jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yasmeen AlFaraj | Somesh Mohapatra | Peyton Shieh | Keith E. L. Husted | Douglas G. Ivanoff | Evan M. Lloyd | Julian Cooper | Yutong Dai | Avni Singhal | Jeffrey S. Moore | Nancy R. Sottos | Rafael Gómez‐Bombarelli | Jeremiah A. Johnson
Source: ChemRxiv (preprint, 2023-01-16)
Field: Materials Science — Materials Chemistry

A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low Data Regime Data science can accelerate materials discovery by learning composition-processing-performance models from pre-existing data sets, which then feed into active learning cycles in the laboratory. Thermoset polymer waste is a pressing environmental challenge that may be addressed by the accelerated discovery of new deconstructable variants; however, the combinatorial space of possible monomers, crosslinkers, additives, and manufacturing conditions is vast and Edisonian experimentation may struggle to find optimal designs. Moreover, data-driven strategies are limited for complex (co)polymers like thermosets because the training data is scarce and sourced from heterogeneous experimental approaches, resulting in overfit transferable models. Here, we introduce a novel closed-loop approach to the predictive design of chemically deconstructable thermosets that leverages experimental synthesis and characterization, machine learning, and virtual screening. Our computational model learns to map the molecular features of bifunctional silyl ether (BSE)-based cleavable comonomers to the thermal properties of the industrial thermoset polydicyclopentadiene (pDCPD). We address the challenges of limited data and overfitting by relying on both structural and information-rich domain-specific molecular features as inputs and by thoroughly quantifying model uncertainty. By training an ensemble of predictive models mixing multiple model architectures and parametrizations, our approach achieves predictions of a key thermoset parameter—the glass transition temperature—within less than 15 °C error over a wide temperature range with only 101 data points. The trained models were used to screen new possible BSE comonomer compositions and synthesis conditions, with promising combinations successfully validated experimentally. This work offers a closed-loop design process that we expect to be widely applicable to the discovery of deconstructable polymeric materials. Overfitting ; Thermosetting polymer ; Computer science ; Data-driven ; Dataflow ; Ensemble forecasting ; Comonomer ; Artificial intelligence ; Biological system ; Machine learning ; Materials science ; Polymer ; Monomer ; Artificial neural network Machine Learning in Materials Science ; Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization
Authors: Yiqing Hao | Erxi Feng | Dan Lu | Leah Zimmer | Zachary Morgan | Bryan C. Chakoumakos | Guannan Zhang | Huibo Cao
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

Machine-learning assisted automation of single-crystal neutron diffraction Neutron scattering is a versatile and powerful technique widely used in materials science to gain insights into materials' properties and uncover new materials.However, this method is often expensive and time-consuming, requiring advanced detector technology and complex data reduction and analysis procedures.Machine learning (ML) has opened new avenues for neutron diffraction data reduction and experiment operation.In this regard, an ML-assisted data reduction and analysis method has been proposed and utilized at HB-3A DEMAND at HFIR, ORNL.The method is designed to recognize Bragg peaks with high precision and identify the corresponding regions of interest.Once the peaks are recognized, the method can automatically align a measured crystal and optimize the data collection using user-provided information and uncertainty quantification values of the detected peaks.The proposed method has been shown to perform exceptionally well in various complex sample environments, enabling automated single-crystal neutron diffraction.This method's success can dramatically accelerate the discovery of new materials with unique properties.The ability to automate the data collection process will also free up valuable time for researchers to focus on other aspects of their research, leading to more efficient and productive experiments. Neutron diffraction ; Materials science ; Single crystal ; Automation ; Crystallography ; Computer science ; Chemistry ; Engineering ; Mechanical engineering ; Crystal structure X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Albert Thie | Maximilian F. S. J. Menger | Shirin Faraji
Source: arXiv (Cornell University) (preprint, 2023-02-01)
Field: Materials Science — Materials Chemistry

HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks Computational chemistry has become an important tool to predict and understand molecular properties and reactions. Even though recent years have seen a significant growth in new algorithms and computational methods that speed up quantum chemical calculations, the bottleneck for trajectory-based methods to study photoinduced processes is still the huge number of electronic structure calculations. In this work, we present an innovative solution, in which the amount of electronic structure calculations is drastically reduced, by employing machine learning algorithms and methods borrowed from the realm of artificial intelligence. However, applying these algorithms effectively requires finding optimal hyperparameters, which remains a challenge itself. Here we present an automated user-friendly framework, HOAX, to perform the hyperparameter optimization for neural networks, which bypasses the need for a lengthy manual process. The neural network generated potential energy surfaces (PESs) reduces the computational costs compared to the ab initio-based PESs. We perform a comparative investigation on the performance of different hyperparameter optimiziation algorithms, namely grid search, simulated annealing, genetic algorithm, and bayesian optimizer in finding the optimal hyperparameters necessary for constructing the well-performing neural network in order to fit the PESs of small organic molecules. Our results show that this automated toolkit not only facilitate a straightforward way to perform the hyperparameter optimization but also the resulting neural networks-based generated PESs are in reasonable agreement with the ab initio-based PESs. Hyperparameter optimization ; Hyperparameter ; Computer science ; Machine learning ; Artificial neural network ; Artificial intelligence ; Bayesian optimization ; Bottleneck ; Algorithm Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Xiang Zhang | Zichun Zhou | Ming Chen | Yi‐Yang Sun
Source: arXiv (Cornell University) (preprint, 2023-06-25)
Field: Materials Science — Materials Chemistry

GPT-assisted learning of structure-property relationships by graph neural networks: Application to rare-earth doped phosphors Applications of machine learning techniques in materials science are often based on two key ingredients, a set of empirical descriptors and a database of a particular material property of interest. The advent of graph neural networks, such as the Crystal Graph Convolutional Neural Network (CGCNN), demonstrates the possibility of directly mapping the relationship between material structures and properties without employing empirical descriptors. Another exciting recent advancement is in large language models such as OpenAI's GPT-4, which demonstrates competency at reading comprehension tasks and holds great promise for accelerating the acquisition of databases on material properties. Here, we utilize the combination of GPT-4 and CGCNN to develop rare-earth doped phosphors for solid-state lighting. GPT-4 is applied to data-mine chemical formulas and emission wavelengths of 264 Eu(II)-doped phosphors from 274 papers. A CGCNN model is trained on the acquired dataset, achieving a test $R^2$ of 0.77. The model is then used to screen over 40,000 inorganic materials to make predictions on the emission wavelengths. We also demonstrate the possibility of leveraging transfer learning to fine-tune a bandgap-predicting CGCNN model towards the prediction of phosphor emission wavelengths. The workflow requires minimal human supervision, little domain knowledge about phosphors, and is generalizable to other material properties. Phosphor ; Computer science ; Workflow ; Convolutional neural network ; Artificial neural network ; Graph ; Doping ; Artificial intelligence ; Machine learning ; Materials science ; Database ; Optoelectronics ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Salatan Duangdangchote | Oleksandr Voznyy
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Materials Graph Networks Assisting the Discovery of New Solid-State Electrolyte Materials Finding novel solid-state electrolyte materials with specific desired properties is one of the main challenges of first principles-based modelling due to its high computational cost. Recently, machine learning (ML) has been used exclusively in the materials discovery field due to its remarkable capabilities to process large amounts of data and extract useful information insights. The implementation of ML into atomic-scale materials modeling can accelerated materials sampling with first principles accuracy, this should provide a short workflow and highly reduce the computational cost. In this work, we facilitate the ML model that can effectively screen for targeted properties from the entire chemical database possibility, including those materials that have never been examined. Currently, we are developing a materials graph networks framework for representing periodic crystal systems with the capability to learn atomistic chemical insights, especially for the discovery of new solid-state electrolyte materials. In this talk, we will discuss the basic theory and our recent work towards the development of ML model that can extensively discover and explore materials. We will highlight the computational-guided evolution approaches and screening high-performance Li conductor materials. Finally, we will end on discussing the future of ML-assisted materials discovery to the future of all-solid-state lithium batteries. Workflow ; Computer science ; Solid-state ; Graph ; Scientific discovery ; Process (computing) ; Nanotechnology ; Data science ; Theoretical computer science ; Materials science ; Engineering ; Database ; Engineering physics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Jun-Zhe Hao | Shiai Xu | Jiajun Xu | Hai-Lian Cao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Modeling and Optimization of the Corrosion Resistance of Cr-Free And Cr-Based Chemical Conversion Coatings on Nickel Foil by Artificial Neural Network and Response Surface Method FOIL method ; Nickel ; Corrosion ; Materials science ; Artificial neural network ; Metallurgy ; Response surface methodology ; Composite material ; Artificial intelligence ; Computer science ; Machine learning Corrosion Behavior and Inhibition ; Surface Roughness and Optical Measurements ; Welding Techniques and Residual Stresses
Authors: Evan Walter Clark Spotte‐Smith | Orion Cohen | Samuel M. Blau | Jason M. Munro | Ruoxi Yang | Rishabh D. Guha | Hetal D. Patel | Sudarshan Vijay | Patrick Huck | Ryan Kingsbury | Matthew K. Horton | Kristin A. Persson
Source: ChemRxiv (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

A database of molecular properties integrated in the Materials Project Advanced chemical research is increasingly reliant on large computed datasets of molecules and reactions to discover new functional molecules, understand chemical trends, train machine learning models, and more. To be of greatest use to the scientific community, such datasets should follow FAIR principles (i.e. be findable, accessible, interoperable, and reusable). In this work, we present a FAIR expansion of the Materials Project database ("MPcules") that adds more than 170,000 molecules studied using density functional theory (DFT) to the existing data, which comprises crystalline solids. MPcules is a diverse collection of DFT-calculated molecular properties, with an emphasis on reactive, open-shell, and charged species - relevant for studying reaction pathways - and a wide array of structural, electronic, vibrational, and thermodynamic properties. This database can be queried through an OpenAPI-compliant application programming interface and a featureful web application. We continue to expand the data available on MPcules and encourage contributions from the community. Interoperability ; Interface (matter) ; Computer science ; Chemical database ; Database ; Application programming interface ; Density functional theory ; Work (physics) ; Data science ; World Wide Web ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Matthew Witman
Source:  (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Optimizing Thermodynamic Properties of High Entropy Alloys and Hydrides with Lattice Graph Neural Network Models Artificial neural network ; High entropy alloys ; Entropy (arrow of time) ; Statistical physics ; Thermodynamics ; Materials science ; Computer science ; Artificial intelligence ; Physics ; Metallurgy ; Microstructure Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Advanced Chemical Physics Studies
Authors: Liang Gu | Yang Liu | Pin Chen | Haiyou Huang | Ning Chen | Yang Li | Yutong Lu | Yanjing Su
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Searching High Temperature Superconductors with the assistance of Graph Neural Networks Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progress in material informatics has led to a number of machine learning models predicting Tc, which greatly improves the efficiency of prediction. Unfortunately, prevailing models have not shown adequate physical rationality and generalization ability to find new high temperature superconductors, yet. In this work, in order to give a trustable prediction on the unexplored materials, we built a bond-sensitive graph neural network (BSGNN), which is optimized to process the information of chemical bond and electron interaction in the crystal lattice, to predict the Tc maximum of each type of superconducting materials. On the basis of the domain knowledge considered in the data preparation and algorithm design, our model revealed a relevance between the Tc-Tc maximum and chemical bonds. The results indicate that shorter bond length is favored by high Tc, which is in accordance with previous human experience. Moreover, it also shows that some specific chemical elements are favored by high Tc, which is beyond what human experts already knew. It gives a convenient guidance for searching high temperature superconductors in materials database, by ruling out the materials that could never have high Tc. Superconductivity ; Artificial neural network ; Lattice (music) ; Graph ; Computer science ; Machine learning ; Chemical bond ; Transition temperature ; Generalization ; Crystal (programming language) ; Artificial intelligence ; Condensed matter physics ; Materials science ; Physics ; Theoretical computer science ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; History and advancements in chemistry
Authors: Qi ZHANG | Chao Peng | Dongfeng Xue
Source: Scientia Sinica Technologica (article, 2023-06-28)
Field: Materials Science — Materials Chemistry

Data-driven approaches enabling the screening and design of promising materials for energy storage batteries <p indent="0mm">Data-driven approaches enabling the development of new materials are a research focus of the fourth research paradigm that promotes material innovation and accelerates the cross-integration of multidisciplinary and multifield material applications. As a crucial data-driven approach Integrating the first-principles method, Machine learning (ML) shows great potential to address the issues in interdisciplinary fields, such as materials science, chemistry, physics, and computer science, and elicits a promising avenue for the rapid development of new materials for energy storage batteries. To better understand this emerging field, this review systematically outlines the latest progress in high-throughput computational screening and ML that show great potential in the research of energy storage battery materials. Moreover, online material databases that are widely used in the discovery and design of materials are summarized, the construction of a new database is presented along with an example, and several issues encountered during data collection are discussed. Furthermore, this review discusses in detail the application examples associated with ML methods in terms of high-throughput computational screening, material property prediction, structure–electrochemical performance relationship, and material design. Finally, the challenges of ML in the field of energy storage batteries are analyzed and discussed. In addition, we provide perspectives on the applications of ML in energy storage materials. Energy storage ; Nanotechnology ; Computer science ; Process engineering ; Materials science ; Systems engineering ; Engineering ; Power (physics) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: N. M. Chtchelkatchev | R. E. Ryltsev | M. V. Magnitskaya | S. M. Gorbunov | Kirill A. Cherednichenko | Vladimir L. Solozhenko | V. V. Brazhkin
Source: arXiv (Cornell University) (preprint, 2023-05-11)
Field: Materials Science — Materials Chemistry

Local structure, thermodynamics, and melting curve of boron phosphide at high pressures by deep learning-driven ab initio simulations Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is also poorly understood because both experimental and conventional ab initio methods are restricted to studying refractory covalent materials. The use of machine learning interatomic potentials is a revolutionary trend that gives a unique opportunity for high-temperature study of materials with ab initio accuracy. We develop a deep machine learning potential (DP) for accurate atomistic simulations of solid and liquid phases of BP as well as their transformations near the melting line. Our DP provides quantitative agreement with experimental and ab initio molecular dynamics data for structural and dynamic properties. DP-based simulations reveal that at ambient pressure tetrahedrally bonded cubic BP crystal melts into an open structure consisting of two interpenetrating sub-networks of boron and phosphorous with different structures. Structure transformations of BP melts under compressing are reflected by the evolution of low-pressure tetrahedral coordination to high-pressure octahedral coordination. The main contributions to structural changes at low pressures are made by the evolution of medium-range order in B-subnetwork and at high pressures by the change of short-range order in P-sub-network. Such transformations exhibit an anomalous behavior of structural characteristics in the range of 12--15 GPa. Analysis of the results obtained raise open issues in developing machine learning potentials for covalent materials and stimulate further experimental and theoretical studies of melting behavior in BP. Ab initio ; Materials science ; Thermodynamics ; Ab initio quantum chemistry methods ; Chemical physics ; Chemistry ; Molecule ; Physics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Thermal properties of materials
Authors: Linus C. Erhard | Jochen Rohrer | Karsten Albe | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2023-09-07)
Field: Materials Science — Materials Chemistry

Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si-O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si-O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning. Silicon ; Context (archaeology) ; Nanoscopic scale ; Workflow ; Materials science ; Amorphous silica ; Nanotechnology ; Nanometre ; Atomic units ; Semiconductor ; Scale (ratio) ; Silicon dioxide ; Amorphous solid ; Computer science ; Chemistry ; Optoelectronics ; Engineering ; Chemical engineering ; Physics ; Crystallography ; Metallurgy Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research