jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenjun Wang | Nicolette G. Moreau | Yingfang Yuan | Paul R. Race | Wei Pang
Source: Computational Materials Science (article, 2019-06-19)
Field: Materials Science — Polymers and Plastics

Towards machine learning approaches for predicting the self-healing efficiency of materials Self-healing materials with an inherent repair mechanism have been widely studied. However, the self-healing efficiencies of most materials can only be measured by laboratory-based experiments, which can be time consuming and expensive. Inspired by modern machine learning approaches, we are interested in predicting the self-healing efficiency of new bio-hybrid materials, as part of our ongoing EPSRC funded “Manufacturing Immortality” project. By modelling existing experimental data, predictive models can be built to forecast self-healing efficiency. This has the potential to reduce the time input required by laboratory experiments, guide material and component selection, and inform hypotheses, thereby facilitating the design of novel self-healing materials. In this position paper, we first present preliminary knowledge and quantitative definitions of the self-healing efficiency of materials. We then demonstrate several widely used machine learning approaches and review an experimental case of predictive modelling based on neural networks. Furthermore, and aiming to expedite self-healing material development, we propose an on-line ensemble learning framework as the whole system model for the optimization of predictive computational models. Finally, the rationality of our on-line ensemble learning framework is experimentally studied and validated. Computer science ; Machine learning ; Artificial intelligence ; Component (thermodynamics) ; Self-healing ; Rationality ; Predictive modelling ; Biochemical engineering ; Industrial engineering ; Engineering Polymer composites and self-healing ; Fuel Cells and Related Materials ; Corrosion Behavior and Inhibition
Authors: Emi Minamitani | Masayoshi Ogura | Satoshi Watanabe
Source: Applied Physics Express (article, 2019-07-30)
Field: Materials Science — Materials Chemistry

Simulating lattice thermal conductivity in semiconducting materials using high-dimensional neural network potential Abstract We demonstrate that a high-dimensional neural network potential (HDNNP) can predict the lattice thermal conductivity of semiconducting materials with an accuracy that is comparable to that of density functional theory (DFT) calculation. After a training procedure based on force, the root mean square error between the forces predicted by HDNNP and DFT is less than 40 meV Å −1 . As typical examples, we present the results of Si and GaN bulk crystals. The deviation from the thermal conductivity calculated using DFT is within 1% at 200 to 500 K for Si and within 5.4% at 200 to 1000 K for GaN. Thermal conductivity ; Artificial neural network ; Density functional theory ; Lattice (music) ; Conductivity ; Root mean square ; Mean squared error ; Square lattice Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Aditya Krishna Menon | James A. Thompson-Colón | Newell R. Washburn
Source: Frontiers in Materials (article, 2019-05-08)
Field: Materials Science — Polymers and Plastics

Hierarchical Machine Learning Model for Mechanical Property Predictions of Polyurethane Elastomers From Small Datasets Polyurethanes are a broad class of material that finds application in coatings, foams, and solid elastomers. The urethane chemistry allows a diversity of monomers to be used, and prediction of mechanical properties, which are determined by complex interplay between monomer chemistry and chain architecture, is an unresolved challenge. Urethanes are based on aromatic or cyclic isocyanates and linear or branched polyols, and polymerization results in linear chains for bifunctional monomers or branched chains for multifunctional monomers. Strong intermolecular interactions between aromatic groups result in the formation of hard-segment domains that generate physical crosslinks between disorganized rubbery domains and anchor the material microstructure, contributing to resistance to deformation. Here, a general hierarchical machine learning (HML) model for predicting the stress-at-break, strain-at-break, and Tan δ for thermoplastic and thermoset polyurethanes is presented. The algorithm was trained on a library of 18 polymers with different diisocyanates, bifunctional or trifunctional polyols, and NCO:OH index. HML reduces data requirements through robust embedding of domain knowledge and surrogate data in a middle layer that bridges input variables (composition) and output responses (mechanical properties). In this work, the middle layer included information on overall polymer composition, predictions of chain architecture derived from Monte Carlo simulations of polymerization, information on interchain interactions from empirically derived molecular potentials and shifts in infrared (IR) spectroscopy absorbances. The HML predictions are shown to be more accurate than those from a Random Forest model directly relating composition and properties, suggesting that embedding domain knowledge provides significant advantages in predicting the properties of complex material systems based on small datasets. Materials science ; Monomer ; Elastomer ; Polymer ; Polymerization ; Polymer chemistry ; Thermoplastic elastomer ; Polymer science ; Copolymer ; Composite material Polymer composites and self-healing ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Xuejing Chen | Luyuan Xie | Yonghong He | Tian Guan | Xuesi Zhou | Bei Wang | Guangxia Feng | Haihong Yu | Yanhong Ji
Source: The Analyst (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning A deep learning network called "residual neural network" (ResNet) was used to decode Raman spectra-encoded suspension arrays (SAs). With narrow bandwidths and stable signals, Raman spectra have ideal encoding properties. The different Raman reporter molecules assembled micro-quartz pieces (MQPs) were grafted with various biomolecule probes, which enabled simultaneous detection of numerous target analytes in a single sample. Multiple types of mixed MQPs were measured by Raman spectroscopy and then decoded by ResNet to acquire the type information of analytes. The good classification performance of ResNet was verified by a t-distributed stochastic neighbor embedding (t-SNE) diagram. Compared with other machine learning models, these experiments showed that ResNet was obviously superior in terms of classification stability and training convergence to different datasets. This method simplified the decoding process and the classification accuracy reached 100%. Raman spectroscopy ; Decoding methods ; Computer science ; Artificial intelligence ; Biological system ; Analyte ; Residual ; Materials science ; Pattern recognition (psychology) ; Algorithm ; Chemistry ; Physics ; Optics ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Luca Costantino | Daniela Barlocco
Source: Journal of Chemical Education (article, 2019-03-28)
Field: Chemistry — Physical and Theoretical Chemistry

Teaching an Undergraduate Organic Chemistry Laboratory Course with a Tailored Problem-Based Learning Approach This article presents the structure of the organic chemistry laboratory Course “Synthesis and Extraction of Drugs” that is included in Modena University (Italy) in the third year of the five-year “Chemistry and Pharmaceutical Technologies” degree program. This course is unique in its kind, and it aims to provide students with the critical-thinking skills required to face practical organic synthesis. Because the “problem-based learning” approach ensures a high level of learning, we modified this approach to reconcile the students’ knowledge, the resources available compared with the number of students, and the purpose of a deep learning. Accordingly, tailored academic materials have been prepared (a textbook, a laboratory notebook, and a selection of primary literature), appropriate in-class and laboratory activities have been set up, and a grading pattern that promotes the active learning process has been defined. Although the number of students is high, following this approach, very positive results with respect to both laboratory techniques and critical thinking ability have been obtained. Course (navigation) ; Mathematics education ; Chemistry ; Chemistry education ; Computer science ; Engineering physics ; Engineering ; Psychology Various Chemistry Research Topics ; Innovative Teaching Methods ; Experimental Learning in Engineering
Authors: Yousef Mohammadi | Mohammad Reza Saeb | Alexander Penlidis | Esmaiel Jabbari | Florian J. Stadler | Philippe Zinck | Krzysztof Matyjaszewski
Source: Polymers (article, 2019-04-01)
Field: Materials Science — Materials Chemistry

Intelligent Machine Learning: Tailor-Making Macromolecules Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and successfully put into practice. The two tools are established through the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the produced macromolecules (as responses). The latter is capable of precisely predicting optimal copolymerization conditions to simultaneously satisfy all predefined microstructural features. The effectiveness of the proposed intelligent modeling and optimization techniques for solving this extremely important 'inverse' engineering problem was successfully examined by investigating the possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttling coordination polymerization. Macromolecule ; Computer science ; Artificial intelligence ; Chemistry ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Hiroshi Kajino
Source: International Conference on Machine Learning (article, 2019-06-09)
Field: Materials Science — Materials Chemistry

Molecular Hypergraph Grammar with Its Application to Molecular Optimization Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited. These challenges are to some extent alleviated by a combination of a variational autoencoder (VAE) and Bayesian optimization (BO). VAE converts a molecule into/from its latent continuous vector, and BO optimizes a latent continuous vector (and its corresponding molecule) within a limited number of property evaluations. While the most recent work, for the first time, achieved 100% validity, its architecture is rather complex due to auxiliary neural networks other than VAE, making it difficult to train. This paper presents a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to achieve 100% validity. Our idea is to develop a graph grammar encoding the hard chemical constraints, called molecular hypergraph grammar (MHG), which guides VAE to always generate valid molecules. We also present an algorithm to construct MHG from a set of molecules. Autoencoder ; Hypergraph ; Computer science ; Molecular graph ; Theoretical computer science ; Encoding (memory) ; Set (abstract data type) ; Artificial intelligence ; Algorithm ; Graph ; Artificial neural network ; Mathematics ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Roald Hoffmann | Jean‐Paul Malrieu
Source: Angewandte Chemie International Edition (review, 2019-11-01)
Field: Materials Science — Materials Chemistry

Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part A. Stage Setting We begin our tripartite Essay with a triangle of understanding, theory and simulation. Sketching the intimate tie between explanation and teaching, we also point to the emotional impact of understanding. As we trace the development of theory in chemistry, Dirac's characterization of what is known and what is needed for theoretical chemistry comes up, as does the role of prediction, and Thom's phrase "To predict is not to explain." We give a typology of models, and then describe, no doubt inadequately, machine learning and neural networks. In the second part, we leave philosophy, beginning by describing Roald's being beaten by simulation. This leads us to artificial intelligence (AI), Searle's Chinese room, and Strevens' account of what a go-playing program knows. Back to our terrain-we ask "Quantum Chemistry, † ca. 2020?" Then move to examples of AI affecting social matters, ranging from trivial to scary. We argue that moral decisions are hardly to be left to a computer. At this point, we try to pull the reader up, giving the opposing view of an optimistic, limitless future a voice. But we don't do justice to that view-how could we? We return to questioning the ascetic dimension of scientists, their romance with black boxes. Onward: In the 3rd part of this Essay, we work our way up from pessimism. We trace (another triangle!) the special interests of experimentalists, who want the theory we love, and reliable numbers as well. We detail in our own science instances where theory gave us real joy. Two more examples-on magnetic coupling in inorganic diradicals, and the way to think about alkali metal halides, show us the way to integrate simulation with theory. Back and forth is how it should be-between painfully-obtained, intriguing numbers, begging for interpretation, in turn requiring new concepts, new models, new theoretically grounded tools of computation. Through such iterations understanding is formed. As our tripartite Essay ends, we outline a future of consilience, with a role both for fact-seekers, and searchers for understanding. Chemistry's streak of creation provides in that conjoined future a passage to art and to perceiving, as we argue we must, the sacred in science. Stage (stratigraphy) ; Quantum chemistry ; Quantum ; Chemistry ; Engineering physics ; Physics ; Physical chemistry ; Quantum mechanics ; Geology Machine Learning in Materials Science ; History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Han Liu | Zipeng Fu | Yipeng Li | Nazreen Farina Ahmad Sabri | Mathieu Bauchy
Source: Journal of Non-Crystalline Solids (article, 2019-04-29)
Field: Materials Science — Ceramics and Composites

Balance between accuracy and simplicity in empirical forcefields for glass modeling: Insights from machine learning Simplicity ; Silicate glass ; Computer science ; Transferability ; Intuition ; Monte Carlo method ; Machine learning ; Artificial intelligence ; Parametrization (atmospheric modeling) ; Mathematics ; Materials science Glass properties and applications ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Sergei Posysaev | Olga Miroshnichenko | M. Alatalo | Duy Le | Talat S. Rahman
Source: Computational Materials Science (article, 2019-03-04)
Field: Materials Science — Materials Chemistry

Oxidation states of binary oxides from data analytics of the electronic structure Valence (chemistry) ; Binary number ; Charge (physics) ; Electronic structure ; Oxide ; Chemistry ; Chemical physics ; Materials science ; Computer science ; Computational chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Zhidong Leong | Teck Leong Tan
Source: Physical review. B./Physical review. B (article, 2019-10-28)
Field: Materials Science — Materials Chemistry

Robust cluster expansion of multicomponent systems using structured sparsity Identifying a suitable set of descriptors for modeling physical systems often utilizes either deep physical insights or statistical methods such as compressed sensing. In statistical learning, a class of methods known as structured sparsity regularization seeks to combine both physics- and statistics-based approaches. Used in bioinformatics to identify genes for the diagnosis of diseases, group lasso is a well-known example. Here in physics, we present group lasso as an efficient method for obtaining robust cluster expansions (CEs) of multicomponent systems, a popular computational technique for modeling such systems and studying their thermodynamic properties. Via convex optimization, group lasso selects the most predictive set of atomic clusters as descriptors in accordance with the physical insight that if a cluster is selected, its subclusters should be too. These selection rules avoid spuriously large fitting parameters by redistributing them among lower-order terms, resulting in more physical, accurate, and robust CEs. We showcase these features of group lasso using the CE of the bcc ternary alloy Mo-V-Nb. These results are timely given the growing interest in applying CE to increasingly complex systems, which demand a more reliable machine learning methodology to handle the larger parameter space. Lasso (programming language) ; Regularization (linguistics) ; Computer science ; Cluster (spacecraft) ; Physical system ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Complex system ; Regular polygon ; Data mining ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; RNA and protein synthesis mechanisms
Authors: Takuro Nudejima | Yasuhiro Ikabata | Junji Seino | Takeshi Yoshikawa | Hiromi Nakai
Source: The Journal of Chemical Physics (article, 2019-07-09)
Field: Materials Science — Materials Chemistry

Machine-learned electron correlation model based on correlation energy density at complete basis set limit We propose a machine-learned correlation model that is built using the regression between density variables such as electron density and correlation energy density. The correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total correlation energy. The numerical examination revealed that the correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-correlation functionals of the density functional theory, the Hartree-Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present correlation model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient correlation model. Basis set ; Correlation ; Basis (linear algebra) ; Limit (mathematics) ; Atomic orbital ; Statistical physics ; Density functional theory ; Energy (signal processing) ; Electronic correlation ; Mathematics ; Physics ; Quantum mechanics ; Electron ; Statistics ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Yong-Jie Hu | Ge Zhao | Baiyu Zhang | Chaoming Yang | Mingfei Zhang | Zi-Kui Liu | Xiaofeng Qian | Liang Qi
Source: Nature Communications (article, 2019-10-02)
Field: Materials Science — Materials Chemistry

Local electronic descriptors for solute-defect interactions in bcc refractory metals The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries are essential in determining alloy properties. Here we present a general linear correlation between two descriptors of local electronic structures and the solute-defect interaction energies in binary alloys of body-centered-cubic (bcc) refractory metals (such as W and Ta) with transition-metal substitutional solutes. One electronic descriptor is the bimodality of the d-orbital local density of states for a matrix atom at the substitutional site, and the other is related to the hybridization strength between the valance sp- and d-bands for the same matrix atom. For a particular pair of solute-matrix elements, this linear correlation is valid independent of types of defects and the locations of substitutional sites. These results provide the possibility to apply local electronic descriptors for quantitative and efficient predictions on the solute-defect interactions and defect properties in alloys. Electronic structure ; Atom (system on chip) ; Binary number ; Local density of states ; Bimodality ; Grain boundary ; Matrix (chemical analysis) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quasicrystal Structures and Properties
Authors: Qiaohao Liang | Shyam Dwaraknath | Kristin A. Persson
Source: Scientific Data (article, 2019-07-26)
Field: Materials Science — Materials Chemistry

High-throughput computation and evaluation of raman spectra Raman spectroscopy is used ubiquitously in the characterization of condensed materials, spanning from biomaterials, minerals to polymers, as it provides a unique fingerprint of local bonding and environment. In this work, we design and demonstrate a robust, automatic computational workflow for Raman spectra that employs first-principle calculations based on density functional perturbation theory. A set of computational results are compared to Raman spectra obtained from established experimental databases to estimate the accuracy of the calculated properties across chemical systems and structures. Details regarding the computational methodology and technical validation are presented along with the format of our publicly available data record. Raman spectroscopy ; Workflow ; Computation ; Computer science ; Spectral line ; Computational science ; Fingerprint (computing) ; Set (abstract data type) ; Density functional theory ; Characterization (materials science) ; Perturbation theory (quantum mechanics) ; Biological system ; Computational physics ; Materials science ; Algorithm ; Chemistry ; Physics ; Nanotechnology ; Computational chemistry ; Artificial intelligence ; Optics ; Database ; Biology Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research