jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jürgen Köfinger | Gerhard Hummer
Source: ChemRxiv (preprint, 2021-08-09)
Field: Materials Science — Materials Chemistry

Empirical optimization of molecular simulation force fields by Bayesian inference The demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex systems are studied over longer times. One way to meet these growing demands is to hand over the learning of force fields and their parameters to machines in a systematic (semi-)automatic manner. Doing so, we can take full advantage of exascale computing, the increasing availability of experimental data, and advances in quantum-mechanial computations and the calculation of experimental observables from molecular ensembles. Here, we discuss and illustrate the challenges we face in this endeavor and explore a way forward by adapting the Bayesian inference of ensembles (BioEn) method [Hummer and Köfinger, J. Chem. Phys. (2015)] for force field parameterization. In the Bayesian inference of force fields (BioFF) method developed here, the optimization problem is regularized by a simplified prior on the force field parameters and an entropic prior acting on the ensemble. The latter compensates for the unavoidable over-simplifications in the parameter prior. We determine optimal force field parameters using an iterative predictor-corrector approach, in which we run simula- tions, determine the reference ensemble using the weighted histogram analysis method (WHAM), and optimize the BioFF posterior. We illustrate this approach for a simple polymer model, using the distance between two labeled sites as the experimental observable. By systematically resolving force field issues, the BioFF corrections extend to observables not included in ensemble reweighting. We envision future force field optimization as a formalized, systematic, and (semi-)automatic machine learning effort that incorporates a wide range of data from experiment and high-level quantum chemical calculations and takes advantage of exascale computing resources. Force field (fiction) ; Observable ; Computer science ; Inference ; Bayesian inference ; Field (mathematics) ; Bayesian probability ; Approximate Bayesian computation ; Bayesian optimization ; Statistical physics ; Computation ; Artificial intelligence ; Algorithm ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Jürgen Köfinger | Gerhard Hummer
Source:  (preprint, 2021-07-29)
Field: Materials Science — Materials Chemistry

Empirical optimization of molecular simulation force fields by Bayesian inference The demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex systems are studied over longer times. One way to meet these growing demands is to hand over the learning of force fields and their parameters to machines in a systematic (semi-)automatic man- ner. Doing so, we can take full advantage of exascale computing, the increasing availability of experimental data, and advances in quantum-mechanial computations and the calculation of experimental observables from molecular ensembles. Here, we discuss and illustrate the challenges we face in this endeavor and explore a way forward by adapting the Bayesian inference of ensembles (BioEn) method [Hummer and K ̈ofinger, J. Chem. Phys. (2015)] for force field parameterization. In the Bayesian inference of force fields (BioFF) method developed here, the optimization problem is regularized by a simplified prior on the force field parameters and an entropic prior act- ing on the ensemble. The latter compensates for the unavoidable over-simplifications in the parameter prior. We determine optimal force field parameters using an iterative predictor-corrector approach, in which we run simula- tions, determine the reference ensemble using the weighted histogram analysis method (WHAM), and optimize the BioFF posterior. We illustrate this approach for a simple polymer model, using the distance between two labeled sites as the experimental observable. By systematically resolving force field issues, the BioFF corrections extend to observables not included in ensemble reweighting. We envision future force field optimization as a formalized, systematic, and (semi-)automatic machine learning effort that incorporates a wide range of data from experiment and high-level quantum chemical calculations and takes advantage of exascale computing resources. Force field (fiction) ; Observable ; Computer science ; Inference ; Bayesian inference ; Field (mathematics) ; Approximate Bayesian computation ; Bayesian probability ; Bayesian optimization ; Statistical physics ; Artificial intelligence ; Algorithm ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Shiyan Fang | Junmeng Li | Yan Wang | Yan-ru Zhao | Keqiang Yu
Source: Research Square (preprint, 2021-08-05)
Field: Chemistry — Analytical Chemistry

Surface Enhanced Raman Scattering (SERS) Spectroscopy Combined With Chemical Imaging Analysis For The Early Detection of Apple Valsa Canker Canker ; Materials science ; Raman scattering ; Spectroscopy ; Raman spectroscopy ; Optics ; Botany ; Biology ; Physics ; Astronomy Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaoting Zhong
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning enabled electron microscopy data analysis tool This software is for materials image and spectral data analysis. It consists machine learning models that can segment a large amount of image data in a high-throughput way, and an interface for image data and spectral data analysis. Electron microscope ; Microscopy ; Computer science ; Artificial intelligence ; Materials science ; Optics ; Physics Machine Learning in Materials Science
Authors: А. А. Хлыбов | Yu. G. Kabaldin | M. S. Anosov | D. A. Ryabov | Д. А. Шатагин
Source: Industrial laboratory Diagnostics of materials (article, 2021-09-24)
Field: Materials Science — Materials Chemistry

Study of the low cycle fatigue of 12Cr18Ni10Ti steel based on fractal analysis and artificial intelligence approaches The evolution of the structure and assessment of the age limit of steel 12Cr18Ni10Ti upon fatigue loading is considered using neural network modeling and approaches of fractal analysis of the microstructure. An algorithm for processing images of the microstructures has been developed to improve their quality. An indicator of the fractal dimension of the image is used as a quantitative indicator for assessing the evolution of the microstructure of the surface metal layer. A quantitative assessment of the structures at different stress amplitudes is carried out in a wide range of low temperatures using the fractal dimension index. Correlation of the fractal dimension index with the run of the sample material is shown. The appearance of the main crack was observed in the range of 0.7 - 0.8 from the number of cycles to failure, after which the crack growth rate increased. At a lower temperature, the main crack is formed later, but further loading results in a higher crack growth rate. Formation of the secondary phases in austenitic steel at a lower temperature occurred at earlier stages than that at a temperature of t = +20°C, which led to hardening of the material. An artificial neural network (ANN) has been developed and trained for assessing structural changes in metal proceeding from the fractal dimensionality of the microstructure images at different stages of fatigue loading. The developed neural network made it possible to estimate with a sufficiently high accuracy the number of cycles before damage of the sample and the residual life of the material. Thus, the developed ANN can be used to assess the current state of the material in a wide range of low temperatures. Fractal dimension ; Materials science ; Microstructure ; Artificial neural network ; Fractal analysis ; Fractal ; Paris' law ; Structural engineering ; Composite material ; Fracture mechanics ; Computer science ; Artificial intelligence ; Crack closure ; Mathematics ; Engineering ; Mathematical analysis Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Surface Treatment and Residual Stress
Authors: Julia Westermayr | Michael Gastegger | Dora Vörös | Lisa Panzenboeck | Florian Joerg | Leticia González | Philipp Marquetand
Source: arXiv (Cornell University) (preprint, 2021-08-09)
Field: Materials Science — Materials Chemistry

Roaming leads to amino acid photodamage: A deep learning study of tyrosine Although the amino acid tyrosine is among the main building blocks of life, its photochemistry is not fully understood. Traditional theoretical simulations are neither accurate enough, nor computationally efficient to provide the missing puzzle pieces to the experimentally observed signatures obtained via time-resolved pump-probe spectroscopy or mass spectroscopy. In this work, we go beyond the realms of possibility with conventional quantum chemical methods and develop as well as apply a new technique to shed light on the photochemistry of tyrosine. By doing so, we discover roaming atoms in tyrosine, which is the first time such a reaction is discovered in biology. Our findings suggest that roaming atoms are radicals that could play a fundamental role in the photochemistry of peptides and proteins, offering a new perspective. Our novel method is based on deep learning, leverages the physics underlying the data, and combines different levels of theory. This combination of methods to obtain an accurate picture of excited molecules could shape how we study photochemical systems in the future and how we can overcome the current limitations that we face when applying quantum chemical methods. Roaming ; Tyrosine ; Amino acid ; Computer science ; Spectroscopy ; Quantum ; Excited state ; Quantum chemical ; Chemistry ; Molecule ; Biological system ; Physics ; Biology ; Biochemistry ; Telecommunications ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Alexis M. Fenton | Fikile R. Brushett
Source: ChemRxiv (preprint, 2021-07-06)
Field: Chemistry — Electrochemistry

Using Voltammetry Augmented with Physics-Based Modeling and Bayesian Hypothesis Testing to Estimate Electrolyte Composition Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in electrolytes and as such has been used in numerous fields of study. Recently, automation has been introduced into voltammetric analyses to extend their capabilities (e.g., Bayesian parameter estimation, compound identification <i>via</i> machine learning); however, opportunities exist to enable more versatile methods across a wider range of electrolyte and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of electroactive species in multicomponent electrolytes across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five <i>N</i>-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labeled an electrolyte containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating its ability to identify electroactive constituents of a multicomponent solution. Finally, we identify areas of further improvement (e.g., achieving greater detection accuracy) and future applications to potentially enhance <i>in situ</i> or <i>operando</i> diagnostic workflows. Electrolyte ; Voltammetry ; Phenothiazine ; Cyclic voltammetry ; Bayesian probability ; Protocol (science) ; Computer science ; Bayesian inference ; Biological system ; Electrochemistry ; Materials science ; Chemistry ; Artificial intelligence ; Electrode ; Physical chemistry Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Machine Learning in Materials Science
Authors: Haixin Wei | Zekai Zhao | Ray Luo
Source: arXiv (Cornell University) (preprint, 2021-05-18)
Field: Materials Science — Materials Chemistry

Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulation Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, thus neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level-set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows that the machine-learned SES overlaps with the classical SES on almost all situations. We also implemented the machine-learned SES into the Amber/PBSA program to study its performance on reaction field energy calculation. The analysis shows that the two sets of reaction field energies are highly consistent with 1% deviation on average. Given its level-set formulation, we expect the machine-learned SES to be applied in molecular simulations that require either surface derivatives or high efficiency on parallel computing platforms. Surface (topology) ; Computer science ; Field (mathematics) ; Set (abstract data type) ; Interface (matter) ; Process (computing) ; Implementation ; Computational science ; Machine learning ; Artificial intelligence ; Algorithm ; Parallel computing ; Mathematics ; Geometry ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Software Engineering Research
Authors: Hea‐Lim Park | Min‐Hwi Kim | Hyungjin Kim | Sin‐Hyung Lee
Source: Advanced Electronic Materials (article, 2021-08-01)
Field: Materials Science — Polymers and Plastics

Self‐Selective Organic Memristor by Engineered Conductive Nanofilament Diffusion for Realization of Practical Neuromorphic System (Adv. Electron. Mater. 8/2021) Organic Memristor An unprecedented structure of an organic memristor with high self-selectivity is developed through systematically engineering the conductive nanofilament diffusion in the polymer media by Sin-Hyung Lee and colleagues in article number 2100299. The maximum size of our organic memristor arrays is found to be more than 1 Mbits, and the neural networks based on the device showed reliable recognition performance. Memristor ; Neuromorphic engineering ; Materials science ; Realization (probability) ; Nanotechnology ; Conductive polymer ; Electrical conductor ; Diffusion ; Optoelectronics ; Artificial neural network ; Polymer ; Computer science ; Electronic engineering ; Artificial intelligence ; Physics ; Engineering ; Composite material ; Quantum mechanics ; Mathematics Conducting polymers and applications ; Advanced Memory and Neural Computing ; Polydiacetylene-based materials and applications
Authors: An Su | Ling Wang | Xinqiao Wang | Chengyun Zhang | Hongliang Duan
Source: ChemRxiv (preprint, 2021-02-16)
Field: Materials Science — Materials Chemistry

Reproducing the Invention of a Named Reaction: Predicting Unseen Chemical Reactions via Zero-Shot Learning &lt;div&gt; The study focuses on the proof-of-concept that the human invention of a named reaction can be reproduced by the zero-shot learning version of transformer.&lt;/div&gt;&lt;div&gt;While state-of-art reaction prediction machine learning models can predict chemical reactions through the transfer learning of thousands of training samples with the same reaction types as the ones to predict, how to prepare the models to predict truly "unseen" reactions remains a question. We aim to equip the transformer model with the ability to predict unseen reactions following the concept of "zero-shot learning". To find what kind of auxiliary information is needed, we reproduce the human invention of the Chan-Lam coupling reaction where the inventor was inspired by two existing reactions---Suzuki reaction and Barton's bismuth arylation reaction. After training with the samples from these two reactions as well as the USPTO dataset, the transformer model can pre-dict the Chan-Lam coupling reaction with 55.7% top-1 accuracy which is a huge im-provement comparing to 17.2% from the model trained with the USPTO dataset only. Our model also mimics the later stage of this history where the initial case of Chan-Lam coupling reaction was generalized to a wide range of reactants and reagents via the "one-shot learning" approach. The results of this study show that having existing reactions as auxiliary information can help the transformer predict unseen reactions and providing just one or few samples of the unseen reaction can boost the model's gener-alization ability.&lt;br&gt;&lt;/div&gt; Transformer ; Artificial intelligence ; Computer science ; Coupling reaction ; Machine learning ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Genetics, Bioinformatics, and Biomedical Research
Authors: Ben Symons | Michael Bane | Paul L. A. Popelier
Source:  (preprint, 2021-06-22)
Field: Materials Science — Materials Chemistry

DL_FFLUX: a parallel, quantum chemical topology force field DL_FFLUX performs molecular dynamics for flexible molecules endowed with polarisable QTAIM atomic multipole moments, predicted by the machine learning method Gaussian Process Regression. Newly optimised and parallelised using domain-decomposition MPI, DL_FFLUX now operates in reasonable time frames. DL_FFLUX is delivered as an add-on to the widely distributed molecular dynamics code DL_POLY 4.08. For the systems studied here (10**3-10**5 atoms), DL_FFLUX adds minimal computational cost to the standard DL_POLY package. The parallel DL_FFLUX preserves the quality of the scaling of the MPI implementation in standard DL_POLY. For the first time it is feasible to use the full capability of DL_FFLUX to study systems that are large enough to be of real world interest. For example, a fully flexible, high-rank polarised (up to and including quadrupole moments) 1 ns simulation of a system of 10,125 atoms (3,375 water molecules) takes 30 hours (wall time) on 18 cores. Multipole expansion ; Computer science ; Fast multipole method ; Molecular dynamics ; Quantum ; Force field (fiction) ; Computational science ; Topology (electrical circuits) ; Algorithm ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Xiaoqiong Cao
Source: Scholarworks (University of Massachusetts Amherst) (article, 2021-04-01)
Field: Chemistry — Analytical Chemistry

Interaction of Titanium Dioxide Nanoparticles (NPs) with Food Components and Mechanistic Studies of Adverse Effects Induced by Titanium Dioxide NPs New computing paradigms are highly demanded in the “Big Data” era to efficiently process, store and extract useful information from overwhelmingly rich amount of data. New computing systems based on large scale memristor circuits emerges as a very promising candidate due to its capability to both store and process information, thus eliminating the von Neumann bottleneck in the conventional complementary metal oxide semiconductor (CMOS) based computers. As the lateral scaling of the device geometry approaching its physical limit, three-dimensional stacking of multiple device layers becomes necessary to further increase the packing density. Moreover, innovations in the 3D circuits design can also provide the memristor system with low power consumptions, low latencies and high throughput. In this work, systematic study based on device engineering, circuit design and fabrication process development was carried out. Highly parallel kernel operations were experimentally demonstrated in the 3D memristor circuits for deep neural network and video processing applications. Finally, a transfer printing method for 2D materials was invented as an enabling technology for future 3D hybrid circuits. Titanium dioxide ; Nanoparticle ; Materials science ; Chemistry ; Nanotechnology ; Metallurgy Dye analysis and toxicity
Authors: Wei-Chih Chen | Yogesh K. Vohra | Cheng-Chien Chen
Source: arXiv (Cornell University) (preprint, 2021-11-25)
Field: Materials Science — Materials Chemistry

Discovering Superhard B-N-O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions We search for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from evolutionary algorithm. We first use cohesive energy to evaluate the thermodynamic stability of varying B$_x$N$_y$O$_z$ compositions, and then gradually focus on compositional regions with high cohesive energy and high hardness. The results converge quickly after a few iterations. Our resulting ML models show that B$_{x+2}$N$_{x}$O$_{3}$ compounds with $x \geq 3$ (like B$_5$N$_3$O$_3$, B$_6$N$_4$O$_3$, etc.) are potentially superhard and thermodynamically favorable. Our meta-GGA density functional theory calculations indicate that these materials are also wide bandgap ($\ge 4.4$ eV) insulators, with the valence band maximum related to the $p$-orbitals of nitrogen atoms near vacant sites. This study demonstrates that an iterative method combining ML and ab initio simulations provides a powerful tool for discovering novel materials. Atomic orbital ; Ab initio ; Stability (learning theory) ; Density functional theory ; Band gap ; Materials science ; Crystal structure prediction ; Energy (signal processing) ; Total energy ; Valence (chemistry) ; Crystal structure ; Crystallography ; Computer science ; Machine learning ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Optoelectronics Boron and Carbon Nanomaterials Research ; Advanced ceramic materials synthesis ; Machine Learning in Materials Science
Authors: Sajjad Abdollahramezani | Omid Hemmatyar | Hossein Taghinejad | Muliang Zhu | Alexander Gallmon | Ali Adibi
Source: Conference on Lasers and Electro-Optics (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reconfigurable near-infrared metasurfaces using phase-change materials We experimentally demonstrate a tunable hybrid metasurface benefiting from phase-change materials and plasmon hybridization for non-volatile optical modulation. We also leverage machine learning algorithms to study the effect of structural parameters on the optical performance. Plasmon ; Leverage (statistics) ; Phase change ; Modulation (music) ; Phase modulation ; Materials science ; Infrared ; Optoelectronics ; Computer science ; Phase (matter) ; Optics ; Electronic engineering ; Engineering physics ; Artificial intelligence ; Physics ; Acoustics ; Engineering Metamaterials and Metasurfaces Applications ; Phase-change materials and chalcogenides ; Plasmonic and Surface Plasmon Research