jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guojing Cong | Victor Fung
Source: Machine Learning Science and Technology (article, 2023-08-11)
Field: Materials Science — Materials Chemistry

Improving materials property predictions for graph neural networks with minimal feature engineering <sup>*</sup> Abstract Graph neural networks (GNNs) have been employed in materials research to predict physical and functional properties, and have achieved superior performance in several application domains over prior machine learning approaches. Recent studies incorporate features of increasing complexity such as Gaussian radial functions, plane wave functions, and angular terms to augment the neural network models, with the expectation that these features are critical for achieving a high performance. Here, we propose a GNN that adopts edge convolution where hidden edge features evolve during training and extensive attention mechanisms, and operates on simple graphs with atoms as nodes and distances between them as edges. As a result, the same model can be used for very different tasks as no other domain-specific features are used. With a model that uses no feature engineering, we achieve performance comparable with state-of-the-art models with elaborate features for formation energy and band gap prediction with standard benchmarks; we achieve even better performance when the dataset size increases. Although some domain-specific datasets still require hand-crafted features to achieve state-of-the-art results, our selected architecture choices greatly reduce the need for elaborate feature engineering and still maintain predictive power in comparison. Feature engineering ; Computer science ; Gaussian ; Feature (linguistics) ; Convolution (computer science) ; Artificial neural network ; Enhanced Data Rates for GSM Evolution ; Domain (mathematical analysis) ; Artificial intelligence ; Machine learning ; Graph ; Property (philosophy) ; Deep learning ; Pattern recognition (psychology) ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jiheng Fang | Shangrong Yang | Ming Xie | Jieqiong Hu | Hongsheng Sun | Guohua Liu | Shangqiang Zhao | Yongtai Chen | Youcai Yang | dekui ning | Xingqun He | Qinglin Jin
Source: Applied Materials Today (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Solid-liquid phase transition temperature prediction of alloys based on machine learning key feature screening Feature (linguistics) ; Feature selection ; Artificial intelligence ; Interpretability ; Algorithm ; Key (lock) ; Computer science ; Support vector machine ; Component (thermodynamics) ; Machine learning ; Phase (matter) ; Pattern recognition (psychology) ; Chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced materials and composites ; Additive Manufacturing Materials and Processes
Authors: Maximilian Seifermann | Patrick Reiser | Pascal Friederich | Pavel A. Levkin
Source: ChemRxiv (preprint, 2023-02-14)
Field: Materials Science — Materials Chemistry

High-throughput synthesis and machine learning assisted design of photodegradable hydrogels Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein we report an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads is used to train a machine learning (ML) model for automated decision making. Through iterative model optimization based on Bayesian Optimization we achieve a substantial improvement in response properties and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in our study. We therefore demonstrate the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties. Throughput ; Bayesian optimization ; Computer science ; Workflow ; Scope (computer science) ; Self-healing hydrogels ; Process (computing) ; Chemical space ; Machine learning ; Biochemical engineering ; Artificial intelligence ; Materials science ; Engineering ; Chemistry ; Database Machine Learning in Materials Science
Authors: S R Kumaraswamy | Md. Abul Ala Walid | Neetesh Kumar Sharma | Manish Jaimini | Deepak Sharma | Arnab Chakraborty
Source:  (article, 2023-04-11)
Field: Chemistry — Spectroscopy

Compact Firefly Algorithm with Deep Learning Based Chromatic Condition Predictive Model for Organic Synthesis Purification Chromatography is an effective method utilized in organic synthesis to purify and separate chemical compounds. There are many features which affect the efficacy and efficiency of chromatography, comprising the kind of chromatography utilized, the nature of instances, the type and size of columns, type of mobile phase, and flow rate. In recent times, Deep Learning (DL) has the potential to significantly increase the effectiveness and efficiency of chromatography for purification in organic synthesis allowing the analysis and optimizer of difficult procedures at a much quicker rate than is possible with classical approaches. With this motivation, this study develops a novel Compact Firefly Algorithm with Deep Learning based Chromatic Condition Predictive (CFADL-CCP) Model for Organic Synthesis Purification. The presented CFADL-CCP technique mainly predicts the chromatic conditions accurately and timely for organic synthesis purification. In the presented CFADL-CCP technique, two stage pipeline is involved. At the initial stage, the CFADL-CCP technique uses Deep Neural Network (DNN) model for prediction process. Next, in the second stage, the CFA is used for the optimal hyperparameter tuning of the DNN model which helps to accomplish enhanced predictive outcomes. To illustrate the enhanced predictive results of the CFADL-CCP method, an extensive range of simulations were performed. Extensive result analysis shows the betterment of the CFADL-CCP method over other compared methods. Algorithm ; Computer science ; Firefly algorithm ; Artificial intelligence ; Hyperparameter ; Artificial neural network ; Deep learning ; Pipeline (software) ; Machine learning Analytical Chemistry and Chromatography ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Sichao Li | Amanda S. Barnard
Source: Cell Reports Physical Science (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Multi-target neural network predictions of MXenes as high-capacity energy storage materials in a Rashomon set Machine learning models are sensitive to the hyperparameters used by the algorithms and, depending on the method and settings, will place different importance on the (structural) features when predicting (property) labels. This is crucial for machine learning in high-performance materials such as MXenes, known for their design flexibility and increasing complexity. To provide unequivocal structure/property relationships for materials science applications, a universal feature importance profile is needed, which can be obtained by using a Rashomon set of high-performing models, even for so-called “black-box” models. Presented here is a method for developing universal feature importance profiles that can be combined with any neural network to provide an interpretation that is insensitive to factors such as the number of layers or neurons. This approach gives more comprehensive insights into the importance of features and allows researchers to control specific property ranges to accommodate their research interests or needs without compromising performance. MXenes ; Computer science ; Property (philosophy) ; Flexibility (engineering) ; Feature (linguistics) ; Hyperparameter ; Set (abstract data type) ; Artificial neural network ; Artificial intelligence ; Machine learning ; Black box ; Engineering ; Mathematics MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Daniel Hedman | Ben McLean | Christophe Bichara | Shigeo Maruyama | J. Andreas Larsson | Feng Ding
Source: Research Square (preprint, 2023-08-07)
Field: Materials Science — Materials Chemistry

Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations Nucleation ; Carbon nanotube ; Molecular dynamics ; Nanotechnology ; Chemical physics ; Materials science ; Catalysis ; Crystallography ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Advanced Electron Microscopy Techniques and Applications
Authors: Linh Thi Hoai Nguyen | Yasuhide Fukumoto | Pierluigi Cesana | Aleksandar Staykov
Source: The Journal of Physical Chemistry A (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Fully Automatized Optimization of Ring-Opening Reactions in Lactone Derivatives via Two-Step Machine Learning Cyclization and cycloreversion of organic compounds are fundamental kinetic processes in the design of functional molecules, molecular machines, nanoscale sensors, and switches in the field of molecular and nanoelectronics. We present a fully automatic computational platform for the design of a class of five- and six-membered ring lactones by optimizing the ring-opening reaction rate. Starting from a minimal initial parent set, our algorithm generates iteratively cascades of pools of candidate lactone derivatives where optimization and down-selection are performed without human supervision. We employ the density functional theory combined with the transition state theory to elucidate the exact mechanism leading to the lactone ring-opening reaction. On the basis of the analysis of the reaction pathway and the frontier molecular orbitals, we identify a simple descriptor that can easily correlate with the reaction rate. Consequently, we can omit computationally expensive transition state calculations and deduce the reaction rate from simple ground-state and ionic calculations. To accelerate the platform, we use a data set of the order of 800 molecules to train machine learning models for the prediction of targeted chemical properties, reducing the computational time by a 90% factor. We developed an evolutionary algorithm capable of generating data sets 3 orders of magnitude larger than the initial parent set. Thus, we can explore a large domain of chemical space using minimal computational effort. Our entire platform is modular, and our current implementation for lactone can be further generalized to more complex systems via substitution of the quantum chemical and fingerprinting modules. Chemistry ; Chemical space ; Density functional theory ; Ring (chemistry) ; Computational chemistry ; Transition state ; Computer science ; Drug discovery ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Tomohiro Fukushima | Motoki Fukasawa | Kei Murakoshi
Source: The Journal of Physical Chemistry Letters (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Unveiling the Hidden Energy Profiles of the Oxygen Evolution Reaction via Machine Learning Analyses The oxygen evolution reaction (OER) is a crucial electrochemical process for hydrogen production in water electrolysis. However, due to the involvement of multiple proton-coupled electron transfer steps, it is challenging to identify the specific elementary reaction that limits the rate of the OER. Here we employed a machine-learning-based approach to extract the reaction pathway exhaustively from experimental data. Genetic algorithms were applied to search for thermodynamic and kinetic parameters using the current-electrochemical potential relationship of the OER. Interestingly, analysis of the datasets revealed the energy state distributions of reaction intermediates, which likely originated in the interactions among intermediates or the distribution of multiple sites. Through our exhaustive analyses, we successfully uncovered the hidden energy profiles of the OER. This approach can reveal the reaction pathway to activate for efficient hydrogen production, which facilitates the design of catalysts. Oxygen ; Energy (signal processing) ; Artificial intelligence ; Computer science ; Machine learning ; Chemistry ; Statistics ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Mohamad Wijayanuddin Ali | Mohammad Hussein
Source: Asian Journal of Civil Engineering (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Characterization and optimization of mechanical properties in design materials using convolutional neural networks and particle swarm optimization Particle swarm optimization ; Convolutional neural network ; Flexibility (engineering) ; Computer science ; Artificial intelligence ; Property (philosophy) ; Artificial neural network ; Machine learning ; Mathematics Machine Learning in Materials Science ; Advanced machining processes and optimization ; Injection Molding Process and Properties
Authors: Giorgio Domenichini | Christoph Dellago
Source: The Journal of Chemical Physics (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Molecular Hessian matrices from a machine learning random forest regression algorithm In this article, we present a machine learning model to obtain fast and accurate estimates of the molecular Hessian matrix. In this model, based on a random forest, the second derivatives of the energy with respect to redundant internal coordinates are learned individually. The internal coordinates together with their specific representation guarantee rotational and translational invariance. The model is trained on a subset of the QM7 dataset but is shown to be applicable to larger molecules picked from the QM9 dataset. From the predicted Hessian, it is also possible to obtain reasonable estimates of the vibrational frequencies, normal modes, and zero point energies of the molecules. Hessian matrix ; Random forest ; Representation (politics) ; Hessian equation ; Random matrix ; Algorithm ; Point (geometry) ; Matrix (chemical analysis) ; Energy (signal processing) ; Regression ; Computer science ; Mathematics ; Artificial intelligence ; Statistical physics ; Applied mathematics ; Physics ; Mathematical analysis ; Geometry ; Quantum mechanics ; Eigenvalues and eigenvectors ; Statistics ; Chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Protein Structure and Dynamics
Authors: Mohammed Alsalman | Saad M. Alqahtani | Fahhad H. Alharbi
Source: Materials Science in Semiconductor Processing (article, 2023-03-23)
Field: Materials Science — Materials Chemistry

Bandgap energy prediction of senary zincblende III–V semiconductor compounds using machine learning Band gap ; Ternary operation ; Semiconductor ; Materials science ; Semimetal ; Mean squared error ; Condensed matter physics ; Thermodynamics ; Analytical Chemistry (journal) ; Mathematics ; Physics ; Statistics ; Optoelectronics ; Chemistry ; Computer science Machine Learning in Materials Science ; Semiconductor materials and interfaces ; Nuclear Materials and Properties
Authors: Kirill Zinovjev | Lester O. Hedges | Rubén Montagud Andreu | Christopher Woods | Iñaki Tuñón | Marc W. van der Kamp
Source: ChemRxiv (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

emle-engine: a flexible electrostatic machine learning embedding package for multiscale molecular dynamics simulations We present in this work the emle-engine package – the implementation of a new machine learning embedding scheme for hybrid machine learning potential / molecular mechanics (ML/MM) dynamics simulations. The package is based on an embedding scheme that uses a physics-based model of the electronic density and induction with a handful of tuneable parameters derived from in vacuo properties of the subsystem to be embedded. This scheme is completely independent of the in vacuo potential and requires only the positions of the atoms of the machine learning subsystem and the positions and partial charges of the molecular mechanics environment. These characteristics allow emle-engine to be employed in existing QM/MM software. We demonstrate that the implemented electrostatic machine learning embedding scheme (named EMLE) is stable in enhanced-sampling molecular dynamics simulations. Through calculation of free energy surfaces of alanine dipeptide in water with two different ML options for the in vacuo potential and two embedding models, we test the influence of EMLE. When compared to the reference DFT/MM surface, the EMLE embedding is clearly superior to the MM one based on fixed partial charges. The flexibility introduced in the EMLE model by the configurational dependence of the electronic density and the inclusion of the induction energy leads to a systematic reduction in the average and maximum error of the free energy surface when compared to MM embedding. Embedding ; Molecular dynamics ; Computer science ; Energy (signal processing) ; Scheme (mathematics) ; Flexibility (engineering) ; Electrostatics ; Statistical physics ; Topology (electrical circuits) ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Xuefeng Wu | Yanmei Meng | Jinlai Zhang | Jing Wei | Xulei Zhai
Source: Journal of Food Engineering (article, 2023-01-28)
Field: Chemistry — Analytical Chemistry

Amodal segmentation of cane sugar crystal via deep neural networks Segmentation ; Sugar ; Artificial intelligence ; Artificial neural network ; Crystal (programming language) ; Computer science ; Pattern recognition (psychology) ; Feature (linguistics) ; Materials science ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Sugarcane Cultivation and Processing
Authors: Yuuki Sugawara
Source: Macromolecular Chemistry and Physics (article, 2023-09-10)
Field: Materials Science — Materials Chemistry

Machine Learning‐Aided Prediction and Construction of a Descriptor for Polymer Properties: A Case Study on the Lower Critical Solution Temperature of Copolymerized <i>N</i> ‐Isopropylacrylamides Abstract Artificial Intelligence (AI) technology has been increasingly applied in the field of chemistry in recent years. For proof of concept for the application of AI technology to polymer research, a state‐of‐the‐art machine learning (ML) technique is employed to systematically elucidate the lower critical solution temperature (LCST) of N ‐isopropylacrylamide (NIPAAm) copolymers using data collected from the literature. The information on 110 NIPAAm random copolymers is collected to extract their LCSTs and the chemical and physical parameters of the copolymers and comonomers. The ML analysis reveals that the copolymerized ratio, the elemental composition of carbon and oxygen in the comonomers, and the water solubility of the comonomers are crucial parameters affecting the LCST, and the constructed ML model successfully predicts the LCSTs of various NIPAAm copolymers. Furthermore, the genetic algorithm using symbolic regression identifies a simple and comprehensive descriptor for the LCST. This study demonstrates the usefulness of data‐driven ML techniques for polymer research with the rapid and accurate prediction of polymer properties. Lower critical solution temperature ; Copolymer ; Polymer ; Materials science ; Polymer chemistry ; Solubility ; Smart polymer ; Computer science ; Chemistry ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Fuel Cells and Related Materials
Authors: Antonios P. Sarikas | George S. Fanourgakis | Konstantinos Gkagkas | George E. Froudakis
Source: Sustainable Chemistry for the Environment (article, 2023-12-05)
Field: Chemistry — Inorganic Chemistry

Comparison of machine learning approaches for the identification of top-performing materials for hydrogen storage Unique properties of Metal-Organic Frameworks (MOFs), such as their extremely high surface areas and porosity, render them as one of, if not the most promising adsorbents for gas storage applications. However, their extremely flexible and tunable nature has resulted in an enormous expansion of the available material pool which in turn begs the question as to whether an efficient identification of the best materials is feasible. In the last years, Machine Learning (ML) techniques have been extensively applied for the exploration of large material databases, since they can significantly accelerate this process. In this work, “traditional” ML models and models based on our recently developed iterative self-consistent approach are compared with respect to their ability to efficiently identify the best materials of a database. As a case study, we have used hydrogen adsorption in MOFs at different thermodynamic conditions. Despite their high accuracy, traditional models struggle to pinpoint the best materials materials, regarding usable gravimetric uptake, without compromising computational resources. On the other hand, self-consistent models can even reduce by two orders of magnitude the amount of reference data required for the identification of the best gravimetric materials compared to traditional ones. Notably, 300 training samples are enough for the SC models to correctly identify the top-100 gravimetric materials of a database. Nevertheless, both type of models underperform when they are queried for the top-performing materials with regards to usable volumetric uptake. USable ; Gravimetric analysis ; Identification (biology) ; Computer science ; Adsorption ; Hydrogen storage ; Process engineering ; Process (computing) ; Porous medium ; Work (physics) ; Porosity ; Machine learning ; Database ; Hydrogen ; Materials science ; Chemistry ; Mechanical engineering ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Markus J. Buehler
Source: arXiv (Cornell University) (preprint, 2023-01-14)
Field: Materials Science — Biomaterials

Diatom-inspired architected materials using language-based deep learning: Perception, transformation and manufacturing Learning from nature has been a quest of humanity for millennia. While this has taken the form of humans assessing natural designs such as bones, butterfly wings, or spider webs, we can now achieve generating designs using advanced computational algorithms. In this paper we report novel biologically inspired designs of diatom structures, enabled using transformer neural networks, using natural language models to learn, process and transfer insights across manifestations. We illustrate a series of novel diatom-based designs and also report a manufactured specimen, created using additive manufacturing. The method applied here could be expanded to focus on other biological design cues, implement a systematic optimization to meet certain design targets, and include a hybrid set of material design sets. Computer science ; Artificial intelligence ; Deep learning ; Set (abstract data type) ; Artificial neural network ; Transfer of learning ; Transformer ; Machine learning ; Engineering ; Programming language Diatoms and Algae Research