jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vipul Mann | Venkat Venkatasubramanian
Source: ChemRxiv (preprint, 2021-08-26)
Field: Materials Science — Materials Chemistry

Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach Retrosynthetic prediction is one of the main challenges in chemical synthesis because it requires a search over the space of plausible chemical reactions that often results in complex, multi-step, branched synthesis trees for even moderately complex organic reactions. Here, we propose an approach that performs single-step retrosynthesis prediction using SMILES grammar-based representations in a neural machine translation framework. Information-theoretic analyses of such grammar-representations reveal that they are superior to SMILES representations and are better-suited for machine learning tasks due to their underlying redundancy and high information capacity. We report the top-1 prediction accuracy of 43.8% (syntactic validity 95.6%) and maximal fragment (MaxFrag) accuracy of 50.4%. Comparing our model’s performance with previous work that used character-based SMILES representations demonstrate significant reduction in grammatically invalid predictions and improved prediction accuracy. Fewer invalid predictions for both known and unknown reaction class scenarios demonstrate the model’s ability to learn the underlying SMILES grammar efficiently. Retrosynthetic analysis ; Computer science ; Grammar ; Artificial intelligence ; Machine translation ; Redundancy (engineering) ; Natural language processing ; Rule-based machine translation ; Machine learning ; Translation (biology) ; Character (mathematics) ; Synchronous context-free grammar ; Example-based machine translation ; Mathematics ; Linguistics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis
Authors: Shree Hari Sureshbabu | Manas Sajjan | Sangchul Oh | Sabre Kais
Source: arXiv (Cornell University) (preprint, 2021-03-02)
Field: Materials Science — Materials Chemistry

Implementation of Quantum Machine Learning for Electronic Structure\n Calculations of Periodic Systems on Quantum Computing Devices Quantum machine learning algorithms, the extensions of machine learning to\nquantum regimes, are believed to be more powerful as they leverage the power of\nquantum properties. Quantum machine learning methods have been employed to\nsolve quantum many-body systems and have demonstrated accurate electronic\nstructure calculations of lattice models, molecular systems, and recently\nperiodic systems. A hybrid approach using restricted Boltzmann machines and a\nquantum algorithm to obtain the probability distribution that can be optimized\nclassically is a promising method due to its efficiency and ease of\nimplementation. Here we implement the benchmark test of the hybrid quantum\nmachine learning on the IBM-Q quantum computer to calculate the electronic\nstructure of typical 2-dimensional crystal structures: hexagonal-Boron Nitride\nand graphene. The band structures of these systems calculated using the hybrid\nquantum machine learning are in good agreement with those obtained by the\nconventional electronic structure calculation. This benchmark result implies\nthat the hybrid quantum machine learning, empowered by quantum computers, could\nprovide a new way of calculating the electronic structures of quantum many-body\nsystems.\n Quantum algorithm ; Quantum computer ; Computer science ; Quantum ; Quantum machine learning ; Electronic structure ; Quantum simulator ; Quantum network ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Rong Chen | Fu Liu | Yuchao Tang | Yanjie Liu | Ziqiang Dong | Zhen‐Yan Deng | Xinluo Zhao | Yi Liu
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Combined First-Principles and Machine Learning Study of the Initial Growth of Carbon Nanomaterials on Metal Surfaces Nanomaterials ; Carbon fibers ; Nanotechnology ; Materials science ; Composite number ; Composite material Carbon Nanotubes in Composites ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Adarsh V. Kalikadien | Evgeny A. Pidko | Vivek Sinha
Source: ChemRxiv (preprint, 2021-12-01)
Field: Materials Science — Materials Chemistry

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, ChemSpaX, that is aimed at automating the PF of a given molecular scaffold with special emphasis on TMcomplexes, is introduced. In three representative applications of ChemSpaX by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show thatChemSpaXgenerated geometries can be used in machine learning applications to accurately predict DFT computed HOMO-LUMO gaps for transition metal complexes.ChemSpaXis open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery. Chemical space ; Workflow ; Computer science ; Python (programming language) ; Surface modification ; Nanotechnology ; Molecule ; Steric effects ; Materials science ; Computational science ; Chemistry ; Engineering ; Mechanical engineering ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Ryan Stocks | Amanda S. Barnard
Source: Journal of Physics Condensed Matter (article, 2021-06-02)
Field: Materials Science — Materials Chemistry

Enhancing classical gold nanoparticle simulations with electronic corrections and machine learning Classical simulations of materials and nanoparticles have the advantage of speed and scalability but at the cost of precision and electronic properties, while electronic structure simulations have the advantage of accuracy and transferability but are typically limited to small and simple systems due to the increased computational complexity. Machine learning can be used to bridge this gap by providing correction terms that deliver electronic structure results based on classical simulations, to retain the best of both worlds. In this study we train an artificial neural network (ANN) as a general ansatz to predict a correction of the total energy of arbitrary gold nanoparticles based on general (material agnostic) features, and a limited set of structures simulated with an embedded atom potential and the self-consistent charge density functional tight binding method. We find that an accurate model with an overall precision of 14 eV or 8.6% can be found using a diverse range of particles and a large number of manually generated features which were then reduced using automatic data-driven approach to reduce evaluation bias. We found the ANN reduces to a linear relationship if a suitable subset of important features are identified prior to training, and that the prediction can be improved by classifying the nanoparticles into kinetically limited and thermodynamically limited subsets based prior to training the ANN corrections. The results demonstrate the potential for machine learning to enhance classical molecular dynamics simulations without adding significant computational complexity, and provides methodology that could be used to predict other electronic properties which cannot be calculated solely using classical simulations. Colloidal gold ; Nanoparticle ; Nanotechnology ; Materials science ; Computer science Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Nghia Manh Nguyen | Steph-Yves M. Louis | Lai Wei | Kamal Choudhary | Ming Hu | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2021-11-10)
Field: Materials Science — Materials Chemistry

Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks Lattice vibration frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational calculation of vibration frequencies using density functional theory (DFT) methods is too computationally demanding for a large number of samples in materials screening. Here we propose a deep graph neural network-based algorithm for predicting crystal vibration frequencies from crystal structures with high accuracy. Our algorithm addresses the variable dimension of vibration frequency spectrum using the zero padding scheme. Benchmark studies on two data sets with 15,000 and 35,552 samples show that the aggregated $R^2$ scores of the prediction reaches 0.554 and 0.724 respectively. Our work demonstrates the capability of deep graph neural networks to learn to predict phonon spectrum properties of crystal structures in addition to phonon density of states (DOS) and electronic DOS in which the output dimension is constant. Phonon ; Artificial neural network ; Vibration ; Lattice constant ; Superconductivity ; Condensed matter physics ; Lattice (music) ; Graph ; Materials science ; Crystal (programming language) ; Dimension (graph theory) ; Algorithm ; Computer science ; Statistical physics ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Theoretical computer science ; Acoustics ; Combinatorics Machine Learning in Materials Science ; Thermal properties of materials ; Inorganic Chemistry and Materials
Authors: Nicholas E. Jackson | Ganesh Sivaraman
Source:  (preprint, 2021-10-06)
Field: Materials Science — Materials Chemistry

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) molecular representations using deep neural networks (DNN). While DNN can learn complex representations that prove challenging for traditional kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within the GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions of a conjugated oligomer using range-separated hybrid density functional theory. DKL-ECG provides accurate reproduction of QC electronic properties in conjunction with prediction uncertainties that facilitate efficient training over multiple temperature data sets via active learning. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, the predictive accuracy of DKL-ECG models is effectively identical across all active learning methodologies employed. We attribute this result to the low conformational barriers of our test molecule and the redundant sampling of configurations induced by Boltzmann sampling, even for distinct temperature ensembles. Computer science ; Overfitting ; Artificial intelligence ; Scalability ; Sampling (signal processing) ; Kernel (algebra) ; Density functional theory ; Machine learning ; Deep learning ; Artificial neural network ; Algorithm ; Mathematics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Wenhao Sun | Michael F. Toney
Source: Nature Computational Science (article, 2021-05-17)
Field: Materials Science — Materials Chemistry

AI tool makes phase identification crystal clear Task (project management) ; Identification (biology) ; Computer science ; Work (physics) ; Phase (matter) ; Crystal (programming language) ; Engineering ; Mechanical engineering ; Systems engineering ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Upendra Yadav | Shashank Pathrudkar | Susanta Ghosh
Source:  (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Deformation Manifold Learning Model for Deformation of Multi-Walled Carbon Nano-Tubes: Exploring the Latent Space Abstract A novel machine learning model is presented in this work to obtain the complex high-dimensional deformation of Multi-Walled Carbon Nanotubes (MWCNTs) containing millions of atoms. To obtain the deformation of these high dimensional systems, existing models like Atomistic, Continuum or Atomistic-Continuum models are very accurate and reliable but are computationally prohibitive for these large systems. This high computational requirement slows down the exploration of physics of these materials. To alleviate this problem, we developed a machine learning model that contains a) a novel dimensionality reduction technique which is combined with b) deep neural network based learning in the reduced dimension. The proposed non-linear dimensionality reduction technique serves as an extension of functional principal component analysis. This extension ensures that the geometric constraints of deformation are satisfied exactly and hence we termed this extension as constrained functional principal component analysis. The novelty of this technique is its ability to design a function space where all the functions satisfy the constraints exactly, not approximately. The efficient dimensionality reduction along with the exact satisfaction of the constraint bolster the deep neural network to achieve remarkable accuracy. The proposed model predicts the deformation of MWCNTs very accurately when compared with the deformation obtained through atomistic-physics-based model. To simulate the complex high-dimensional deformation the atomistic-physics-based models takes weeks high performance computing facility, whereas the proposed machine learning model can predict the deformation in seconds. This technique also extracts the universally dominant pattern of deformation in an unsupervised manner. These patterns are comprehensible to us and provides us a better explanation on the working of the model. The comprehensibility of the dominant modes of deformation yields the interpretability of the model. Dimensionality reduction ; Curse of dimensionality ; Nonlinear dimensionality reduction ; Deformation (meteorology) ; Artificial neural network ; Artificial intelligence ; Principal component analysis ; Computer science ; Deep learning ; Dimension (graph theory) ; Algorithm ; Materials science ; Mathematics Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Graphene research and applications
Authors: Utkarsh Saha | Koyendrila Debnath | Soumitra Satapathi
Source: arXiv (Cornell University) (preprint, 2021-11-15)
Field: Materials Science — Materials Chemistry

Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering Data-driven approaches to solve problems in materials science have gained immense popularity in recent times due to their ability to predict unknown material properties and uncover relationships between structure and property. Machine learning algorithms like GBRT, random forest and neural networks have had tremendous success in predicting target properties of materials and design of structures for various applications. However, a major drawback for achieving results within the required accuracy using these algorithms has been the need for large datasets which can be challenging for problems when data is not sufficiently available for training the models. In this work, we propose the use of a hierarchical clustering algorithm which can work considerably better on materials science problems with small dataset constraints. We apply the algorithm to screen out promising double perovskite materials as candidates for solar cells. Computer science ; Cluster analysis ; Hierarchical clustering ; Property (philosophy) ; Photovoltaic system ; Artificial neural network ; Popularity ; Data mining ; Random forest ; Machine learning ; Artificial intelligence ; Work (physics) ; Engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Xiaoyong Cao | Pu Tian
Source: Preprints.org (preprint, 2021-03-04)
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. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested 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. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems. Computer science ; Granularity ; Metadynamics ; Discretization ; Divide and conquer algorithms ; Theoretical computer science ; Computational science ; Software portability ; Algorithm ; Molecular dynamics ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Scientific Computing and Data Management
Authors: Ryan Cohn | Elizabeth A. Holm
Source: arXiv (Cornell University) (preprint, 2021-10-18)
Field: Materials Science — Materials Chemistry

Neural message passing for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution. Abnormal grain growth can significantly alter the properties of materials during processing. This can cause significant variation in the properties and performance of in-spec feedstock components subjected to identical processing paths. Understanding and controlling abnormal grain growth has proved to be elusive due to the stochastic nature of this phenomenon. However, recent advances in deep learning provide a promising alternative to traditional experimental and physics-based methods for understanding this phenomenon. Neural message passing allows deep learning to be applied to irregular inputs including graph representations of grain structures in a material. In this study we generate a large database of Monte Carlo simulations of abnormal grain growth in an idealized system. We apply message passing neural networks to predict the occurrence of abnormal grain growth in these simulations using only the initial state of the system as input. A computer vision model is also trained for the same task for comparison. The preliminary results indicate that the message passing approach outperforms the computer vision method and achieved 75% prediction accuracy, significantly better than random guessing. Analysis of the uncertainty in the Monte Carlo simulations provides a road map for ongoing work on this project. Monte Carlo method ; Computer science ; Artificial neural network ; Task (project management) ; Artificial intelligence ; Grain growth ; Message passing ; Deep learning ; Graph ; Machine learning ; Algorithm ; Statistical physics ; Theoretical computer science ; Distributed computing ; Grain size ; Mathematics ; Materials science ; Engineering ; Statistics ; Physics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Generative Adversarial Networks and Image Synthesis
Authors: Sharon C. Glotzer
Source:  (article, 2021-08-12)
Field: Materials Science — Materials Chemistry

Data Science for Assembly Engineering Discovery and design of new materials able to self assemble from nanoscale building blocks are becoming increasingly enabled by large-scale molecular simulation. Aided by fast simulation codes leveraging powerful computer architectures, an unprecedented amount of data can be generated in the blink of an eye, shifting the effort and focus of the computational scientist from the simulation to the data. How do we manage so much data, and what do we do with it when we have it? In this talk, we discuss the applications of data science and data-driven thinking to molecular and materials simulation. Although we do so in the context of assembly engineering of soft matter, the tools and techniques discussed are general and applicable to a wide range of problems. We present applications of machine learning to automated, structure identification of complex colloidal crystals, high-throughput mapping of phase diagrams, the study of kinetic pathways between fluid and solid phases, and the discovery of previously elusive design rules and structure-property relationships. Computer science ; Context (archaeology) ; Identification (biology) ; Data science ; Focus (optics) ; Science and engineering ; Property (philosophy) ; Engineering Machine Learning in Materials Science ; Scientific Computing and Data Management