jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qi Zhao | Jordan J. Winetrout | Yanxun Xu | Yusu Wang | Hendrik Heinz
Source: arXiv (Cornell University) (preprint, 2021-10-01)
Field: Materials Science — Materials Chemistry

Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and implementation of atomic-scale engineering to harness the theoretical limits of modulus and tensile strength, of which only a fraction is routinely reached today. Here we demonstrate accurate and fast predictions of mechanical properties for CNTs and arbitrary 3D graphitic assemblies based on a training set of over 1000 stress-strain curves from cutting-edge reactive MD simulation and machine learning (ML). Several ML methods are compared and show that our newly proposed hierarchically structured graph neural networks with spatial information (HS-GNNs) achieve predictions in modulus and strength for any 3D nanostructure with only 5-10% error across a wide range of possible values. The reliability is sufficient for practical applications and a great improvement over off-the shelf ML methods with up to 50% deviation, as well as over earlier models for specific chemistry with 20% deviation. The algorithms allow more than 10 times faster mechanical property predictions than traditional molecular dynamics simulations, the identification of the role of defects and random 3D morphology, and high-throughput screening of 3D structures for enhanced mechanical properties. The algorithms can be scaled to morphologies up to 100 nm in size, expanded for chemically similar compounds, and trained to predict a broader range of properties. Nanostructure ; Materials science ; Bottleneck ; Ultimate tensile strength ; Modulus ; Graphene ; Carbon nanotube ; Molecular dynamics ; Nanotechnology ; Artificial neural network ; Computer science ; Algorithm ; Composite material ; Machine learning ; Physics Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Graphene research and applications
Authors: David K. Kreutter | Philippe Schwaller | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2021-05-09)
Field: Materials Science — Materials Chemistry

Predicting Enzymatic Reactions with a Molecular Transformer The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a specific enzyme transformation is very difficult. Here we used multi-task transfer learning to train the Molecular Transformer, a sequence-to-sequence machine learning model, with one million reactions from the US Patent Office (USPTO) database combined with 32,181 enzymatic transformations annotated with a text description of the enzyme. The resulting Enzymatic Transformer model predicts the structure and stereochemistry of enzyme-catalyzed reaction products with remarkable accuracy. One of the key novelties is that we combined the reaction SMILES language of only 405 atomic tokens with thousands of human language tokens describing the enzymes, such that our Enzymatic Transformer not only learned to interpret SMILES, but also the natural language as used by human experts to describe enzymes and their mutations. Transformer ; Enzyme ; Computer science ; Chemistry ; Artificial intelligence ; Computational biology ; Combinatorial chemistry ; Biochemistry ; Biology ; Engineering Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Genomics and Phylogenetic Studies
Authors: Emily Mazeau | Priyanka Satpute | Katrín Blöndal | Franklin Goldsmith | Richard H. West
Source: ChemRxiv (preprint, 2021-02-04)
Field: Materials Science — Materials Chemistry

Automated Mechanism Generation Using Linear Scaling Relationships and Sensitivity Analyses Applied to Catalytic Partial Oxidation of Methane &lt;div&gt;Kinetic parameters for surface reactions can be predicted using a combination of DFT calculations, scaling relations, and machine learning algorithms; however, construction of microkinetic models still requires a knowledge of all the possible, or at least reasonable, reaction pathways. The recently developed Reaction Mechanism Generator (RMG) for heterogeneous catalysis, now included in RMG version 3.0, is built upon well-established, open-source software that can provide detailed reaction mechanisms from user-supplied initial conditions without making &lt;i&gt;a priori&lt;/i&gt; assumptions. RMG is now able to estimate adsorbate thermochemistry and construct detailed microkinetic models on a range of hypothetical metal surfaces using linear scaling relationships. These relationships are a simple, computationally efficient way to estimate adsorption energies by scaling the energy of a calculated surface species on one metal to any other metal. By conducting simulations with sensitivity analyses, users can not only determine the rate limiting step on each surface by plotting a "volcano surface" for the degree of rate control of each reaction as a function of elemental binding energies, but also screen novel catalysts for desirable properties. We investigated the catalytic partial oxidation of methane to demonstrate the utility of this new tool and determined that an inlet gas C/O ratio of 0.8 on a catalyst with carbon and oxygen binding energies of -6.75 eV and -5.0 eV, respectively, yields the highest amount of synthesis gas. Sensitivity analyses show that while the dissociative adsorption of O&lt;sub&gt;2&lt;/sub&gt; has the highest degree of rate control, the interactions between individual reactions and reactor conditions are complex, which result in a dynamic rate-limiting step across differing metals.&lt;/div&gt; Catalysis ; Scaling ; Chemistry ; Adsorption ; Thermochemistry ; Methane ; Sensitivity (control systems) ; Partial oxidation ; Reaction mechanism ; Physical chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Wenhui Yang | Edirisuriya M. Dilanga Siriwardane | Rongzhi Dong | Yuxin Li | Jianjun Hu
Source: Journal of Physics Condensed Matter (preprint, 2021-08-13)
Field: Materials Science — Materials Chemistry

Crystal structure prediction of materials with high symmetry using differential evolution Crystal structure determines properties of materials. With the crystal structure of a chemical substance, many physical and chemical properties can be predicted by first-principles calculations or machine learning models. Since it is relatively easy to generate a hypothetical chemically valid formula, crystal structure prediction becomes an important method for discovering new materials. In our previous work, we proposed a contact map-based crystal structure prediction method, which uses global optimization algorithms such as genetic algorithms to maximize the match between the contact map of the predicted structure and the contact map of the real crystal structure to search for the coordinates at the Wyckoff positions (WP), demonstrating that known geometric constraints (such as the contact map of the crystal structure) help the crystal structure reconstruction. However, when predicting the crystal structure with high symmetry, we found that the global optimization algorithm has difficulty to find an effective combination of WP that satisfies the chemical formula, which is mainly caused by the inconsistency between the dimensionality of the contact map of the predicted crystal structure and the dimensionality of the contact map of the target crystal structure. This makes it challenging to predict the crystal structures of high-symmetry crystals. In order to solve this problem, here we propose to use PyXtal to generate and filter random crystal structures with given symmetry constraints based on the information such as chemical formulas and space groups. With contact map as the optimization goal, we use differential evolution algorithms to search for non-special coordinates at the WP to realize the structure prediction of high-symmetry crystal materials. Our experimental results show that our proposed algorithm CMCrystalHS can effectively solve the problem of inconsistent contact map dimensions and predict the crystal structures with high symmetry. Crystal structure prediction ; Symmetry (geometry) ; Crystal (programming language) ; Curse of dimensionality ; Crystal structure ; Algorithm ; Computer science ; Artificial intelligence ; Mathematics ; Geometry ; Chemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Fabio Urbina | Christopher Lowden | Christopher Culberson | Sean Ekins
Source:  (preprint, 2021-09-30)
Field: Materials Science — Materials Chemistry

MegaSyn: Integrating Generative Molecule Design, Automated Analog Designer and Synthetic Viability Prediction Drug discovery is a multi-stage process, often beginning with the identification of active molecules from a high-throughput screen or machine learning model. Once structure activity relationship trends become well established, identifying new analogs with better properties is important. Synthesizing these new compounds is a logical next step, and is key to research groups that have a synthetic chemistry team or external collaborators. Generative machine learning models have become widely adopted to generate new molecules and explore molecular space, with the goal of discovering novel compounds with desires properties. These generative models have been composed from recurrent neural networks (RNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) and are often combined with transfer learning or scoring of physicochemical properties to steer generative design. While these generative models have proven useful in generating new molecular libraries, often they are not capable of addressing a wide variety of potential problems, and often converge into similar molecular space when combined with a scoring function for desired properties. In addition, generated compounds are often not synthetically feasible, reducing their capabilities outside of virtual composition and limiting their usefulness in real-world scenarios. Here we introduce a suite of automated tools called MegaSyn representing 3 components: a new hill-climb algorithm which makes use of SMILES-based RNN generative models, analog generation software, and retrosynthetic analysis coupled with fragment analysis to score molecules for their synthetic feasibility. We now describe the development and testing of this suite of tools and propose how they might be used to optimize molecules or prioritize promising lead compounds using test case examples. Generative grammar ; Computer science ; Chemical space ; Artificial intelligence ; Machine learning ; Generative model ; Suite ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yi-Shen Lin | G. P. Purja Pun | Y. Mishin
Source: Research Square (preprint, 2021-01-21)
Field: Materials Science — Materials Chemistry

Development of a physically-informed neural network interatomic potential for tantalum Abstract Large-scale atomistic simulations of materials heavily rely on interatomic potentials, which predict the system energy and atomic forces. One of the recent developments in the field is constructing interatomic potentials by machine-learning (ML) methods. ML potentials predict the energy and forces by numerical interpolation using a large reference database generated by quantum-mechanical calculations. While high accuracy of interpolation can be achieved, extrapolation to unknown atomic environments is unpredictable. The recently proposed physically-informed neural network (PINN) model significantly improves the transferability by combining a neural network regression with a physics-based bond-order interatomic potential. Here, we demonstrate that general-purpose PINN potentials can be developed for body-centered cubic (BCC) metals. The proposed PINN potential for tantalum reproduces the reference energies within 2.8 meV/atom. It accurately predicts a broad spectrum of physical properties of Ta, including (but not limited to) lattice dynamics, thermal expansion, energies of point and extended defects, the dislocation core structure and the Peierls barrier, the melting temperature, the structure of liquid Ta, and the liquid surface tension. The potential enables large-scale simulations of physical and mechanical behavior of Ta with nearly first-principles accuracy while being orders of magnitude faster. This approach can be readily extended to other BCC metals. Interatomic potential ; Extrapolation ; Embedded atom model ; Tantalum ; Interpolation (computer graphics) ; Molecular dynamics ; Materials science ; Transferability ; Atomic units ; Lattice (music) ; Statistical physics ; Physics ; Computer science ; Quantum mechanics ; Machine learning ; Classical mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Hong-Bin Ren | Lei Wang | Xi Dai
Source: Chinese Physics B (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Differentiable programming and density matrix based Hartree–Fock method* Differentiable programming is an emerging programming paradigm that allows people to take derivative of an output of arbitrary code snippet with respect to its input. It is the workhorse behind several well known deep learning frameworks, and has attracted significant attention in scientific machine learning community. In this paper, we introduce and implement a density matrix based Hartree–Fock method that naturally fits into the demands of this paradigm, and demonstrate it by performing fully variational ground state calculation on several representative chemical molecules. Differentiable function ; Computer science ; Realization (probability) ; Matrix (chemical analysis) ; Density matrix ; Snippet ; Ground state ; Mathematical optimization ; Applied mathematics ; Mathematics ; Programming language ; Pure mathematics ; Quantum mechanics ; Physics ; Materials science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Cuong Ly | Cody A. Nizinski | Ada Toydemir | Clement Vachet | Luther W. McDonald | Tolga Taşdizen
Source: Microscopy and Microanalysis (article, 2021-11-04)
Field: Materials Science — Materials Chemistry

Determining the Composition of a Mixed Material with Synthetic Data Abstract Determining the composition of a mixed material is an open problem that has attracted the interest of researchers in many fields. In our recent work, we proposed a novel approach to determine the composition of a mixed material using convolutional neural networks (CNNs). In machine learning, a model “learns” a specific task for which it is designed through data. Hence, obtaining a dataset of mixed materials is required to develop CNNs for the task of estimating the composition. However, the proposed method instead creates the synthetic data of mixed materials generated from using only images of pure materials present in those mixtures. Thus, it eliminates the prohibitive cost and tedious process of collecting images of mixed materials. The motivation for this study is to provide mathematical details of the proposed approach in addition to extensive experiments and analyses. We examine the approach on two datasets to demonstrate the ease of extending the proposed approach to any mixtures. We perform experiments to demonstrate that the proposed approach can accurately determine the presence of the materials, and sufficiently estimate the precise composition of a mixed material. Moreover, we provide analyses to strengthen the validation and benefits of the proposed approach. Computer science ; Task (project management) ; Convolutional neural network ; Process (computing) ; Composition (language) ; Artificial intelligence ; Machine learning ; Data mining ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Mineral Processing and Grinding
Authors: Adam C. Mater | Michelle L. Coote
Source: Journal of Chemical Information and Modeling (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Explainable Molecular Sets: Using Information Theory to Generate Meaningful Descriptions of Groups of Molecules Algorithmically identifying the meaningful similarities between an assortment of molecules is a critical chemical problem, and one which is only gaining in relevance as data-driven chemistry continues to progress. Effectively addressing this challenge can be achieved through a reformulation of the problem into information theory, cluster-based supervised classification, and the implementation of key concepts, particularly information entropy and mutual information. These concepts are combined with unsupervised learning atop learned chemical spaces to generate meaningful labels for arbitrary collections of molecules. An open-source and highly extensible codebase is provided to undertake these experiments, demonstrate the viability of the approach on known clusters, and glean insights into the learned representations of chemical space within message-passing neural networks, an architecture not readily permitting interpretability. This approach facilitates the interoperability between human chemical knowledge and the algorithmically derived insights, which will continue to become more prevalent in the coming years. Computer science ; Interpretability ; Chemical space ; Cheminformatics ; Codebase ; Relevance (law) ; Chemical database ; Interoperability ; Entropy (arrow of time) ; Data science ; Theoretical computer science ; Information theory ; Artificial intelligence ; World Wide Web ; Chemistry ; Drug discovery ; Mathematics ; Software Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Michael J. Statt | Brian A. Rohr | Kris S. Brown | Dan Guevarra | Jens S. Hummelshøj | Linda Hung | abraham anapolsky | John M. Gregoire | Santosh K. Suram
Source: ChemRxiv (preprint, 2021-06-10)
Field: Materials Science — Materials Chemistry

ESAMP: Event-Sourced Architecture for Materials Provenance Management and Application to Accelerated Materials Discovery While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in data generation, ingestion, and materials state-aware machine learning. High-throughput experiments and automated computational workflows are addressing the challenge of data generation, and capitalizing on these emerging data resources requires ingestion of data into an architecture that captures the complex provenance of experiments and simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence and interrelationships among events occurring in a simulation or experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments performed using various modalities such as serial, parallel, multimodal experimentation. Our data architecture tracks the evolution of a material’s state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with statechanging processes in addition to the typically used composition data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in enabling several aspects of accelerated materials discovery such as dynamic workflow design, generation of knowledge graphs, and efficient integration of theory and experiment. Computer science ; Workflow ; Metadata ; Event (particle physics) ; Architecture ; Data science ; Distributed computing ; Data mining ; Database ; World Wide Web Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Junyu Jiao | Genming Lai | Liang Zhao | Jiaze Lu | Qidong Li | Xianqi Xu | Yao Jiang | Yan‐Bing He | Chuying Ouyang | Feng Pan | Hong Li | Jiaxin Zheng
Source: arXiv (Cornell University) (preprint, 2021-06-21)
Field: Materials Science — Materials Chemistry

Self-healing mechanism of lithium in lithium metal batteries Li metal is an ideal anode material for use in state-of-the-art secondary batteries. However, Li-dendrite growth is a safety concern and results in low coulombic efficiency, which significantly restricts the commercial application of Li secondary batteries. Unfortunately, the Li deposition (growth) mechanism is poorly understood on the atomic scale. Here, we used machine learning to construct a Li potential model with quantum-mechanical computational accuracy. Molecular dynamics simulations in this study with this model revealed two self-healing mechanisms in a large Li-metal system, viz. surface self-healing and bulk self-healing, and identified three Li-dendrite morphologies under different conditions, viz. "needle", "mushroom", and "hemisphere". Finally, we introduce the concepts of local current density and variance in local current density to supplement the critical current density when evaluating the probability of self-healing. Lithium (medication) ; Anode ; Materials science ; Faraday efficiency ; Nanotechnology ; Mechanism (biology) ; Current density ; Dendrite (mathematics) ; Density functional theory ; Self-healing ; Metal ; Chemical physics ; Construct (python library) ; Computer science ; Computational chemistry ; Chemistry ; Metallurgy ; Mathematics ; Physics ; Physical chemistry ; Electrode ; Quantum mechanics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials