jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kanishka Singh | Jannes Münchmeyer | Leon Weber | Ulf Leser | Annika Bande
Source: Journal of Chemical Theory and Computation (article, 2022-06-07)
Field: Materials Science — Materials Chemistry

Graph Neural Networks for Learning Molecular Excitation Spectra Machine learning (ML) approaches have demonstrated the ability to predict molecular spectra at a fraction of the computational cost of traditional theoretical chemistry methods while maintaining high accuracy. Graph neural networks (GNNs) are particularly promising in this regard, but different types of GNNs have not yet been systematically compared. In this work, we benchmark and analyze five different GNNs for the prediction of excitation spectra from the QM9 dataset of organic molecules. We compare the GNN performance in the obvious runtime measurements, prediction accuracy, and analysis of outliers in the test set. Moreover, through TMAP clustering and statistical analysis, we are able to highlight clear hotspots of high prediction errors as well as optimal spectra prediction for molecules with certain functional groups. This in-depth benchmarking and subsequent analysis protocol lays down a recipe for comparing different ML methods and evaluating dataset quality. Benchmarking ; Computer science ; Benchmark (surveying) ; Artificial neural network ; Artificial intelligence ; Outlier ; Machine learning ; Test set ; Graph ; Set (abstract data type) ; Cluster analysis ; Data mining ; Pattern recognition (psychology) ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Sungwoo Kang | Wonseok Jeong | Chang‐Ho Hong | Seungwoo Hwang | Youngchae Yoon | Seungwu Han
Source: npj Computational Materials (article, 2022-05-12)
Field: Materials Science — Materials Chemistry

Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials Abstract The discovery of multicomponent inorganic compounds can provide direct solutions to scientific and engineering challenges, yet the vast uncharted material space dwarfs synthesis throughput. While the crystal structure prediction (CSP) may mitigate this frustration, the exponential complexity of CSP and expensive density functional theory (DFT) calculations prohibit material exploration at scale. Herein, we introduce SPINNER, a structure-prediction framework based on random and evolutionary searches. Harnessing speed and accuracy of neural network potentials (NNPs), the program navigates configurational spaces 10 2 –10 3 times faster than DFT-based methods. Furthermore, SPINNER incorporates algorithms tuned for NNPs, achieving performances exceeding conventional algorithms. In blind tests on 60 ternary compositions, SPINNER identifies experimental (or theoretically more stable) phases for ~80% of materials. When benchmarked against data-mining or DFT-based evolutionary predictions, SPINNER identifies more stable phases in many cases. By developing a reliable and fast structure-prediction framework, this work paves the way to large-scale, open exploration of undiscovered inorganic crystals. Computer science ; Density functional theory ; Crystal structure prediction ; Ternary operation ; Space (punctuation) ; Chemical space ; Identification (biology) ; Scale (ratio) ; Throughput ; Machine learning ; Artificial neural network ; Algorithm ; Artificial intelligence ; Statistical physics ; Theoretical computer science ; Crystal structure ; Physics ; Chemistry ; Computational chemistry ; Drug discovery ; Biology ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Mary M. Bajomo | Yilong Ju | Jingyi Zhou | Simina Elefterescu | Corbin Farr | Yiping Zhao | Oara Neumann | Peter Nordlander | Ankit Patel | Naomi J. Halas
Source: Proceedings of the National Academy of Sciences (article, 2022-12-19)
Field: Chemistry — Analytical Chemistry

Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures Surface-enhanced Raman spectroscopy (SERS) holds exceptional promise as a streamlined chemical detection strategy for biological and environmental contaminants compared with current laboratory methods. Priority pollutants such as polycyclic aromatic hydrocarbons (PAHs), detectable in water and soil worldwide and known to induce multiple adverse health effects upon human exposure, are typically found in multicomponent mixtures. By combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of machine learning (ML), we examine whether individual PAHs can be identified through an analysis of the SERS spectra of multicomponent PAH mixtures. We have developed an unsupervised ML method we call Characteristic Peak Extraction, a dimensionality reduction algorithm that extracts characteristic SERS peaks based on counts of detected peaks of the mixture. By analyzing the SERS spectra of two-component and four-component PAH mixtures where the concentration ratios of the various components vary, this algorithm is able to extract the spectra of each unknown component in the mixture of unknowns, which is then subsequently identified against a SERS spectral library of PAHs. Combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of ML, this effort is a step toward the computational demixing of unknown chemical components occurring in complex multicomponent mixtures. Component (thermodynamics) ; Raman spectroscopy ; Chemistry ; Biological system ; Chemometrics ; Surface-enhanced Raman spectroscopy ; Spectral line ; Curse of dimensionality ; Chromatography ; Analytical Chemistry (journal) ; Artificial intelligence ; Computer science ; Raman scattering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Hongtao Zhang | Huadong Fu | Yuheng Shen | Jianxin Xie
Source: International Journal of Minerals Metallurgy and Materials (article, 2022-05-23)
Field: Materials Science — Materials Chemistry

Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning Alloy ; Materials science ; Bayesian optimization ; Deformation (meteorology) ; Ultimate tensile strength ; Copper ; Electrical resistivity and conductivity ; Metallurgy ; Precipitation ; Composite material ; Computer science ; Artificial intelligence ; Engineering ; Electrical engineering Microstructure and mechanical properties ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Ying Zhou | Prashanth Srinivasan | Fritz Körmann | Blazej Grabowski | Roger Smith | Pooja Goddard | Andrew Ian Duff
Source: Physical review. B./Physical review. B (article, 2022-06-10)
Field: Materials Science — Materials Chemistry

Thermodynamics up to the melting point in a TaVCrW high entropy alloy: Systematic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>a</mml:mi><mml:mi>b</mml:mi></mml:mrow><mml:mo> </mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi></mml:mrow></mml:math> study aided by machine learning potentials Multi-principal-component alloys have attracted great interest as a novel paradigm in alloy design, with often unique properties and a vast compositional space auspicious for materials discovery. High entropy alloys (HEAs) belong to this class and are being investigated for prospective nuclear applications with reported superior mechanical properties including high-temperature strength and stability compared to conventional alloys. Computational materials design has the potential to play a key role in screening such alloys, yet for high-temperature properties, challenges remain in finding an appropriate balance between accuracy and computational cost. Here we develop an approach based on density-functional theory (DFT) and thermodynamic integration aided by machine learning based interatomic potential models to address this challenge. We systematically evaluate and compare the efficiency of computing the full free energy surface and thermodynamic properties up to the melting point at different stages of the thermodynamic integration scheme. Our new approach provides a $\ifmmode\times\else\texttimes\fi{}4$ speed-up with respect to comparable free energy approaches at the level of DFT, with errors on high-temperature free energy predictions less than 1 meV/atom. Calculations are performed on an equiatomic HEA, TaVCrW---a low-activation composition and therefore of potential interest for next generation fission and fusion reactors. Thermodynamics ; Melting point ; Materials science ; Computer science ; Density functional theory ; High entropy alloys ; Alloy ; Machine learning ; Statistical physics ; Algorithm ; Physics ; Chemistry ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Nuclear Materials and Properties
Authors: Tung Nguyen | Mona Bavarian
Source: Industrial & Engineering Chemistry Research (article, 2022-08-09)
Field: Materials Science — Materials Chemistry

A Machine Learning Framework for Predicting the Glass Transition Temperature of Homopolymers Technological advances and the need for new polymers necessitate continuous research in the design and identification of polymers with specific physical and chemical properties. Among the different properties of polymers, the glass transition temperature is a key property in determining the specific application and the required processing conditions. Due to the complexity of polymers, realizing a novel polymer for a certain application still presents many obstacles. Thus, computational approaches that allow for gaining information on the properties of a designed polymer, a priori, are advantageous. Here, to predict the glass transition temperatures of polymers, we investigate the learning performance of both the graph attention network (GAN) and convolutional neural network (CNN). We utilize five different features of polymers such as atom symbol, neighboring atoms, atom mask, bond types, and neighboring bonds as inputs to the GAN model. For the CNN model, we use image pixels of the chemical structures as input variables. Both optimization and training set duplication are utilized to find the appropriate hyperparameters for improving the models’ prediction. Here, for the tested homopolymers, we found that the GAN model outperforms the CNN model for predicting the glass transition temperature. Furthermore, the attention mechanism showed its capability in helping the machine learning model to extract important information and facilitate better predictions of the glass transition temperature. Glass transition ; Polymer ; Computer science ; Hyperparameter ; Convolutional neural network ; Materials science ; Artificial intelligence ; Biological system ; Machine learning ; Algorithm ; Composite material Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: C. Lawrence Zitnick | Abhishek Das | Adeesh Kolluru | Janice Lan | Muhammed Shuaibi | Anuroop Sriram | Zachary W. Ulissi | Brandon C. Wood
Source: arXiv (Cornell University) (preprint, 2022-06-29)
Field: Materials Science — Materials Chemistry

Spherical Channels for Modeling Atomic Interactions Modeling the energy and forces of atomic systems is a fundamental problem in computational chemistry with the potential to help address many of the world's most pressing problems, including those related to energy scarcity and climate change. These calculations are traditionally performed using Density Functional Theory, which is computationally very expensive. Machine learning has the potential to dramatically improve the efficiency of these calculations from days or hours to seconds. We propose the Spherical Channel Network (SCN) to model atomic energies and forces. The SCN is a graph neural network where nodes represent atoms and edges their neighboring atoms. The atom embeddings are a set of spherical functions, called spherical channels, represented using spherical harmonics. We demonstrate, that by rotating the embeddings based on the 3D edge orientation, more information may be utilized while maintaining the rotational equivariance of the messages. While equivariance is a desirable property, we find that by relaxing this constraint in both message passing and aggregation, improved accuracy may be achieved. We demonstrate state-of-the-art results on the large-scale Open Catalyst dataset in both energy and force prediction for numerous tasks and metrics. Spherical harmonics ; Computer science ; Constraint (computer-aided design) ; Atom (system on chip) ; Property (philosophy) ; Enhanced Data Rates for GSM Evolution ; Topology (electrical circuits) ; Statistical physics ; Theoretical computer science ; Physics ; Geometry ; Mathematics ; Artificial intelligence ; Combinatorics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Yucheng Ji | Ni Li | Zhanming Cheng | Xiaoqian Fu | Min Ao | Menglin Li | Xiaoguang Sun | Thee Chowwanonthapunya | Dawei Zhang | Kui Xiao | Jingli Ren | Poulumi Dey | Xiaogang Li | Chaofang Dong
Source: npj Materials Degradation (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

Random forest incorporating ab-initio calculations for corrosion rate prediction with small sample Al alloys data Abstract Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we propose a strategy Integrating Ab-initio Calculations with Random Forest (IACRF) to optimize the model, thereby estimating the corrosion rate of Al alloys in diverse environments. Based on the thermodynamic assessment of the secondary phases, the ab-initio calculation quantities, especially the work function, significantly improved the prediction accuracy with respect to small-sample Al alloys corrosion dataset. To build a better generic prediction model, the most accessible and effective features are identified to train IACRF. Finally, the independent field exposure experiments in Southeast Asia have proven the generalization ability of IACRF in which the average prediction accuracy is improved up to 91%. Corrosion ; Random forest ; Generalization ; Ab initio ; Field (mathematics) ; Sample size determination ; Computer science ; Sample (material) ; Data mining ; Materials science ; Machine learning ; Artificial intelligence ; Statistics ; Mathematics ; Metallurgy ; Thermodynamics ; Chemistry ; Physics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Koushik Pal | Cheol Woo Park | Yi Xia | Jiahong Shen | Chris Wolverton
Source: npj Computational Materials (article, 2022-03-24)
Field: Materials Science — Materials Chemistry

Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity Abstract We design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM′Q 3 (A/M/M ' = alkali, alkaline earth, post-transition metals, lanthanides, and Q = chalcogens). These compounds are shown to possess ultralow lattice thermal conductivity ( κ l ), a desired requirement for thermal-barrier coatings and thermoelectrics. Upon screening the thermodynamic stability of ~1 million compounds using the ML model iteratively and performing density-functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their κ l using Peierls–Boltzmann transport equation, which reveals ultralow κ l (&lt;2 Wm −1 K −1 at room temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high efficiency of scale-invariant ML model in predicting novel compounds and presents experimental-research opportunities with these new compounds. Phonon ; Materials science ; Density functional theory ; Thermal conductivity ; Boltzmann equation ; Thermodynamics ; Anharmonicity ; Machine learning ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry ; Computer science Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Zhiheng Cheng | Chaolun Wang | Xing Wu | Junhao Chu
Source: Journal of Semiconductors (article, 2022-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Review in situ transmission electron microscope with machine learning Abstract Advanced electronic materials are the fundamental building blocks of integrated circuits (ICs). The microscale properties of electronic materials (e.g., crystal structures, defects, and chemical properties) can have a considerable impact on the performance of ICs. Comprehensive characterization and analysis of the material in real time with high-spatial resolution are indispensable. In situ transmission electron microscope (TEM) with atomic resolution and external field can be applied as a physical simulation platform to study the evolution of electronic material in working conditions. The high-speed camera of the in situ TEM generates a high frame rate video, resulting in a large dataset that is beyond the data processing ability of researchers using the traditional method. To overcome this challenge, many works on automated TEM analysis by using machine-learning algorithm have been proposed. In this review, we introduce the technical evolution of TEM data acquisition, including analysis, and we summarize the application of machine learning to TEM data analysis in the aspects of morphology, defect, structure, and spectra. Some of the challenges of automated TEM analysis are given in the conclusion. Microscale chemistry ; Characterization (materials science) ; Computer science ; Transmission electron microscopy ; In situ ; High-resolution transmission electron microscopy ; Transmission (telecommunications) ; Materials science ; Nanotechnology ; Artificial intelligence ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Ryan C. Cammarota | Wenbin Liu | John Bacsa | Huw M. L. Davies | Matthew S. Sigman
Source: Journal of the American Chemical Society (article, 2022-01-24)
Field: Chemistry — Organic Chemistry

Mechanistically Guided Workflow for Relating Complex Reactive Site Topologies to Catalyst Performance in C–H Functionalization Reactions Leveraging congested catalyst scaffolds has emerged as a key strategy for altering innate substrate site-selectivity profiles in C-H functionalization reactions. Similar to enzyme active sites, optimal small molecule catalysts often feature reactive cavities tailored for controlling substrate approach trajectories. However, relating three-dimensional catalyst shape to reaction output remains a formidable challenge, in part due to the lack of molecular features capable of succinctly describing complex reactive site topologies in terms of numerical inputs for machine learning applications. Herein, we present a new set of descriptors, "Spatial Molding for Approachable Rigid Targets" (SMART), which we have applied to quantify reactive site spatial constraints for an expansive library of dirhodium catalysts and to predict site-selectivity for C-H functionalization of 1-bromo-4-pentylbenzene via donor/acceptor carbene intermediates. Optimal site-selectivity for the terminal methylene position was obtained with Rh<sub>2</sub>(<i>S</i>-2-Cl-5-MesTPCP)<sub>4</sub> (30.9:1 rr, 14:1 dr, 87% ee), while C-H functionalization at the electronically activated benzylic site was increasingly favored for Rh<sub>2</sub>(TPCP)<sub>4</sub> catalysts lacking an <i>ortho</i>-Cl, Rh<sub>2</sub>(<i>S</i>-PTAD)<sub>4</sub>, and Rh<sub>2</sub>(<i>S</i>-TCPTAD)<sub>4</sub>, respectively. Intuitive global site-selectivity models for 25 disparate dirhodium catalysts were developed via multivariate linear regression to explicitly assess the contributing roles of steric congestion and dirhodium-carbene electrophilicity in controlling the site of C-H functionalization. The workflow utilizes spatial classification to extract descriptors only for reactive catalyst conformers, a nuance that may be widely applicable for establishing close correspondence between ground-state model systems and transition states. Broader still, SMART descriptors are amenable for delineating salient reactive site features to predict reactivity in other chemical and biological contexts. Chemistry ; Electrophile ; Carbene ; Catalysis ; Selectivity ; Steric effects ; Combinatorial chemistry ; Surface modification ; Substrate (aquarium) ; Stereochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Cyclopropane Reaction Mechanisms ; Machine Learning in Materials Science
Authors: Martin Seifrid | Jason Hattrick‐Simpers | Alán Aspuru‐Guzik | Tom Kalil | Steve Cranford
Source: Matter (article, 2022-06-08)
Field: Materials Science — Materials Chemistry

Reaching critical MASS: Crowdsourcing designs for the next generation of materials acceleration platforms Exploit ; Crowdsourcing ; Data science ; Acceleration ; Computer science ; Critical mass (sociodynamics) ; Automation ; Artificial intelligence ; Engineering ; Mechanical engineering ; Computer security ; World Wide Web Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Siddhartha Nanda | Sourav Ghosh | Tiju Thomas
Source: Journal of Power Sources (article, 2022-08-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning aided cyclic stability prediction for supercapacitors Supercapacitor ; Interpretability ; Capacitance ; Stability (learning theory) ; Computer science ; Energy storage ; Electricity ; Key (lock) ; Electrolyte ; Renewable energy ; Pseudocapacitor ; Materials science ; Process engineering ; Electrode ; Artificial intelligence ; Machine learning ; Electrical engineering ; Engineering ; Chemistry ; Power (physics) Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research