jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. N. Kiselyova | Victor Dudarev | V. V. Ryazanov | O. V. Sen’ko | A. A. Dokukin
Source: Perspektivnye Materialy (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predictions of chalcospinels with composition ABCX4 (X – S or Se) New chalcospinels of the most common compositions were predicted: AIBIIICIVX4 (X — S or Se) and AIIBIIICIIIS4 (A, B, and C are various chemical elements). They are promising for the search for new materials for magneto-optical memory elements, sensors and anodes in sodium-ion batteries. The parameter “a” values of their crystal lattice are estimated. When predicting only the values of chemical elements properties were used. The calculations were carried out using machine learning programs that are part of the information-analytical system developed by the authors (various ensembles of algorithms of: the binary decision trees, the linear machine, the search for logical regularities of classes, the support vector machine, Fisher linear discriminant, the k-nearest neighbors, the learning a multilayer perceptron and a neural network), — for predicting chalcospinels not yet obtained, as well as an extensive family of regression methods, presented in the scikit-learn package for the Python language, and multilevel machine learning methods that were proposed by the authors — for estimation of the new chalcospinels lattice parameter value). The prediction accuracy of new chalcospinels according to the results of the cross-validation is not lower than 80%, and the prediction accuracy of the parameter of their crystal lattice (according to the results of calculating the mean absolute error (when cross-validation in the leave-one-out mode)) is ± 0.1 Å. The effectiveness of using multilevel machine learning methods to predict the physical properties of substances was shown. Artificial neural network ; Python (programming language) ; Artificial intelligence ; Support vector machine ; Multilayer perceptron ; Computer science ; Machine learning ; Linear discriminant analysis ; Lattice (music) ; Binary number ; Linear regression ; Perceptron ; Algorithm ; Mathematics ; Physics Crystal Structures and Properties ; Solid-state spectroscopy and crystallography
Authors: Julia Maria Westermayr
Source: University of Vienna (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for excited-state molecular dynamics simulations Durch das Aufeinandertreffen der künstlichen Intelligenz und Quantenchemie haben sich eine Vielzahl neuer Wege eröffnet, von denen besonders das Forschungsgebiet der Photochemie profitieren kann. Das Zusammenspiel von Licht, Elektronen und Atomkernen ist jedoch so komplex, dass der hohe Rechenaufwand der mit einer akkuraten Beschreibung verbunden ist, dazu führte, dass dieses Forschungsgebiet bis zu Beginn dieser Arbeit von maschinellem Lernen weitgehend unberührt blieb. Das grundlegende Ziel dieser Arbeit ist es daher, die Techniken des maschinellen Lernens für Photodynamiksimulationen anzuwenden und weiterzuentwickeln, um eine Vielzahl von molekularen elektronischen Zuständen unterschiedlicher Spinmultiplizitäten, deren Ableitungen und Kopplungen zu beschreiben. Im Mittelpunkt dieser Arbeit steht die Identifizierung der Hauptprobleme, die einer Realisierung von effizienten Modellen des maschinellen Lernens für die molekulare Photochemie im Weg stehen, sowie deren Überwindung: (1) Eigenschaften, die sich durch die Wechselwirkung unterschiedlicher elektronischer Zustände ergeben, sind durch ein willkürliches Vorzeichen gekennzeichnet, das eine sinnvolle, direkte Anwendung von maschinellen Lerntechniken verhindert. Die Willkür bei Vorzeichen kann mit einem in dieser Arbeit entwickelten Phasenkorrektur-Algorithmus beseitigt werden. Als Alternative dazu ist eine modell-intrinsische Lösung in einem neuen Ansatz namens SchNarc implementiert. (2) Viele quantenchemische Programme können keine Kopplungen zwischen elektronischen Zuständen berechnen. Daher werden Näherungen auf Basis maschinell gelernter Potentiale bereitgestellt. (3) Aufgrund der fehlenden Studien zur Beschreiben der Photochemie mit maschinellem Lernen, bleiben viele Fragen offen, unter anderem, welche Algorithmen und molekularen Repräsentationen für angeregte Zustände am besten geeignet sind. Aus diesem Grund werden mehrere solche Modell des maschinellen Lernens verglichen. (4) Aufgrund des hohen Rechenaufwands quantenchemischer Methoden wird eine kostengünstige Trainingssatzgenerierung benötigt. Daher wird ein effizienter Algorithmus für den elektronischen Grundzustand für die Photochemie erweitert. Die Möglichkeiten und Präzision all dieser neuartigen Strategien und Methoden wird anhand kleiner Moleküle untersucht. Lange Photodynamiksimulationen werden mit neuronale Netzen für das Methylenimmonium-Kation ermöglicht. Die komplexe Photochemie der Aminosäure Tyrosin fasst die Herausforderungen für Methoden des maschinellen Lernens zusammen und demonstriert dessen Möglichkeiten, angeregte elektronische Zustände von Molekülen zu beschreiben. Physics ; Computer science Machine Learning in Materials Science ; Various Chemistry Research Topics ; Spectroscopy and Quantum Chemical Studies
Authors: Xiaoyong Cao | Pu Tian
Source: Preprints.org (preprint, 2020-12-03)
Field: Materials Science — Materials Chemistry

"Dividing and Conquering" and "Caching" in Molecular Modeling Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. To access interesting behavior of complex molecular systems in a wide range of spatial and temporal scales, the molecular modeling community has invested tremendous efforts on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in Metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. With introduction of machine learning techniques, many new developments, particularly those based on deep learning, have been implemented to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We recently developed the generalized solvation free energy theory , which suggests a third class of algorithm that facilitate molecular modeling through partially transferable in resolution "caching" of local free energy landscape. Connections and potential interactions among these three algorithmic directions are discussed. This brief review is on both the traditional development and the application of machine learning in molecular modeling from the perspective of "dividing and conquering" and "caching", with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms in this regard. Computer science ; Divide and conquer algorithms ; Metadynamics ; Theoretical computer science ; Computational science ; Algorithm ; Molecular dynamics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Jenke Scheen | Wilson Wu | Antonia S. J. S. Mey | Paolo Tosco | Mark Mackey | Julien Michel
Source: ChemRxiv (preprint, 2020-05-29)
Field: Materials Science — Materials Chemistry

A Hybrid Alchemical Free Energy and Machine Learning Methodology for the Calculation of Absolute Hydration Free Energies of Small Molecules A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database, and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration, and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations, and to flag molecules which will benefit the most from bespoke forcefield parameterisation efforts. Bespoke ; Range (aeronautics) ; Computer science ; Set (abstract data type) ; Fraction (chemistry) ; Energy (signal processing) ; Training set ; Molecule ; Measure (data warehouse) ; Artificial intelligence ; Chemistry ; Materials science ; Mathematics ; Physics ; Data mining ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Scott E. Denmark | Andrew F. Zahrt | William Darrow | Brennan Rose | Jeremy Henle
Source: ChemRxiv (preprint, 2020-11-16)
Field: Materials Science — Materials Chemistry

Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next The application of machine learning (ML) to problems in homogeneous catalysis has emerged as a promising avenue for catalyst optimization. An important aspect of such optimization campaigns is determining which reactions to run at the outset of experimentation and which future predictions are the most reliable. Herein, we explore methods for these two tasks in the context of our previously developed chemoinformatics workflow. First, different methods for training set selection are compared, including algorithmic selection and selection informed by unsupervised learning methods. Next, an array of different metrics for assessment of prediction confidence are examined in multiple catalyst manifolds. These approaches will inform future computer-guided studies to accelerate catalyst selection and reaction optimization. Finally, this work demonstrates the generality of the Average Steric Occupancy (ASO) and Average Electronic Indicator Field (AEIF) descriptors in their application to transition metal catalysts for the first time. Cheminformatics ; Generality ; Workflow ; Selection (genetic algorithm) ; Computer science ; Context (archaeology) ; Set (abstract data type) ; Field (mathematics) ; Machine learning ; Artificial intelligence ; Database ; Chemistry ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Sirodom Mongkhonthanaphon | Yachai Limpiyakorn
Source: International Journal of Machine Learning and Computing (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A Deep Neural Network for Pixel-Wise Classification of Titanium Microstructure In recent decades, deep learning has been widely used for automatically classifying images with high accuracy. Semantic segmentation is a type of deep learning that focuses on classifying every pixel into classes. In Metallurgy domain, Titanium and its alloys are considered an ideal biomaterial that suit for implant material. In this research, deep learning is thus introduced as a means to automate the pixel-wise classification of Titanium microstructure in order to reduce the time and uncertainties during human inspection for quality control. The method applies a semantic segmentation technique, fully convolutional neural network, implemented with the U-net architecture. The proposed approach has investigated the integration of Fine-tuning to the U-net architecture for improving the model performance. The dataset of Ti-6A1-4V microstructure images is augmented using elastic deformations. The results report slightly increase of accuracy, while the training time is much faster than training from scratch. Computer science ; Artificial neural network ; Pixel ; Microstructure ; Artificial intelligence ; Titanium ; Deep neural networks ; Pattern recognition (psychology) ; Computer vision ; Composite material ; Materials science ; Metallurgy Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Rama K. Vasudevan | Maxim Ziatdinov | Lukáš Vlček | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2020-05-04)
Field: Materials Science — Materials Chemistry

Off-the-shelf deep learning is not enough: parsimony, Bayes and causality Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level performance and ushered in a new wave of "AI." Buoyed by these successes, researchers in the physical sciences have made steady progress in incorporating deep learning into their respective domains. However, such adoption brings substantial challenges that need to be recognized and confronted. Here, we discuss both opportunities and roadblocks to implementation of deep learning within materials science, focusing on the relationship between correlative nature of machine learning and causal hypothesis driven nature of physical sciences. We argue that deep learning and AI are now well positioned to revolutionize fields where causal links are known, as is the case for applications in theory. When confounding factors are frozen or change only weakly, this leaves open the pathway for effective deep learning solutions in experimental domains. Similarly, these methods offer a pathway towards understanding the physics of real-world systems, either via deriving reduced representations, deducing algorithmic complexity, or recovering generative physical models. However, extending deep learning and "AI" for models with unclear causal relationship can produce misleading and potentially incorrect results. Here, we argue the broad adoption of Bayesian methods incorporating prior knowledge, development of DL solutions with incorporated physical constraints, and ultimately adoption of causal models, offers a path forward for fundamental and applied research. Most notably, while these advances can change the way science is carried out in ways we cannot imagine, machine learning is not going to substitute science any time soon. Deep learning ; Artificial intelligence ; Causality (physics) ; Computer science ; Cognitive science ; Machine learning ; Paradigm shift ; Data science ; Causal model ; Generative grammar ; Epistemology ; Psychology ; Mathematics Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning ; Topic Modeling
Authors: Qingfeng Zeng | Yong Wei Gao | Kang Guan | Jiantao Liu | Zhiqiang Feng
Source: Research Square (preprint, 2020-08-12)
Field: Materials Science — Materials Chemistry

Machine Learning and a Computational Fluid Dynamic Approach to Estimate Phase Composition of Chemical Vapor Deposition Boron Carbide Abstract Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl 2 and BCl 3 produce the most boron atoms, while C 2 H 4 and CH 4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron-carbon ratio and provide a new design solution for other multi-element systems. Boron ; Boron carbide ; Chemical vapor deposition ; Deposition (geology) ; Carbon fibers ; Computational fluid dynamics ; Materials science ; Phase (matter) ; Gas composition ; Chemical composition ; Carbide ; Work (physics) ; Composition (language) ; Analytical Chemistry (journal) ; Volumetric flow rate ; Chemistry ; Thermodynamics ; Metallurgy ; Nanotechnology ; Composite material ; Environmental chemistry ; Composite number ; Organic chemistry ; Physics ; Geology Thermal properties of materials ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Haoyang Hu | Zhihong Yuan
Source: BMC Chemical Engineering (article, 2020-04-07)
Field: Materials Science — Materials Chemistry

Hard-threshold neural network-based prediction of organic synthetic outcomes Abstract Retrosynthetic analysis is a canonical technique for planning the synthesis route of organic molecules in drug discovery and development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is far from completing this step independently. Previous studies attempted to apply a neural network to forward reaction prediction, but the accuracy was not satisfying. Through using the Edit Vector-based description and extended-connectivity fingerprints to transform the reaction into a vector, this study focuses on the update of the neural network to improve the template-based forward reaction prediction. Hard-threshold activation and the target propagation algorithm are implemented by introducing mixed convex-combinatorial optimization. Comparative tests were conducted to explore the optimal hyperparameter set. Using 15,000 experimental reaction data extracted from granted United States patents, the proposed hard-threshold neural network was systematically trained and tested. The results demonstrated that a higher prediction accuracy was obtained than that for the traditional neural network with backpropagation algorithm. Some successfully predicted reaction examples are also briefly illustrated. Artificial neural network ; Computer science ; Backpropagation ; Set (abstract data type) ; Artificial intelligence ; Hyperparameter ; Tree (set theory) ; Algorithm ; Feedforward neural network ; Machine learning ; Data mining ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Nguyễn Ngọc Sơn | Huy Anh Ho Pham | Duy Khanh Nguyen
Source:  (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Modified Feedback Error Learning Control of Shape Memory Alloys Actuator using Evolutionary Neural Network In this paper, a modified feedback error learning approach (called MFEL) is proposed for a nonlinear system. In MFEL, an inverse evolutionary neural (IEN) model that dynamically identifies offline all nonlinear features of the nonlinear system, provides the initial value of a feedforward compensator. A PID feedback control is combined with a feedforward compensator to eliminate the steady-state error and guarantee the global asymptotic stability of the overall system. The learning is based on the feedback error signal is employed. An adaptive backpropagation (aBP) with self-adaptive learning rate based Sugeno Fuzzy logic is developed and employed in the MFEL to adapt well to disturbances and dynamic variations in its operation. To prove the effectiveness of the proposed MFEL controller, first, the benchmark nonlinear SISO system is used to evaluate the controller. Then, the experimental shape memory alloys (SMA) actuator is set up to test the controller. The simulation and experimental results proved that the proposed controller provides better results compared to the feedback control. Control theory (sociology) ; Feed forward ; Benchmark (surveying) ; Computer science ; Controller (irrigation) ; Artificial neural network ; Nonlinear system ; Backpropagation ; PID controller ; Feedforward neural network ; Control engineering ; Artificial intelligence ; Control (management) ; Engineering ; Temperature control Shape Memory Alloy Transformations
Authors: Rama K. Vasudevan | Maxim Ziatdinov | Lukáš Vlček | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2020-05-04)
Field: Materials Science — Materials Chemistry

Off-the-shelf deep learning is not enough: parsimony, Bayes and\n causality Deep neural networks ("deep learning") have emerged as a technology of choice\nto tackle problems in natural language processing, computer vision, speech\nrecognition and gameplay, and in just a few years has led to superhuman level\nperformance and ushered in a new wave of "AI." Buoyed by these successes,\nresearchers in the physical sciences have made steady progress in incorporating\ndeep learning into their respective domains. However, such adoption brings\nsubstantial challenges that need to be recognized and confronted. Here, we\ndiscuss both opportunities and roadblocks to implementation of deep learning\nwithin materials science, focusing on the relationship between correlative\nnature of machine learning and causal hypothesis driven nature of physical\nsciences. We argue that deep learning and AI are now well positioned to\nrevolutionize fields where causal links are known, as is the case for\napplications in theory. When confounding factors are frozen or change only\nweakly, this leaves open the pathway for effective deep learning solutions in\nexperimental domains. Similarly, these methods offer a pathway towards\nunderstanding the physics of real-world systems, either via deriving reduced\nrepresentations, deducing algorithmic complexity, or recovering generative\nphysical models. However, extending deep learning and "AI" for models with\nunclear causal relationship can produce misleading and potentially incorrect\nresults. Here, we argue the broad adoption of Bayesian methods incorporating\nprior knowledge, development of DL solutions with incorporated physical\nconstraints, and ultimately adoption of causal models, offers a path forward\nfor fundamental and applied research. Most notably, while these advances can\nchange the way science is carried out in ways we cannot imagine, machine\nlearning is not going to substitute science any time soon.\n Deep learning ; Artificial intelligence ; Causality (physics) ; Computer science ; Cognitive science ; Machine learning ; Paradigm shift ; Data science ; Generative grammar ; Causal model ; Epistemology ; Psychology ; Mathematics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI)