jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuki Nagai | Masahiko Okumura | Keita Kobayashi | Motoyuki Shiga
Source: Physical review. B./Physical review. B (article, 2020-07-29)
Field: Materials Science — Materials Chemistry

Self-learning hybrid Monte Carlo: A first-principles approach We propose an approach called self-learning hybrid Monte Carlo (SLHMC), which is a general method to make use of machine learning potentials to accelerate the statistical sampling of first-principles density-functional-theory (DFT) simulations. The trajectories are generated on an approximate machine learning (ML) potential energy surface. The trajectories are then accepted or rejected by the Metropolis algorithm based on DFT energies. In this way, the statistical ensemble is sampled exactly at the DFT level for a given thermodynamic condition. Meanwhile, the ML potential is improved on the fly by training to enhance the sampling, whereby the training data set, which is sampled from the exact ensemble, is created automatically. Using the examples of $\ensuremath{\alpha}$-quartz crystal ${\mathrm{SiO}}_{2}^{}$ and phonon-mediated unconventional superconductor ${\mathrm{YNi}}_{2}^{}{\mathrm{B}}_{2}^{}\mathrm{C}$ systems, we show that SLHMC with artificial neural networks (ANN) is capable of very efficient sampling, while at the same time enabling the optimization of the ANN potential to within meV/atom accuracy. The ANN potential thus obtained is transferable to ANN molecular dynamics simulations to explore dynamics as well as thermodynamics. This makes the SLHMC approach widely applicable for studies on materials in physics and chemistry. Monte Carlo method ; Statistical physics ; Artificial neural network ; Computer science ; Sampling (signal processing) ; Statistical ensemble ; Density functional theory ; Molecular dynamics ; Atom (system on chip) ; Artificial intelligence ; Machine learning ; Canonical ensemble ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Alfred W. Mayhew | David Topping | Jacqueline F. Hamilton
Source: ACS Omega (article, 2020-04-14)
Field: Chemistry — Spectroscopy

New Approach Combining Molecular Fingerprints and Machine Learning to Estimate Relative Ionization Efficiency in Electrospray Ionization Electrospray ionization (ESI) is widely used as an ionization source for the analysis of complex mixtures by mass spectrometry. However, different compounds ionize more or less effectively in the ESI source, meaning instrument responses can vary by orders of magnitude, often in hard-to-predict ways. This precludes the use of ESI for quantitative analysis where authentic standards are not available. Relative ionization efficiency (RIE) scales have been proposed as a route to predict the response of compounds in ESI. In this work, a scale of RIEs was constructed for 51 carboxylic acids, spanning a wide range of additional functionalities, to produce a model for predicting the RIE of unknown compounds. While using a limited number of compounds, we explore the usefulness of building a predictor using popular supervised regression techniques, encoding the compounds as combinations of different structural features using a range of common "fingerprints". It was found that Bayesian ridge regression gives the best predictive model, encoding compounds using features designed for activity coefficient models. This produced a predictive model with an <i>R</i> <sup>2</sup> score of 0.62 and a root-mean-square error (RMSE) of 0.362. Such scores are comparable to those obtained in previous studies but without the requirement to first measure or predict the physical properties of the compounds, potentially reducing the time required to make predictions. Electrospray ionization ; Ionization ; Metric (unit) ; Regression analysis ; Mean squared error ; Range (aeronautics) ; Regression ; Chemistry ; Mass spectrometry ; Correlation coefficient ; Computer science ; Biological system ; Artificial intelligence ; Pattern recognition (psychology) ; Statistics ; Machine learning ; Mathematics ; Chromatography ; Materials science ; Ion ; Engineering Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies
Authors: Dong-Heon Lee | Kyeongpung Lee | Dongsun Yoo | Wonseok Jeong | Seungwu Han
Source: Computational Materials Science (article, 2020-04-16)
Field: Materials Science — Materials Chemistry

Crystallization of amorphous GeTe simulated by neural network potential addressing medium-range order Crystallization ; Amorphous solid ; Materials science ; Phase (matter) ; Relaxation (psychology) ; Chemical physics ; Molecular dynamics ; Work (physics) ; Range (aeronautics) ; Statistical physics ; Thermodynamics ; Crystallography ; Physics ; Chemistry ; Computational chemistry ; Composite material Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Guoqing Zhou | Benjamin Nebgen | Nicholas Lubbers | Walter Malone | Anders M. N. Niklasson | Sergei Tretiak
Source: Journal of Chemical Theory and Computation (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch A new open-source high-performance implementation of Born Oppenheimer molecular dynamics based on semiempirical quantum mechanics models using PyTorch called PYSEQM is presented. PYSEQM was designed to provide researchers in computational chemistry with an open-source, efficient, scalable, and stable quantum-based molecular dynamics engine. In particular, PYSEQM enables computation on modern graphics processing unit hardware and, through the use of automatic differentiation, supplies interfaces for model parameterization with machine learning techniques to perform multiobjective training and prediction. The implemented semiempirical quantum mechanical methods (MNDO, AM1, and PM3) are described. Additional algorithms include a recursive Fermi-operator expansion scheme (SP2) and extended Lagrangian Born Oppenheimer molecular dynamics allowing for rapid simulations. Finally, benchmark testing on the nanostar dendrimer and a series of polyethylene molecules provides a baseline of code efficiency, time cost, and scaling and stability of energy conservation, verifying that PYSEQM provides fast and accurate computations. Computer science ; Graphics processing unit ; Benchmark (surveying) ; Computational science ; Molecular dynamics ; CUDA ; Graphics ; Computation ; Quantum ; Scalability ; Parallel computing ; Statistical physics ; Algorithm ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Kevin Rossi | Veronika Jurásková | Raphaël Wischert | Laurent Garel | Clémence Corminbœuf | Michele Ceriotti
Source: Journal of Chemical Theory and Computation (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH<sub>3</sub>SO<sub>3</sub>H and H<sub>2</sub>O<sub>2</sub> in Phenol We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H<sub>2</sub>O<sub>2</sub> and CH<sub>3</sub>SO<sub>3</sub>H in phenol as an example. To address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network (NN) potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density functional tight-binding (DFTB) level. The energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (<i>i.e</i>., baseline-free) is exploited via a multiple-time-step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant noncovalent interactions in the mixture, also considering nuclear quantum effects. Solvation ; Metadynamics ; Artificial neural network ; Computer science ; Sampling (signal processing) ; Integrator ; Density functional theory ; Stability (learning theory) ; Computational chemistry ; Molecular dynamics ; Statistical physics ; Chemistry ; Biological system ; Molecule ; Machine learning ; Physics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Steven K. Kauwe | Taylor Welker | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2020-07-24)
Field: Materials Science — Materials Chemistry

Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning Computer science ; Experimental data ; Ensemble learning ; Machine learning ; Artificial intelligence ; Speedup ; Property (philosophy) ; Ensemble forecasting ; Ideal (ethics) ; Mean squared error ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Surface and Thin Film Phenomena
Authors: Heesoo Park | Raghvendra Mall | Adnan Ali | Stefano Sanvito | Halima Bensmail | Fedwa El‐Mellouhi
Source: Computational Materials Science (article, 2020-06-18)
Field: Materials Science — Materials Chemistry

Importance of structural deformation features in the prediction of hybrid perovskite bandgaps Artificial intelligence ; Computer science ; Robustness (evolution) ; Machine learning ; Deformation (meteorology) ; Feature selection ; Predictive modelling ; Artificial neural network ; Experimental data ; Materials science ; Mathematics ; Chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Marco Eckhoff | Florian Schönewald | Marcel Risch | Cynthia A. Volkert | Peter E. Blöchl | Jörg Behler
Source: Physical review. B./Physical review. B (article, 2020-11-02)
Field: Materials Science — Materials Chemistry

Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential Many positive electrode materials in lithium ion batteries include transition metals, which are difficult to describe by electronic structure methods like density functional theory (DFT) due to the presence of multiple oxidation states. A prominent example is the lithium manganese oxide spinel ${\mathrm{Li}}_{x}{\mathrm{Mn}}_{2}{\mathrm{O}}_{4}$ with $0\ensuremath{\le}x\ensuremath{\le}2$. While DFT, employing the local hybrid functional PBE0r, provides a reliable description, the need for extended computer simulations of large structural models remains a significant challenge. Here, we close this gap by constructing a DFT-based high-dimensional neural network potential (HDNNP) providing accurate energies and forces at a fraction of the computational costs. As different oxidation states and the resulting Jahn-Teller distortions represent a new level of complexity for HDNNPs, the potential is carefully validated by performing x-ray diffraction experiments. We demonstrate that the HDNNP provides atomic level details and is able to predict a series of properties like the lattice parameters and expansion with increasing Li content or temperature, the orthorhombic to cubic transition, the lithium diffusion barrier, and the phonon frequencies. We show that for understanding these properties access to large time and length scales as enabled by the HDNNP is essential to close the gap between theory and experiment. Density functional theory ; Spinel ; Lithium (medication) ; Materials science ; Orthorhombic crystal system ; Lattice (music) ; Manganese ; Condensed matter physics ; Transition metal ; Oxide ; Physics ; Computational chemistry ; Chemistry ; Crystal structure ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: Janine George | Geoffroy Hautier | Albert P. Bartók | Gábor Cśanyi | Volker L. Deringer
Source: Cambridge University Engineering Department Publications Database (article, 2020-07-28)
Field: Materials Science — Materials Chemistry

Combining phonon accuracy with high transferability in Gaussian approximation potential models. Machine learning driven interatomic potentials, including Gaussian approximation potential (GAP) models, are emerging tools for atomistic simulations. Here, we address the methodological question of how one can fit GAP models that accurately predict vibrational properties in specific regions of configuration space while retaining flexibility and transferability to others. We use an adaptive regularization of the GAP fit that scales with the absolute force magnitude on any given atom, thereby exploring the Bayesian interpretation of GAP regularization as an "expected error" and its impact on the prediction of physical properties for a material of interest. The approach enables excellent predictions of phonon modes (to within 0.1 THz-0.2 THz) for structurally diverse silicon allotropes, and it can be coupled with existing fitting databases for high transferability across different regions of configuration space, which we demonstrate for liquid and amorphous silicon. These findings and workflows are expected to be useful for GAP-driven materials modeling more generally. Transferability ; Phonon ; Gaussian ; Statistical physics ; Regularization (linguistics) ; Computer science ; Bayesian optimization ; Gaussian network model ; Physics ; Machine learning ; Artificial intelligence ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuekuan Zhou | Siqian Zheng
Source: Renewable Energy (article, 2020-01-30)
Field: Chemistry — Spectroscopy

Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization Glazing ; Solar gain ; Taguchi methods ; Optimal design ; Aerogel ; Heuristic ; Computer science ; Engineering ; Mathematical optimization ; Algorithm ; Thermal ; Machine learning ; Mathematics ; Artificial intelligence ; Meteorology ; Civil engineering ; Geography Aerogels and thermal insulation ; Building Energy and Comfort Optimization ; Urban Heat Island Mitigation
Authors: Felix T. Bölle | Nicolai Rask Mathiesen | Alexander J. Nielsen | Tejs Vegge | J. M. García‐Lastra | Ivano E. Castelli
Source: Batteries & Supercaps (article, 2020-01-16)
Field: Materials Science — Materials Chemistry

Autonomous Discovery of Materials for Intercalation Electrodes Abstract The development of automated computational tools is required to accelerate the discovery of novel battery materials. In this work, we design and implement a workflow, in the framework of Density Functional Theory, which autonomously identifies materials to be used as intercalation electrodes in batteries, based on descriptors like adsorption energies and diffusion barriers. A substantial acceleration for the calculations of the kinetic properties is obtained due to a recent implementation of the Nudged Elastic Bands (NEB) method, which takes into consideration the symmetries of the system to reduce the number of images to calculate. We have applied this workflow to discover new cathode materials for Mg batteries, where two of these materials display a threefold increase in the potential of the Chevrel phase, the state‐of‐the‐art cathode in commercial prototype Mg batteries. Intercalation (chemistry) ; Electrode ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Inorganic chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Extraction and Separation Processes
Authors: Zhen Li | Shunqi Mei | Yajie Dong | Fenghua She | Yongzhen Li | Puwang Li | Lingxue Kong
Source: Pharmaceutics (review, 2020-06-08)
Field: Materials Science — Biomaterials

Functional Nanofibrous Biomaterials of Tailored Structures for Drug Delivery—A Critical Review Nanofibrous biomaterials have huge potential for drug delivery, due to their structural features and functions that are similar to the native extracellular matrix (ECM). A wide range of natural and polymeric materials can be employed to produce nanofibrous biomaterials. This review introduces the major natural and synthetic biomaterials for production of nanofibers that are biocompatible and biodegradable. Different technologies and their corresponding advantages and disadvantages for manufacturing nanofibrous biomaterials for drug delivery were also reported. The morphologies and structures of nanofibers can be tailor-designed and processed by carefully selecting suitable biomaterials and fabrication methods, while the functionality of nanofibrous biomaterials can be improved by modifying the surface. The loading and releasing of drug molecules, which play a significant role in the effectiveness of drug delivery, are also surveyed. This review provides insight into the fabrication of functional polymeric nanofibers for drug delivery. Drug delivery ; Nanofiber ; Nanotechnology ; Biocompatible material ; Materials science ; Tissue engineering ; Biomedical engineering ; Medicine Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Graphene and Nanomaterials Applications
Authors: Isabelle O’Bryon | Sarah Jenson | Eric Merkley
Source: Protein Science (review, 2020-07-26)
Field: Chemistry — Spectroscopy

Flying blind, or just flying under the radar? The underappreciated power of <i>de novo</i> methods of mass spectrometric peptide identification Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs. Proteogenomics ; Computational biology ; Identification (biology) ; Proteomics ; Tandem mass spectrometry ; Computer science ; Genome ; Database search engine ; Sequence assembly ; Peptide mass fingerprinting ; Sequence database ; Sequence (biology) ; Bottom-up proteomics ; Mass spectrometry ; Biology ; Genomics ; Chemistry ; Genetics ; Gene ; Search engine ; Protein mass spectrometry ; Information retrieval ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Aditya Savara | Eric A. Walker
Source: ChemCatChem (article, 2020-08-25)
Field: Materials Science — Materials Chemistry

CheKiPEUQ Intro 1: Bayesian Parameter Estimation Considering Uncertainty or Error from both Experiments and Theory** Abstract A common goal is extraction of physico‐chemical parameter values such as pre‐exponentials and activation energies from experiment. Ever increasing knowledge from experiments and computations is enabling semi‐quantitative prior predictions of such values. When prior knowledge of physically realistic ranges is available, a method named Bayesian parameter estimation (BPE) enables more physically realistic parameter estimation relative to unsophisticated fitting by seeking the most probable value when considering together the uncertainties from prior knowledge, experimental data, and approximations in the model. An impediment to widespread use of BPE is a lack of understanding, training, and user‐friendly software. Along with this invited publication, a general software package for BPE is being released that is user‐friendly and that does not require understanding of the math behind the methodology. Two previously unpublished catalysis science examples are provided along with considerations and guidelines for successful application of BPE. Following this work, BPE can become more widespread to enable extraction of physically meaningful parameter values. Bayesian probability ; Computer science ; Software ; Computation ; Estimation theory ; Exponential function ; Experimental data ; Work (physics) ; Approximate Bayesian computation ; Estimation ; Value (mathematics) ; Machine learning ; Data mining ; Artificial intelligence ; Algorithm ; Statistics ; Mathematics ; Physics ; Engineering ; Systems engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion