ; Composite material ; Engineering Machine Learning in Materials Science
Authors: Leili Zhang | Giacomo Domeniconi | Chih-Chieh Yang | Seung-Gu Kang | Ruhong Zhou | Guojing Cong
Source: arXiv (Cornell University) (preprint, 2020-11-27)
Field: Materials Science — Materials Chemistry

CASTELO: Clustered Atom Subtypes aidEd Lead Optimization -- a combined machine learning and molecular modeling method Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that automates lead optimization workflow \textit{in silico}. The initial data collection is achieved with physics-based molecular dynamics (MD) simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional clustering method and CVAE-based clustering method are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. With no need for extensive structure-activity relationship database, our method provides new hints for drug modification hotspots which can be used to improve drug efficacy. Our workflow can potentially reduce the lead optimization turnaround time from months/years to days compared with the conventional labor-intensive process and thus can potentially become a valuable tool for medical researchers. Workflow ; Computer science ; Autoencoder ; Cluster analysis ; Lead (geology) ; Data mining ; Machine learning ; Process (computing) ; Artificial intelligence ; Representation (politics) ; Deep learning ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Pedro Baptista de Castro | Kensei Terashima | Takafumi D. Yamamoto | Suguru Iwasaki | Ryo Matsumoto | Shintaro Adachi | Yoshito Saito | Song Peng | Hiroyuki Takeya | Yoshihiko Takano
Source: The Japan Society of Applied Physics (article, 2020-01-28)
Field: Materials Science — Materials Chemistry

Search of magnetocaloric materials through machine-learning Magnetic refrigeration ; Materials science ; Nanotechnology ; Engineering physics ; Engineering ; Magnetization Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: 慶浩 林 | Yoshihiro Hayashi | Massimiliano Zamengo
Source: Institutional Repositories DataBase (IRDB) (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

Potentials and challenges of polymer informatics: exploiting machine learning for polymer design Polymer ; Feature (linguistics) ; Pattern recognition (psychology) ; Key (lock) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Advanced Physical and Chemical Molecular Interactions
Authors: Valentin Stanev | Haotong Liang | A. Gilad Kusne | Ichiro Takeuchi
Source: Bulletin of the American Physical Society (article, 2020-03-06)
Field: Materials Science — Materials Chemistry

Machine Learning Tool for Crystal Structure Predictions Computer science ; Crystal structure prediction ; Artificial intelligence ; Crystal structure ; Materials science ; Machine learning ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Huaping Li | Yuliang Jin | Ying Jiang | Jeff Z. Y. Chen
Source:  (article, 2020-07-28)
Field: Materials Science — Materials Chemistry

Gardner transition: non-equilibrium criticality in a structural glass determined from a hybrid study of molecular simulations and machine learning Apparent critical phenomena, typically indicated by growing correlation lengths and dynamical slowing-down, are ubiquitous in non-equilibrium systems such as supercooled liquids, amorphous solids, active matter and spin glasses. It is often challenging to determine if such observations are related to a true second-order phase transition as in the equilibrium case, or simply a crossover, and even more so to measure the associated critical exponents. Here we provide strong numerical evidence that a canonical non-equilibrium system, the hard-sphere glass, exhibits a true second-order phase transition, the so-called Gardner transition, in the limits of large systems and long-aging times. Using a hybrid molecular simulation - machine learning approach, we obtain scaling laws for both finite-size and aging effects, and determine the critical exponents that traditional methods fail to estimate. Our study provides a novel approach that is useful to understand the nature of glass transitions, and can be generalized to analyze other non-equilibrium phase transitions. Statistical physics ; Supercooling ; Spin glass ; Phase transition ; Glass transition ; Crossover ; Scaling ; Criticality ; Critical exponent ; Amorphous solid ; Physics ; Condensed matter physics ; Materials science ; Thermodynamics ; Mathematics ; Computer science ; Chemistry ; Machine learning ; Geometry Material Dynamics and Properties ; Theoretical and Computational Physics ; Complex Systems and Time Series Analysis
Authors: Yu Shi | Carrie C. Doyle | Thomas L. Beck
Source: ChemRxiv (preprint, 2020-07-29)
Field: Materials Science — Materials Chemistry

Water Dipole and Quadrupole Moment Contributions to the Ion Hydration Free Energy by the Deep Neural Network Trained with Ab Initio Molecular Dynamics Data We report a calculation scheme on water molecular dipole and quadrupole moments in the liquid phase through a Deep Neural Network (DNN) model. Employing the the Maximally Localized Wannier Functions (MLWF) for the valence electrons, we obtain the water moments through a post-process on trajectories from \textit{ab-initio} molecular dynamics (AIMD) simulations at the density functional theory (DFT) level. In the framework of the deep potential molecular dynamics (DPMD), we develop a scheme to train a DNN with the AIMD moments data. Applying the model, we calculate the contributions from water dipole and quadrupole moments to the electrostatic potential at the center of a cavity of radius 4.1 \AA\ as -3.87 V, referenced to the average potential in the bulk-like liquid region.To unravel the ion-independent water effective local potential contribution to the ion hydration free energy, we estimate the 3rd cumulant term as -0.22 V from simulations totally over 6 ns, a time-scale inaccessible for AIMD calculations. Quadrupole ; Dipole ; Molecular dynamics ; Ion ; Physics ; Wannier function ; Ab initio ; Moment (physics) ; Atomic physics ; Molecular physics ; Chemistry ; Statistical physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Kostiantyn Lapchevskyi
Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Predicting Properties of Crystals Crystalline structures are vital to the modern technology. Yet, we are still only starting to ﬁgure out how to properly estimate their directional properties using machine learning techniques. In order to improvethat, I build uponthe theory and codebase of Euclidean Neural Networks (networks equivariant to 3D rotations). The main contributions of this work are: a derivation of the decompositon/reconstruction equations of elastic tensor that enables using it as a train target, optimized CUDA implementation of the core operation PeriodicConvolution that makes it fast and scalable, and ananalys is of the trends of geometric structures and electronic properties of the crystal in Materials Project Database and how these trends impact hyper-parameters for convolutional neural network architectures such as Euclidean Neural Networks. Euclidean geometry ; Convolutional neural network ; Artificial neural network ; Computer science ; Tensor (intrinsic definition) ; Scalability ; Codebase ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Computational science ; Mathematics ; Geometry ; Software ; Programming language ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Meeri Kim
Source: Scilight (article, 2020-07-09)
Field: Chemistry — Physical and Theoretical Chemistry

A machine learning model to predict molecular dipole moments The model achieves highly accurate results compared to the current state of the art for predicting dipole moments at a fraction of the computational cost. Dipole ; Fraction (chemistry) ; Current (fluid) ; Method of moments (probability theory) ; Computer science ; Statistical physics ; Artificial intelligence ; Moment (physics) ; State (computer science) ; Algorithm ; Physics ; Machine learning ; Mathematics ; Quantum mechanics ; Chemistry ; Statistics ; Thermodynamics Various Chemistry Research Topics ; Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies
Authors: Anh Tran | Julien Tranchida | Timothy Wildey | Aidan P. Thompson
Source: arXiv (Cornell University) (preprint, 2020-05-29)
Field: Materials Science — Materials Chemistry

Multi-fidelity machine-learning with uncertainty quantification and\n Bayesian optimization for materials design: Application to ternary random\n alloys We present a scale-bridging approach based on a multi-fidelity (MF)\nmachine-learning (ML) framework leveraging Gaussian processes (GP) to fuse\natomistic computational model predictions across multiple levels of fidelity.\nThrough the posterior variance of the MFGP, our framework naturally enables\nuncertainty quantification, providing estimates of confidence in the\npredictions. We used Density Functional Theory as high-fidelity prediction,\nwhile a ML interatomic potential is used as the low-fidelity prediction.\nPractical materials design efficiency is demonstrated by reproducing the\nternary composition dependence of a quantity of interest (bulk modulus) across\nthe full aluminum-niobium-titanium ternary random alloy composition space. The\nMFGP is then coupled to a Bayesian optimization procedure and the computational\nefficiency of this approach is demonstrated by performing an on-the-fly search\nfor the global optimum of bulk modulus in the ternary composition space. The\nframework presented in this manuscript is the first application of MFGP to\natomistic materials simulations fusing predictions between Density Functional\nTheory and classical interatomic potential calculations.\n Bayesian optimization ; Ternary operation ; Fidelity ; Density functional theory ; Computer science ; Gaussian ; Bulk modulus ; Uncertainty quantification ; Materials science ; Gaussian process ; Interatomic potential ; Algorithm ; Bayesian probability ; Molecular dynamics ; Machine learning ; Statistical physics ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Dakota Folmsbee
Source:  (preprint, 2020-03-20)
Field: Chemistry — Physical and Theoretical Chemistry

Assessing conformer energies: Machine learning vs conventional quantum chemistry Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight binding method (GFN) for property calculations, we are able to efficiently search chemical space for polymers with specific electric properties. Given the scope of the search space, using the GA technique saves considerable time and resources over other strategies including systematic, brute-force search. By using the GFN method to simultaneously optimize and determine property values for selected polymers, hundreds of polymers can be evaluated in less than a day, a considerable speed-up over more traditional quantum methods. Currently, we are employing this GA-GFN method to search for oligomers with simultaneously large polarizabilities and dipole moments for possible application in high-dielectric capacitors. Given the presence of an apparent trade-off between these two properties when plotted against one another for large known polymer sets, we are interested in testing the rigidity of the Pareto front that appears in these plots. By using the GA-GFN method, we hope to either push past this front and discover novel polymers with large values for both properties, or to establish more firmly the presence of an inverse relationship between these properties in polymeric materials and explore the underlying physics and chemistry behind this phenomenon. Conformational isomerism ; Quantum chemistry ; Quantum chemical ; Quantum ; Chemistry ; Computational chemistry ; Chemical physics ; Nanotechnology ; Physics ; Materials science ; Quantum mechanics ; Organic chemistry ; Molecule ; Catalysis ; Reaction mechanism Various Chemistry Research Topics ; History and advancements in chemistry ; Machine Learning in Materials Science
Authors: Sutanay Choudhury | Jenna A. Bilbrey | Logan Ward | Sotiris S. Xantheas | Ian Foster | Joseph P. Heindel | Ben Blaiszik | Marcus Schwarting
Source: arXiv (Cornell University) (preprint, 2020-11-30)
Field: Materials Science — Materials Chemistry

HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and the universal solvation properties of water. We present a set of challenge problems for preserving intermolecular interactions and structural motifs in machine-learning approaches to chemical problems, through the use of a recently published dataset of 4.95 million water clusters held together by hydrogen bonding interactions and resulting in longer range structural patterns. The dataset provides spatial coordinates as well as two types of graph representations, to accommodate a variety of machine-learning practices. Intermolecular force ; Benchmark (surveying) ; Solvation ; Generative grammar ; Computer science ; Graph ; Set (abstract data type) ; Structural motif ; Variety (cybernetics) ; Generative model ; Theoretical computer science ; Machine learning ; Chemical physics ; Artificial intelligence ; Molecule ; Chemistry ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics