jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chia‐Hao Lee | Abid Khan | Di Luo | Chuqiao Shi | Yue Zhang | M. Abir Hossain | Arend M. van der Zande | Bryan K. Clark | Pinshane Y. Huang
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Deep Learning Enabled Atom-by-Atom Analysis of 2D materials on the Million-Atom Scale An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the 'Save PDF' action button. Atom (system on chip) ; Scale (ratio) ; Materials science ; Action (physics) ; Nanotechnology ; Computer science ; Physics ; Quantum mechanics ; Embedded system Machine Learning in Materials Science ; Advancements in Battery Materials ; Graphene research and applications
Authors: Mingyuan Xu | Tong Zhu | John ZH Zhang
Source: ChemRxiv (preprint, 2021-04-05)
Field: Materials Science — Materials Chemistry

Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly non-trivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural-network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, One can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters and by de-redundancy of a sub-data set of the ANI-1 database. We believe that the ESOINN-DP method provides a novelty idea for the construction of NNPES and especially, the reference datasets, and it can be used for MD simulations of various gas-phase and condensed-phase chemical systems. Artificial neural network ; Computer science ; Redundancy (engineering) ; Artificial intelligence ; Chemical space ; Construct (python library) ; Explosive material ; Data mining ; Machine learning ; Chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Denghui Lu | Wanrun Jiang | Yixiao Chen | Linfeng Zhang | Weile Jia | Handong Wang | Mohan Chen
Source: arXiv (Cornell University) (preprint, 2021-07-05)
Field: Materials Science — Materials Chemistry

DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning based PES model. This scheme, we call DP Compress, is an efficient post-processing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster, and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online. Computer science ; Computation ; Deep learning ; Compression (physics) ; Boosting (machine learning) ; Artificial neural network ; Scheme (mathematics) ; Computer engineering ; Computational science ; Efficient energy use ; Artificial intelligence ; Algorithm ; Data compression ; Materials science ; Mathematics ; Electrical engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Topic Modeling
Authors: Xingang Zhao | Kun Zhou | Bangyu Xing | Ruoting Zhao | Shulin Luo | Tianshu Li | Yuanhui Sun | Guangren Na | Jiahao Xie | Xiaoyu Yang | Xinjiang Wang | Xiaoyu Wang | Xin He | Jian Lv | Yuhao Fu | Lijun Zhang
Source: arXiv (Cornell University) (preprint, 2021-03-14)
Field: Materials Science — Materials Chemistry

JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design. It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek for new materials, functionality, principles, etc. Developing specialized facility to generate, collect, manage, learn and mine large-scale materials data is crucial to materials informatics. We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP), which is an open-source Python framework to meet the research requirements of computational materials informatics. It is integrated by materials production factory, high-throughput first-principles calculations engine, automatic tasks submission and monitoring progress, data extraction, management and storage system, and artificial intelligence machine learning based data mining functions. We have integrated specific features such as inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials. We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case. By obeying the principles of automation, extensibility, reliability and intelligence, the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics. Materials informatics ; Computer science ; Automation ; Exploit ; Informatics ; Python (programming language) ; Artificial intelligence ; Data science ; Software engineering ; Systems engineering ; Health informatics ; Engineering ; Engineering informatics Machine Learning in Materials Science
Authors: Mahesh R. Neupane | J. L. Dı́az Cruz | James Weil | Michael N. Groves
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2021-10-06)
Field: Materials Science — Polymers and Plastics

Neural network-based study of structural, chemical and electronic properties of doped $$\hbox {MoO}_{3}$$ Materials science ; Doping ; Electronegativity ; Acceptor ; Dopant ; Conductivity ; Molybdenum trioxide ; Diamond ; Electronic structure ; Molybdenum ; Nanotechnology ; Chemical physics ; Physical chemistry ; Condensed matter physics ; Optoelectronics ; Computational chemistry ; Chemistry Transition Metal Oxide Nanomaterials ; Catalysis and Oxidation Reactions ; Polyoxometalates: Synthesis and Applications
Authors: Inimfon I. Akpabio | Serap A. Savari
Source:  (article, 2021-09-29)
Field: Materials Science — Surfaces, Coatings and Films

Uncertainty quantification of machine learning models: on conformal prediction Prediction intervals which describe the reliability of the predictive performance of machine learning models are important to guide decision making and to improve trust in deep learning and other forms of machine learning and artificial intelligence. Conformal prediction is a relatively recent, increasingly popular, rigorously proven and simple methodology to address this need for both classification and regression problems, and it does not use distributional assumptions like Gaussianity or the Bayesian framework; one new variant combines it with another technique to generate prediction intervals known as quantile regression. We will illustrate the strengths and limitations of different conformal prediction procedures for a regression problem involving line edge roughness (LER) estimation; LER affects semiconductor device performance and the yield of the manufacturing process. Low-dose images from the scanning electron microscope (SEM) are often used for roughness measurements because of relatively small acquisition times and resist shrinkage, but such images are corrupted by noise, blur, edge effects and other instrument errors. We consider prediction intervals for the deep convolutional neural network EDGENet, which was trained on a large dataset of simulated SEM images and directly estimates the edge positions from a SEM rough line image containing an unknown level of Poisson noise. Artificial intelligence ; Machine learning ; Computer science ; Noise (video) ; Convolutional neural network ; Artificial neural network ; Pattern recognition (psychology) ; Deep learning ; Reliability (semiconductor) ; Enhanced Data Rates for GSM Evolution ; Image (mathematics) Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Advancements in Photolithography Techniques
Authors: He Li | Zun Wang | Nianlong Zou | Meng Ye | Wenhui Duan | Yong Xu
Source: arXiv (Cornell University) (preprint, 2021-04-08)
Field: Materials Science — Materials Chemistry

Deep Neural Network Representation of Density Functional Theory Hamiltonian The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern research of material science. Here we study the crucial problem of representing DFT Hamiltonian for crystalline materials of arbitrary configurations via deep neural network. A general framework is proposed to deal with the infinite dimensionality and covariance transformation of DFT Hamiltonian matrix in virtue of locality and use message passing neural network together with graph representation for deep learning. Our example study on graphene-based systems demonstrates that high accuracy ($\sim$meV) and good transferability can be obtained for DFT Hamiltonian, ensuring accurate predictions of materials properties without DFT. The Deep Hamiltonian method provides a solution to the accuracy-efficiency dilemma of DFT and opens new opportunities to explore large-scale materials and physics. Hamiltonian (control theory) ; Density functional theory ; Transferability ; Curse of dimensionality ; Computer science ; Artificial neural network ; Deep learning ; Graph theory ; Locality ; Statistical physics ; Artificial intelligence ; Theoretical computer science ; Applied mathematics ; Mathematics ; Physics ; Mathematical optimization ; Machine learning ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Rhys E. A. Goodall | Abhijith S. Parackal | Felix A. Faber | Rickard Armiento | Alpha A. Lee
Source: arXiv (Cornell University) (preprint, 2021-06-21)
Field: Materials Science — Materials Chemistry

Rapid Discovery of Stable Materials by Coordinate-free Coarse Graining A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships, allowing the stability and functional properties of materials to be accurately predicted. However, most of these approaches use atomic coordinates as input and are thus bottlenecked by crystal structure identification when investigating previously unidentified materials. Our approach solves this bottleneck by coarse-graining the infinite search space of atomic coordinates into a combinatorially enumerable search space. The key idea is to use Wyckoff representations, coordinate-free sets of symmetry-related positions in a crystal, as the input to a machine learning model. Our model demonstrates exceptionally high precision in finding unknown theoretically stable materials, identifying 1569 materials that lie below the known convex hull of previously calculated materials from just 5675 ab initio calculations. Our approach opens up fundamental advances in computational materials discovery. Granularity ; Stability (learning theory) ; Crystal structure prediction ; Property (philosophy) ; Computer science ; Bottleneck ; Convex hull ; Space (punctuation) ; Regular polygon ; Mathematics ; Crystal structure ; Geometry ; Chemistry ; Machine learning ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: He Li | Zun Wang | Nianlong Zou | Meng Ye | Runzhang Xu | Xiaoxun Gong | Wenhui Duan | Yong Xu
Source: arXiv (Cornell University) (preprint, 2021-04-08)
Field: Materials Science — Materials Chemistry

Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent DFT Hamiltonian (DeepH) of crystalline materials, aiming to bypass the computationally demanding self-consistent field iterations of DFT and substantially improve the efficiency of ab initio electronic-structure calculations. A general framework is proposed to deal with the large dimensionality and gauge (or rotation) covariance of DFT Hamiltonian matrix by virtue of locality and is realized by the message passing neural network for deep learning. High accuracy, high efficiency and good transferability of the DeepH method are generally demonstrated for various kinds of material systems and physical properties. The method provides a solution to the accuracy-efficiency dilemma of DFT and opens opportunities to explore large-scale material systems, as evidenced by a promising application to study twisted van der Waals materials. Density functional theory ; Ab initio ; Hamiltonian (control theory) ; Computer science ; van der Waals force ; Curse of dimensionality ; Artificial neural network ; Electronic structure ; Artificial intelligence ; Statistical physics ; Computational chemistry ; Physics ; Quantum mechanics ; Mathematics ; Chemistry ; Mathematical optimization ; Molecule Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Xinqiao Wang | Chuansheng Yao | Yun Zhang | Jiahui Yu | Haoran Qiao | Chengyun Zhang | Yejian Wu | Renren Bai | Hongliang Duan
Source:  (preprint, 2021-08-10)
Field: Materials Science — Materials Chemistry

From Theory to Experiment: Transformer-Based Generation Enables Rapid Discovery of Novel Reactions Deep learning methods have been proven their potential roles in the chemical field, such as reaction prediction and retrosynthesis analysis. However, the de novo generation of unreported reactions using artificial intelligence technology remains not be completely explored. Inspired by molecular generation, we proposed the task of novel reaction generation. In this work, we applied the Heck reactions to train the transformer model, state-of-art natural language process model and obtained 4717 generated reactions after sampling and processing. We then confirmed that 2253 novel Heck reactions by organizing chemists to judge the generated reactions, and adopted organic synthesis experiment to verify the feasibility of unreported reactions. In this process, it only took 15 days from Heck reaction generation to experimental verification, proving that our model learns reaction rules in-depth and can make great contributions in the novel reaction discovery. Computer science ; Transformer ; Artificial intelligence ; Heck reaction ; Chemistry ; Engineering ; Palladium ; Organic chemistry ; Electrical engineering Machine Learning in Materials Science
Authors: Anna Lucia de Marco | Davide Bochicchio | Andrea Gardin | Giovanni Doni | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2021-01-29)
Field: Materials Science — Biomaterials

Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects Supramolecular fibers, composed of monomers that self-assemble directionally via non-covalent interactions, are ubiquitous in nature and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a non-trivial challenge. Here we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional vs. non-directional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies. Supramolecular chemistry ; Supramolecular polymers ; Monomer ; Polymer ; Dynamic covalent chemistry ; Nanotechnology ; Chemistry ; Chemical physics ; Materials science ; Molecule ; Organic chemistry Supramolecular Self-Assembly in Materials ; Polymer composites and self-healing ; Supramolecular Chemistry and Complexes
Authors: Zheren Wang | Olga Kononova | Kevin Cruse | Tanjin He | Haoyan Huo | Yuxing Fei | Yan Zeng | Yingzhi Sun | Zijian Cai | Wenhao Sun | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2021-11-21)
Field: Materials Science — Materials Chemistry

Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis "recipes" extracted from the scientific literature. Each recipe contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every recipe is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis recipes. Recipe ; Heuristics ; Computer science ; Construct (python library) ; Scale (ratio) ; Chemistry ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Aditya Nandy | Chenru Duan | Heather J. Kulik
Source: Journal of the American Chemical Society (preprint, 2021-10-13)
Field: Chemistry — Inorganic Chemistry

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4000 manuscripts, we use natural language processing and image analysis to obtain over 2000 solvent-removal stability measures and 3000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-transition-metal-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications. Leverage (statistics) ; Heuristics ; Stability (learning theory) ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Esther Heid | Jiannan Liu | Andrea Aude | William H. Green
Source: ChemRxiv (preprint, 2021-09-29)
Field: Materials Science — Materials Chemistry

On the influence of template size, canonicalization and exclusivity for retrosynthesis and reaction prediction applications Heuristic and machine learning models for rank-ordering reaction templates comprise an important basis for computer-aided organic synthesis regarding both product prediction and retrosynthetic pathway planning. Their viability relies heavily on the quality and characteristics of the underlying template database. With the advent of automated reaction and template extraction software and consequently the creation of template databases too large to be curated manually, a data-driven approach to assess and improve the quality of template sets is needed. We therefore systematically studied the influence of template generality, canonicalization and exclusivity on the performance of different template ranking models. We find that duplicate and non-exclusive templates, \textit{i.e.} templates which describe the same chemical transformation on identical or overlapping sets of molecules, decrease both the accuracy of the ranking algorithm and the applicability of the respective top-ranked templates significantly. To remedy the negative effects of non-exclusivity, we developed a general and computationally efficient framework to deduplicate and hierarchically correct templates. As a result, performance improved for both heuristic and machine learning template ranking algorithms across different template sizes. The canonicalization and correction code was made freely available. Template ; Generality ; Computer science ; Ranking (information retrieval) ; Heuristic ; Quality (philosophy) ; Data mining ; Retrosynthetic analysis ; Artificial intelligence ; Transformation (genetics) ; Rank (graph theory) ; Software ; Machine learning ; Mathematics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis