jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuzhen Fan | Xiaoyu Zhang | Chuanhou Gao | Denis Dochain
Source: arXiv (Cornell University) (preprint, 2023-11-30)
Field: Materials Science — Materials Chemistry

Automatic Implementation of Neural Networks through Reaction Networks -- Part I: Circuit Design and Convergence Analysis Information processing relying on biochemical interactions in the cellular environment is essential for biological organisms. The implementation of molecular computational systems holds significant interest and potential in the fields of synthetic biology and molecular computation. This two-part article aims to introduce a programmable biochemical reaction network (BCRN) system endowed with mass action kinetics that realizes the fully connected neural network (FCNN) and has the potential to act automatically in vivo. In part I, the feedforward propagation computation, the backpropagation component, and all bridging processes of FCNN are ingeniously designed as specific BCRN modules based on their dynamics. This approach addresses a design gap in the biochemical assignment module and judgment termination module and provides a novel precise and robust realization of bi-molecular reactions for the learning process. Through equilibrium approaching, we demonstrate that the designed BCRN system achieves FCNN functionality with exponential convergence to target computational results, thereby enhancing the theoretical support for such work. Finally, the performance of this construction is further evaluated on two typical logic classification problems. Computer science ; Realization (probability) ; Artificial neural network ; Computation ; Convergence (economics) ; Component (thermodynamics) ; Process (computing) ; Feed forward ; Theoretical computer science ; Backpropagation ; Artificial intelligence ; Bridging (networking) ; Algorithm ; Control engineering ; Mathematics ; Engineering Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Gene Regulatory Network Analysis
Authors: Daniel Schwalbe‐Koda
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Data for: "Inorganic synthesis-structure maps in zeolites with machine learning..." Repository containing all the data necessary to reproduce the paper "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". This content includes: raw data for all results in the article; figures appearing in the main paper; and Jupyter notebooks and scripts containing the code for analyzing/reproducing the results. Computer science ; Chemical engineering ; Chemistry ; Engineering Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Computational Drug Discovery Methods
Authors: Ryohei Yamaguchi | Shigenori Takeda | Masatsugu Yamada | Toshifumi Kakiuchi | Yutaka Imamura
Source: ChemRxiv (preprint, 2023-04-18)
Field: Chemistry — Spectroscopy

Improving Deep Neural Network in Predicting Electron Ionization Mass Spectra by Molecular Similarity-wise Sampling Mass spectrometry is a widely used technique for identifying molecules in a variety of applications, including organic synthesis and metabolomics. Recently,a deep neural network model for mass spectrometry has been developed and numerically assessed. However, we confirmed that the model performs poorly for a specific target such as highly-fluorinated compound, this study introduces a simple dataset undersampling scheme using a molecular similarity. The model trained on the undersampled dataset shows that the predictive performance was improved for fluorinated compounds and was relatively maintained even for non-fluorinated compounds. This performance is probably ascribed to the reduction of bit collisions of ECFPs. The undersampling approach is general and applicable to any specific target. Undersampling ; Similarity (geometry) ; Artificial neural network ; Computer science ; Mass spectrometry ; Sampling (signal processing) ; Pattern recognition (psychology) ; Mass spectrum ; Biological system ; Artificial intelligence ; Data mining ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Liang Sun | Mohan Chen
Source: arXiv (Cornell University) (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

Machine learning based nonlocal kinetic energy density functional for simple metals and alloys Developing an accurate kinetic energy density functional (KEDF) remains a major hurdle in orbital-free density functional theory. We propose a machine learning based physical-constrained nonlocal (MPN) KEDF and implement it with the usage of the bulk-derived local pseudopotentials and plane wave basis sets in the ABACUS package. The MPN KEDF is designed to satisfy three exact physical constraints: the scaling law of electron kinetic energy, the free electron gas limit, and the non-negativity of Pauli energy density. The MPN KEDF is systematically tested for simple metals, including Li, Mg, Al, and 59 alloys. We conclude that incorporating nonlocal information for designing new KEDFs and obeying exact physical constraints are essential to improve the accuracy, transferability, and stability of ML-based KEDF. These results shed new light on the construction of ML-based functionals. Kinetic energy ; Pauli exclusion principle ; Density functional theory ; Simple (philosophy) ; Basis (linear algebra) ; Energy (signal processing) ; Statistical physics ; Computer science ; Limit (mathematics) ; Scaling ; Physics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Akram Ibrahim | Daniel Wines | Can Ataca
Source: arXiv (Cornell University) (preprint, 2023-08-09)
Field: Materials Science — Materials Chemistry

Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations The chemical exfoliation of non-van der Waals (vdW) materials to ultrathin nanosheets remains a formidable challenge. This difficulty arises from the strong preference of these materials to engage in three-dimensional chemical bonding, resulting in uncontrolled atomic migration into the vdW gaps during cation deintercalation from the bulk structure, ultimately leading to unpredictable structural disorder. We propose a generic framework using neural network potentials (NNPs) to accurately model the widespread nonstoichiometric environments resulting from disordered atomic migrations during exfoliation of non-vdW materials. We apply our framework to investigate the crystal structures and phase transformations occurring during the exfoliation of non-vdW nonstoichiometric Cr$_{(1-x)}$S systems, a compelling material category with substantial potential for two-dimensional (2D) magnetic applications. The efficacy of the NNP outperforms the conventional cluster expansion, exhibiting superior accuracy and transferability to unexplored crystal structures and compositions. By employing the NNP in simulated annealing optimizations, we predict low-energy Cr$_{(1-x)}$S structures anticipated to result from experimental synthesis. A notable structural transition is discerned at the Cr$_{0.5}$S composition, with half of the Cr atoms preferentially migrating to vdW gaps. This aligns with experimental observations in the chemical exfoliation of 2D CrS$_2$, and emphasizes the vital role of excess Cr atoms beyond the Cr/S = $1/2$ composition ratio in stabilizing vdW gaps. Additionally, we utilize the NNP in a vacancy diffusion Monte Carlo simulation to illustrate the impact of lateral compressive strains in catalyzing the formation of vdW gaps within non-vdW CrS$_2$ slabs. This provides a direct pathway for more facile exfoliation of ultrathin nanosheets from non-vdW materials through strain engineering. van der Waals force ; Chemical physics ; Exfoliation joint ; Crystal (programming language) ; Materials science ; Vacancy defect ; Crystal structure ; Crystallography ; Nanotechnology ; Chemistry ; Molecule ; Graphene 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Daniel Persaud | Logan Ward | Jason Hattrick‐Simpers
Source: arXiv (Cornell University) (preprint, 2023-10-10)
Field: Materials Science — Materials Chemistry

Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials' The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards open-source data and tools to propel the field. Despite the increasing usefulness and capabilities of these tools, developers neglecting to follow reproducible practices creates a significant barrier for researchers looking to use or build upon their work. In this study, we investigate the challenges encountered while attempting to reproduce a section of the results presented in "A general-purpose machine learning framework for predicting properties of inorganic materials." Our analysis identifies four major categories of challenges: (1) reporting computational dependencies, (2) recording and sharing version logs, (3) sequential code organization, and (4) clarifying code references within the manuscript. The result is a proposed set of tangible action items for those aiming to make code accessible to, and useful for the community. Computer science ; Field (mathematics) ; Code (set theory) ; Set (abstract data type) ; Data science ; Action (physics) ; Software engineering ; Programming language Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Wesley Zeng | Heather Killeen
Source: Journal of Emerging Investigators (article, 2023-01-01)
Field: Materials Science — Metals and Alloys

Developing a neural network to model the mechanical properties of 13-8 PH stainless steel alloy We systematically evaluated the effects of raw material composition, heat treatment, and mechanical properties on 13-8PH stainless steel alloy. 13-8PH is widely used in the aerospace industry for various structural components. Little is known about the predictive modeling of mechanical properties for 13-8 PH per heat treatment. To achieve the desired properties, multiple back-and-forth heat treatments and mechanical tests are often required which utilize significant amounts of resources. Thus, we hypothesized that no specific heat treatment temperature can be used to achieve the desired property ranges across all 13-8PH compositions. We heat treated 13-8PH alloy through solution treatment, cryogenic treatment, and final elevated temperature aging. We conducted double shear testing to measure this mechanical property. Consistent with expectations, experimental data confirmed that no single specific aging heat treatment temperature could be used to achieve desired property range for all compositions. A unique aging heat treatment temperature is required and there is a need to develop a model to predict heat treatment response for each composition. The results of the neural network models were in agreement with our experimental results and aided in the evaluation of the effects of aging temperature on double shear strength. The data suggests that this model can be used to determine the appropriate 13-8PH alloy aging temperature needed to achieve the desired mechanical properties, eliminating the need for many costly trials and errors through re-heat treatments. Materials science ; Alloy ; Aerospace ; Shear (geology) ; Specific heat ; Composite material ; Thermodynamics ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; High Temperature Alloys and Creep
Authors: Jianxin Hou | Tiehan Fan
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

An Effective Framework for Predicting Performance of Solid-Solution Copper Alloys Using Feature Engineering Technique in Machine Learning Copper ; Feature (linguistics) ; Feature engineering ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Process engineering ; Metallurgy ; Engineering ; Deep learning ; Philosophy Nanoporous metals and alloys ; Aluminum Alloy Microstructure Properties
Authors: Xiaoying Zhuang
Source:  (article, 2023-10-22)
Field: Materials Science — Materials Chemistry

Machine Learning Based Exploitation and Characterization of 2D Materials<sup>*</sup> 2D materials have attracted widespread attention in recent years. They have some unique properties that other usual materials do not have. For example, its electrical, mechanical, thermal and optical properties vary with the number of layers. Density functional theory (DFT) calculations are robust tools to explore the physical properties of pristine structures as well as to explore new type of 2D nanomaterials at their ground state, but they become exceedingly expensive for large systems or at finite temperatures. Classical molecular dynamics (CMD) simulations offer the possibility to study larger systems at elevated temperatures, but they require accurate interatomic potentials. We developed machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio datasets which can be used to evaluate the complex physical properties of nanostructured materials, with only a fractional computational cost of conventional DFT-based solutions, cutting down from months to tens of hours. MLIPs offer extraordinary capabilities to marry the first-principles accuracy with multiscale modeling and thus enable the modeling of complex nanostructures at continuum level and has flexibility without paying unaffordable computational costs. We show outstanding and robust potential to develop fully automated platforms, to design, optimize and explore various properties, i.e. mechanical, thermal, optical and electrical properties of 2D materials and structures at continuum level, and with inherent precision and robustness. Robustness (evolution) ; Density functional theory ; Flexibility (engineering) ; Interatomic potential ; Computer science ; Thermal ; Nanomaterials ; Ab initio ; Molecular dynamics ; Multiscale modeling ; Materials science ; Statistical physics ; Nanotechnology ; Computational science ; Physics ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Satvik Lolla | Haotong Liang | A. Gilad Kusne | Ichiro Takeuchi | William Ratcliff
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

A semi-supervised deep-learning approach for automatic crystal structure classification The structural solution problem can be a daunting and time-consuming task.Especially in the presence of impurity phases, current methods, such as indexing, become more unstable.In this work, the novel approach of semi-supervised learning is applied towards the problem of identifying the Bravais lattice and the space group of inorganic crystals.The reported semi-supervised generative deep-learning model[1-3] can train on both labeled data, i.e. diffraction patterns with the associated crystal structure, and unlabeled data, i.e. diffraction patterns that lack this information.This semi-supervised scheme consists of a generator and discriminative models, which are bundled together into a Generative Adversarial Network as shown in Fig. 1.By training on the unlabeled diffraction patterns, the discriminator model gains the ability to extract useful features with regard to the diffraction patterns, which in principle, could be beneficial to performing downstream supervised tasks.This approach allows the models to take advantage of the troves of unlabeled data that current supervised learning approaches cannot, which should result in models that can more accurately generalize to real data.In this work, powder diffraction patterns are classified into all 14 Bravais lattices and 144 space groups (the number is limited due to sparse coverage in crystal structure databases), which covers more crystal classes than other studies.The reported models[4] also outperform current deep-learning approaches[5-7] for both space group and Bravais lattice classification using fewer training data. Artificial intelligence ; Computer science ; Deep learning ; Pattern recognition (psychology) ; Machine learning X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: S. Park | Gunwoo Noh
Source: Civil-comp conferences (article, 2023-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerated material design of Mn-Zn ferrite toroidal core using artificial neural network based surrogate model This paper presents an effective framework for predicting magnetic properties and optimizing the material design of Mn-Zn ferrite core.The objective of the current work is to construct a high-accuracy machine learning-based surrogate model correlating the configuration parameters of ferrite core and its electromagnetic performance according to the various material composition.The finite element method (FEM) combined with a model that considers the dielectric effect was developed to analyze dimensional resonance by magnetic simulation.The dielectric effect was treated as the equivalent circuit and was formulated by coupling with Maxwell's equations.To accelerate evaluating performance, we construct an ANNbased FE surrogate model.Training data is generated through the FEM-based electromagnetic analysis framework, and analysis-based data is added to the previous experimental-based data.ANN models were trained to predict microstructure parameters, magnetic properties, and core loss using expanded data.Finally, the Mn-Zn ferrite core performance for various compositions can be mapped through the effective surrogate model and identifies material compositions with optimized magnetic properties.Therefore, the magnetic properties are effectively calculated by the trained neural network, and the optimized composition of the ferrite core shows that the proposed framework can significantly improve the efficiency of material design. Ferrite core ; Ferrite (magnet) ; Materials science ; Artificial neural network ; Magnetic core ; Toroid ; Finite element method ; Dielectric ; Surrogate model ; Microstructure ; Inductor ; Computer science ; Electronic engineering ; Mechanical engineering ; Composite material ; Engineering ; Machine learning ; Structural engineering ; Electromagnetic coil ; Physics ; Optoelectronics ; Electrical engineering Magnetic Properties and Applications ; Induction Heating and Inverter Technology ; Magnetic Properties and Synthesis of Ferrites
Authors: Abdel Aty, Hisham
Source: Spiral (Imperial College London) (article, 2023-06-03)
Field: Materials Science — Materials Chemistry

Contextual representations of the chemical space for task agnostic machine learning methods The chemical space is a vast and complex multi-scale space that is difficult to represent in a way that is both computationally efficient and contextually meaningful. In practice, chemists represent molecular structures in a variety of ways, from skeletal and line formulae for organic compounds, to spectroscopic representations such as mass spectra (MS) and nuclear magnetic resonance (NMR) spectra. In this thesis, fast, contextually tailored computational representations of molecules and aggregate structures were developed. These representations not only give predictive power to computational predictors, but are also used to aid experimentalists with analysing more classical spectroscopic representations more rapidly. The first representation developed within this thesis builds on the Small-angle X-ray scattering (SAXS) representation of lipids, an experimental spectroscopic representation of aggregate lipid structures. Synthetic modelling of lipid scattering patterns was used to generate training datasets for a convolutional neural network (CNN) to quickly predict the lipid phase of a given lipid system based on its SAXS pattern. This new 2D representation is more suitable for CNNs and provides good and fast predictive power. Combining this with a user friendly WebApp means that experimentalists are now able to analyse their results and adjust their experiments at the beamline facility if necessary. The second representation developed within this thesis is a flexible molecular representation that initially abstracts structure data into a 768-dimensional vector, which can then be fine-tuned to a specific task. Whilst the task-agnostic, pre-trained performance of this representation is reasonable, it is the fine-tuning of the representation to a specific task that gives it its contextual power, as it can be applied to almost any chemical task given a small sample of data. The model architecture generates Molecular Fingerprints through Bidirectional Encoder Representations from Transformers (MFBERT) applied to SMILES strings... Representation (politics) ; Task (project management) ; Chemical space ; Aggregate (composite) ; Convolutional neural network ; Space (punctuation) ; Context (archaeology) ; Feature learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Martina Crippa | Annalisa Cardellini | Matteo Cioni | Gábor Cśanyi | Giovanni M. Pavan
Source: arXiv (Cornell University) (preprint, 2023-08-31)
Field: Chemistry — Spectroscopy

Machine learning of microscopic structure-dynamics relationships in complex molecular systems In many complex molecular systems, the macroscopic ensemble's properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g., Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g., Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g., LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales. Statistical physics ; Dynamical systems theory ; Complex system ; Computer science ; Molecular machine ; Molecular dynamics ; Biological system ; Local structure ; Artificial intelligence ; Complex dynamics ; Physics ; Nanotechnology ; Chemical physics ; Mathematics ; Materials science ; Biology Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Machine Learning in Materials Science