jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeren Shui | Daniel S. Karls | Mingjian Wen | Ilia Nikiforov | Ellad B. Tadmor | George Karypis
Source: arXiv (Cornell University) (preprint, 2022-10-14)
Field: Materials Science — Materials Chemistry

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties For decades, atomistic modeling has played a crucial role in predicting the behavior of materials in numerous fields ranging from nanotechnology to drug discovery. The most accurate methods in this domain are rooted in first-principles quantum mechanical calculations such as density functional theory (DFT). Because these methods have remained computationally prohibitive, practitioners have traditionally focused on defining physically motivated closed-form expressions known as empirical interatomic potentials (EIPs) that approximately model the interactions between atoms in materials. In recent years, neural network (NN)-based potentials trained on quantum mechanical (DFT-labeled) data have emerged as a more accurate alternative to conventional EIPs. However, the generalizability of these models relies heavily on the amount of labeled training data, which is often still insufficient to generate models suitable for general-purpose applications. In this paper, we propose two generic strategies that take advantage of unlabeled training instances to inject domain knowledge from conventional EIPs to NNs in order to increase their generalizability. The first strategy, based on weakly supervised learning, trains an auxiliary classifier on EIPs and selects the best-performing EIP to generate energies to supplement the ground-truth DFT energies in training the NN. The second strategy, based on transfer learning, first pretrains the NN on a large set of easily obtainable EIP energies, and then fine-tunes it on ground-truth DFT energies. Experimental results on three benchmark datasets demonstrate that the first strategy improves baseline NN performance by 5% to 51% while the second improves baseline performance by up to 55%. Combining them further boosts performance. Generalizability theory ; Benchmark (surveying) ; Computer science ; Artificial neural network ; Density functional theory ; Artificial intelligence ; Domain (mathematical analysis) ; Ground truth ; Classifier (UML) ; Machine learning ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Fuel Cells and Related Materials
Authors: Bin Xi | Kin Fai Tse | Tsz-Fung Kok | Ho Ming Chan | Man Kit Chan | Ho‐Yin Chan | Kwan Yue Clinton Wong | Robin S. H. Yuen | Junyi Zhu
Source: The Journal of Physical Chemistry C (article, 2022-07-18)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Acceleration on High-Symmetry Materials Search: Space Group Predictions from Band Structures Efficiency of search of wanted materials with desired properties is limited by the huge search space. By deep learning methods, we demonstrate that space group information can be acquired from band structure inputs to reduce the search space. Despite atomic orbital or accidental degeneracies mixed with lattice degeneracies, band degeneracies as input can yield 96.0% prediction accuracy for cubic systems that leads to a 25.1-fold acceleration of searching speed overall. Additionally, for all space groups, the prediction accuracy is 82.0% with overall 36.9-fold acceleration in the search speed. In addition, valence band degeneracies as inputs can yield satisfactory results and may assist in structural analysis from ARPES results. Space (punctuation) ; Acceleration ; Lattice (music) ; Physics ; Computer science ; Algorithm ; Classical mechanics ; Acoustics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Dinuka Rajapakse | Josh Meckstroth | Dylan T. Jantz | Kyle V. Camarda | Zijun Yao | Kevin C. Leonard
Source: ACS Measurement Science Au (article, 2022-11-15)
Field: Chemistry — Electrochemistry

Deconvoluting Kinetic Rate Constants of Catalytic Substrates from Scanning Electrochemical Approach Curves with Artificial Neural Networks Extracting information from experimental measurements in the chemical sciences typically requires curve fitting, deconvolution, and/or solving the governing partial differential equations via numerical (e.g., finite element analysis) or analytical methods. However, using numerical or analytical methods for high-throughput data analysis typically requires significant postprocessing efforts. Here, we show that deep learning artificial neural networks can be a very effective tool for extracting information from experimental data. As an example, reactivity and topography information from scanning electrochemical microscopy (SECM) approach curves are highly convoluted. This study utilized multilayer perceptrons and convolutional neural networks trained on simulated SECM data to extract kinetic rate constants of catalytic substrates. Our key findings were that multilayer perceptron models performed very well when the experimental data were close to the ideal conditions with which the model was trained. However, convolutional neural networks, which analyze images as opposed to direct data, were able to accurately predict the kinetic rate constant of Fe-doped nickel (oxy)hydroxide catalyst at different applied potentials even though the experimental approach curves were not ideal. Due to the speed at which machine learning models can analyze data, we believe this study shows that artificial neural networks could become powerful tools in high-throughput data analysis. Artificial neural network ; Convolutional neural network ; Computer science ; Perceptron ; Artificial intelligence ; Throughput ; Reaction rate constant ; Kinetic energy ; Biological system ; Pattern recognition (psychology) ; Materials science ; Physics Electrochemical Analysis and Applications ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Yiqun Wang | Xiaojie Zhang | Fei Xia | Elsa Olivetti | Stephen D. Wilson | Ram Seshadri | James M. Rondinelli
Source: Physical Review Research (article, 2022-04-11)
Field: Materials Science — Materials Chemistry

Learning the crystal structure genome for property classification Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely on featurization of materials composition, however, whether the exclusive use of structural knowledge in such models has the capacity to make comparable predictions remains unknown. Here we employ a deep neural network model to decode structure-property relationships in crystalline materials without explicitly considering chemical compositions. The focus is on classification of crystal systems, mechanical elasticity, electronic band gap, and phase stability. Our model utilizes a three-dimensional (3D) momentum space representation of structure from elastic x-ray scattering theory that exhibits rotation and permutation invariance. We perform novel ablation studies to help interpret the model performance by perturbing the physically meaningful input features (i.e., the diffraction patterns) instead of tuning the architecture of the learning model as in conventional ablation methods. We find that the spatial symmetry of the 3D diffraction patterns, which reflects crystalline symmetry operations, is more important than the diffraction intensities contained within for the model to make a successful classification. Our work showcases the potential of using statistical learning models to help understand materials physics, rather than performing predictive and generative tasks as in most materials informatics research. We also argue that learning the crystal structure genome in a chemistry-agnostic manner demonstrates that some crystal structures inherently host high propensities for optimal materials properties, which enables the decoupling of structure and composition for future codesign of multifunctionality. Artificial intelligence ; Computer science ; Machine learning ; Diffraction ; Property (philosophy) ; Physics ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Mohammad Ebqa’ai | Mohammad F. Tamimi | Andrew J. Kassick | Saadyah Averick | Toby L. Nelson
Source: Macromolecules (article, 2022-10-24)
Field: Materials Science — Biomaterials

One-Pot Phenolic-Initiated Mechanochemical Synthesis of Poly(lactic acid) Nanoparticles: Application of the Artificial Neural Network Algorithm to Perform Sensitivity Assessment Models Due to its biodegradable and biocompatible nature, poly(lactic acid) (PLA) has found widespread use in biomedical applications. Controlled drug delivery using PLA-based nanoparticles (PLA-NPs) is an important tool for tuning the bioavailability of therapeutics. Traditional methods to prepare PLA-NPs require a multistep process that is time-consuming and produces significant aqueous and organic waste streams. This work reports a one-pot mechanochemical synthesis of lactide to PLA-NPs. In this report, we developed and trained an artificial neural network algorithm to optimize PLA and PLA-NP preparation. Our results demonstrate a robust and optimized one-pot method for preparing precisely defined nanoparticles that generates minimal waste. Nanoparticle ; Artificial neural network ; Sensitivity (control systems) ; Lactic acid ; Materials science ; Chemistry ; Nanotechnology ; Computer science ; Artificial intelligence ; Engineering biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization ; Bone Tissue Engineering Materials
Authors: Taohong Zhang | Xueqiang Guo | Han Zheng | Yun Liu | Aziguli Wulamu | Han Chen | Xuxu Guo | Zhizhuo Zhang
Source: Science of Advanced Materials (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Review on Perovskite-Type Compound Using Machine Learning Perovskite is a kind of promising class of materials nowadays because of its exciting performance in energy, catalysis, semiconductor, and many other areas. Machine learning is a potential method by using big data to mine the deep hidden laws of the data and make some predictions of the new data. Applying machine learning method in perovskite is a meaningful attempt to explore the new material with new properties and to predict the properties of new materials. This review shows recent progress of perovskite using machine learning, and these attempts show the success of combining big data technique and material science which give us the new direction to explore the application of machine learning method and the new tools for material science. Perovskite (structure) ; Big data ; Materials science ; Artificial intelligence ; Machine learning ; Class (philosophy) ; Nanotechnology ; Computer science ; Data mining ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Michael Alverson | Sterling G. Baird | Ryan Murdock | Taylor D. Sparks
Source: ChemRxiv (preprint, 2022-11-08)
Field: Materials Science — Materials Chemistry

Generative adversarial networks and diffusion models in material discovery The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal structures. In this work, we pursue three primary objectives. I) Introduce CrysTens, a crystal encoding that can be used in a wide variety of deep-learning generative models. II) Investigate and analyze the performance of Generative Adversarial Networks (GANs) and Diffusion Models to find an innovative and effective way of generating theoretical crystal structures that are synthesizable and stable. III) Show that the models that have a better “understanding” of the structure of CrysTens produce more symmetrical and realistic crystals and exhibit a better apprehension of the dataset as a whole. We accomplish these objectives using over fifty thousand Crystallographic Information Files (CIFs) from Pearson’s Crystal Database. Generative grammar ; Computer science ; Crystal (programming language) ; Variety (cybernetics) ; Artificial intelligence ; Adversarial system ; Generative model ; Apprehension ; Machine learning ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Mohammadamin Tavakoli | А. Н. Шмаков | Francesco Ceccarelli | Pierre Baldi
Source: arXiv (Cornell University) (preprint, 2022-01-02)
Field: Materials Science — Materials Chemistry

Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation It is fundamental for science and technology to be able to predict chemical reactions and their properties. To achieve such skills, it is important to develop good representations of chemical reactions, or good deep learning architectures that can learn such representations automatically from the data. There is currently no universal and widely adopted method for robustly representing chemical reactions. Most existing methods suffer from one or more drawbacks, such as: (1) lacking universality; (2) lacking robustness; (3) lacking interpretability; or (4) requiring excessive manual pre-processing. Here we exploit graph-based representations of molecular structures to develop and test a hypergraph attention neural network approach to solve at once the reaction representation and property-prediction problems, alleviating the aforementioned drawbacks. We evaluate this hypergraph representation in three experiments using three independent data sets of chemical reactions. In all experiments, the hypergraph-based approach matches or outperforms other representations and their corresponding models of chemical reactions while yielding interpretable multi-level representations. Hypergraph ; Interpretability ; Computer science ; Robustness (evolution) ; Universality (dynamical systems) ; Theoretical computer science ; Exploit ; Representation (politics) ; Graph ; Artificial intelligence ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Joshua A. Rackers | Lucas Tecot | Mario Geiger | Tess Smidt
Source: arXiv (Cornell University) (preprint, 2022-01-11)
Field: Materials Science — Materials Chemistry

Cracking the Quantum Scaling Limit with Machine Learned Electron Densities A long-standing goal of science is to accurately solve the Schrödinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical computers imposes an effective limit of about a few dozen atoms for which we can calculate molecular electronic structure. We present a machine learning (ML) method to break through this scaling limit and make quantum chemistry calculations of very large systems possible. We show that Euclidean Neural Networks can be trained to predict the electron density with high fidelity from limited data. Learning the electron density allows us to train a machine learning model on small systems and make accurate predictions on large ones. We show that this ML electron density model can break through the quantum scaling limit and calculate the electron density of systems of thousands of atoms with quantum accuracy. Scaling ; Limit (mathematics) ; Quantum ; Statistical physics ; Electron density ; Electron ; Quantum chemistry ; Euclidean geometry ; Physics ; Scaling limit ; Quantum mechanics ; Computer science ; Mathematics ; Molecule ; Mathematical analysis Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Daigo Kaneko | Hiromasa Kaneko | Fumitaka Hayashi | Kohei Fukaishi | Tetsuya Yamada | Katsuya Teshima
Source: Industrial & Engineering Chemistry Research (article, 2022-12-27)
Field: Materials Science — Materials Chemistry

Process-Informatics-Assisted Preparation of Lithium Titanate Crystals with Various Sizes and Morphologies Functional crystalline materials are applicable to various fields because of their unique physical and (photo)chemical properties. Flux crystal growth is a versatile liquid-phase method that enables efficient crystal growth below the melting points of the solutes. Although tremendous effort focuses on the design growth conditions and synthesis of crystalline materials with target physicochemical properties, it is difficult to completely clarify relationships between experimental conditions and physicochemical properties. Therefore, much trial and error is necessary to obtain target crystalline materials. In this study, machine learning (ML) and Bayesian optimization (BO) improve the efficiency of the synthesis of crystalline layered lithium titanate Li2TiO3 (LTO) with a target particle size and morphology. An experimental data set that centers on the flux crystal growth of LTO verifies our proposed method. Coupling flux growth experiments with ML and BO methods can achieve a high preparation efficiency of LTO crystals with a desired particle size and morphology. Materials science ; Crystal (programming language) ; Lithium (medication) ; Crystal growth ; Lithium titanate ; Particle size ; Flux (metallurgy) ; Phase (matter) ; Chemical engineering ; Titanate ; Particle (ecology) ; Nanotechnology ; Computer science ; Thermodynamics ; Lithium-ion battery ; Composite material ; Crystallography ; Chemistry ; Metallurgy ; Organic chemistry ; Battery (electricity) Machine Learning in Materials Science ; Cloud Computing and Resource Management
Authors: Sijin Ren | Eric Fonseca | William D. Perry | Hai‐Ping Cheng | Xiaoguang Zhang | Richard G. Hennig
Source: The Journal of Physical Chemistry A (article, 2022-01-22)
Field: Materials Science — Materials Chemistry

Ligand Optimization of Exchange Interaction in Co(II) Dimer Single Molecule Magnet by Machine Learning Designing single-molecule magnets (SMMs) for potential applications in quantum computing and high-density data storage requires tuning their magnetic properties, especially the strength of the magnetic interaction. These properties can be characterized by first-principles calculations based on density functional theory (DFT). In this work, we study the experimentally synthesized Co(II) dimer (Co<sub>2</sub>(C<sub>5</sub>NH<sub>5</sub>)<sub>4</sub>(μ-PO<sub>2</sub>(CH<sub>2</sub>C<sub>6</sub>H<sub>5</sub>)<sub>2</sub>)<sub>3</sub>) SMM with the goal to control the exchange energy, Δ<i>E</i><sub><i>J</i></sub>, between the Co atoms through tuning of the capping ligands. The experimentally synthesized Co(II) dimer molecule has a very small Δ<i>E</i><sub><i>J</i></sub> < 1 meV. We assemble a DFT data set of 1081 ligand substitutions for the Co(II) dimer. The ligand exchange provides a broad range of exchange energies, Δ<i>E</i><sub><i>J</i></sub>, from +50 to -200 meV, with 80% of the ligands yielding a small Δ<i>E</i><sub><i>J</i></sub> < 10 meV. We identify descriptors for the classification and regression of Δ<i>E</i><sub><i>J</i></sub> using gradient boosting machine learning models. We compare one-hot encoded, structure-based, and chemical descriptors consisting of the HOMO/LUMO energies of the individual ligands and the maximum electronegativity difference and bond order for the ligand atom connecting to Co. We observe a similar overall performance with the chemical descriptors outperforming the other descriptors. We show that the exchange coupling, Δ<i>E</i><sub><i>J</i></sub>, is correlated to the difference in the average bridging angle between the ferromagnetic and antiferromagnetic states, similar to the Goodenough-Kanamori rules. Electronegativity ; Density functional theory ; Antiferromagnetism ; Dimer ; Chemistry ; Molecule ; Ligand (biochemistry) ; Ferromagnetism ; Computational chemistry ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Magnetism in coordination complexes ; Surface Chemistry and Catalysis
Authors: Damilola O. Akamo | Ahmed Olanrewaju Ijaola | Toyosi T. George | Katharine Page | David J. Keffer | Yuzhan Li | Monojoy Goswami | Tim J. LaClair | Kyle Gluesenkamp | Orlando Rios
Source: European Journal of Materials (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Bibliometric review and recent advances in total scattering pair distribution function analysis: 21 years in retrospect Global research activities have been driven by the quest to develop and characterize novel materials for technological advancements. The total scattering pair distribution function (TSPDF) is a powerful and versatile characterization technique for examining the structural details of diverse complex materials including liquid, amorphous, disordered crystalline, and nanostructured materials. Thus, it is critical to keep track of research progress, identify research gaps, and future research directions of the application of the TSPDF technique in materials development and discovery. In this work, a bibliometric analysis of literature regarding the TSPDF technique between 2000 and 2021 was conducted using datasets retrieved from the Web of Science database. The research trends based on publication outputs, research subject distribution, co-authorships among institutions, countries/regions, co-citation of referenced sources, and keyword co-occurrence are evaluated and discussed herein. The impact of the TSPDF technique is projected to increase due to its importance in probing emerging functional materials, and the advances in specialized facilities and instrumentation among the scientific communities engaged with it. Finally, current and emerging research hotspots related to TSPDF technique such as catalysis, computer modeling and simulation, pharmaceutics, machine learning, hydrogen storage, battery materials, and layered structured materials are also identified and discussed. Data science ; Function (biology) ; Computer science ; Nanotechnology ; Characterization (materials science) ; Materials science X-ray Diffraction in Crystallography ; Crystal Structures and Properties ; Pigment Synthesis and Properties
Authors: Atefe Hasani | Mohammd Reza Shojaei | Gholam Reza Khayati
Source: Materials Letters (article, 2022-08-18)
Field: Materials Science — Materials Chemistry

Evolving application of machine learning in the synthesis of CHA/ZrO2 nanocomposite for the microhardness prediction Indentation hardness ; Nanocomposite ; Support vector machine ; Materials science ; Least squares support vector machine ; Artificial intelligence ; Computer science ; Machine learning ; Algorithm ; Composite material ; Microstructure Machine Learning in Materials Science ; Bone Tissue Engineering Materials ; Fuel Cells and Related Materials