jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiangyang Chen | Hao Wang | Junzong Feng
Source:  (article, 2024-12-29)
Field: Chemistry — Analytical Chemistry

Chemical Composition Identification Model Based on a Variety of Unsupervised Machine Learning Classification Methods This paper begins by visually representing the data and examining the classification patterns between highpotassium glass and lead-barium glass. Subsequently, we employ SPSSPRO for K-means clustering, utilizing the elbow method to ascertain the optimal value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{K}$</tex>. Post-classification, we further subclassify the characteristics of the cluster center coordinates for each index within the sample dataset. By establishing R-type clustering to subclassify the data again, comparing the results of K -means clustering and R -type clustering to judge the sensitivity and stability of the classification model, it is concluded that K-means clustering for subclassification is reasonable and clear, and has good stability and consistency. Then, the data were preprocessed using the isometric logarithmic ratio transformation, considering that the “closure effect” caused by the numerical values would lead to pseudo-correlation of the chemical data. Next, PCA downscaling was performed on the processed data to reduce the data to 2 dimensions. Finally, Form 3 glass categories were classified according to this classification threshold. Use decision tree classification to reclassify the attached Form 3, and then compare the results of the decision tree classification model with the results of the principal component threshold classification model, and conclude that the classification model is less sensitive to parameters and has better stability. Cluster analysis ; Pattern recognition (psychology) ; Decision tree ; Principal component analysis ; Stability (learning theory) ; Statistical classification ; Identification (biology) ; Data classification ; Discriminative model Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems
Authors: Jiayu Peng
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Bridging Physics-Informed and Data-Driven Materials Designs to Catalyze Deep Decarbonization Electrifying and decarbonizing key industrial sectors, including chemical and materials industries, as well as transportation and aviation, is a pressing mission of our time. Industrial players and governments worldwide have set ambitious targets to reduce carbon emissions, which need to be decreased to net-zero around mid-century in order to address global warming and climate change. For such deep decarbonization, it is crucial to develop unprecedented materials, such as electrocatalysts that can efficiently and steadily produce green chemicals and fuels at scale and generate renewable electricity on demand. In this talk, I will introduce how a joint computational–experimental approach can be established to accelerate catalyst materials design using first-principles atomistic simulations, synchrotron X-ray spectroscopies, and physics-inspired machine learning. I will highlight how mechanistically elucidating and quantitatively controlling the electronic structure of transition metal compounds can effectively alter their chemical bondings, modulate key reaction barriers toward dissolution and electrocatalysis, and thus influence their durability and reactivity, respectively, offering physics-driven guiding principles for optimizing their catalytic performance. Moreover, I will demonstrate how combining machine learning with materials physics can accelerate the discovery of multicomponent oxides—a new class of materials with too-complicated atomic orderings for an exhaustive investigation—through the development of machine-learning models that accurately predict ordering-dependent materials properties by capturing intrinsic symmetries. To conclude, I will discuss new opportunities for further resolving fundamental knowledge gaps between the structural, reaction, and compositional complexities of electrocatalyst surfaces to fully unlock transformative materials design for catalyzing sustainability and decarbonization. Bridging (networking) ; Physics ; Nanotechnology ; Engineering physics ; Materials science ; Computer science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electron and X-Ray Spectroscopy Techniques
Authors: Yonghyuk Lee | Taehun Lee
Source: arXiv (Cornell University) (preprint, 2024-12-11)
Field: Materials Science — Materials Chemistry

Machine-Learning-Accelerated Surface Exploration of Reconstructed BiVO$_{4}$(010) and Characterization of Their Aqueous Interfaces Understanding the semiconductor-electrolyte interface in photoelectrochemical (PEC) systems is crucial for optimizing stability and reactivity. Despite the challenges in establishing reliable surface structure models during PEC cycles, this study explores the complex surface reconstructions of BiVO$_{4}$(010) by employing a computational workflow integrated with a state-of-the-art active learning protocol for a machine-learning interatomic potential and global optimization techniques. Within this workflow, we identified 494 unique reconstructed surface structures that surpass conventional chemical intuition-driven, bulk-truncated models. After constructing the surface Pourbaix diagram under Bi- and V-rich electrolyte conditions using density functional theory and hybrid functional calculations, we proposed structural models for the experimentally observed Bi-rich BiVO$_{4}$ surfaces. By performing hybrid functional molecular dynamics simulations with explicit treatment of water molecules on selected reconstructed BiVO$_{4}$(010) surfaces, we observed spontaneous water dissociation, marking the first theoretical report of this phenomenon. Our findings demonstrate significant water dissociation on reconstructed Bi-rich surfaces, highlighting the critical role of bare and under-coordinated Bi sites (only observable in reconstructed surfaces) in driving hydration processes. Our work establishes a foundation for understanding the role of complex, reconstructed Bi surfaces in surface hydration and reactivity. Additionally, our theoretical framework for exploring surface structures and predicting reactivity in multicomponent oxides offers a precise approach to describing complex surface and interface processes in PEC systems. Characterization (materials science) ; Surface (topology) ; Aqueous solution ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Physical chemistry ; Geometry ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yangshuo Liu | Liang Qiao | Yao Meng | Chubo Wang | Yaotian Yan | Xiao Hang Zheng
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Ag-Based Electrocatalysts Design for Boosting Electrocatalytic Hydrogen Evolution Boosting (machine learning) ; Nanotechnology ; Materials science ; Electrocatalyst ; Computer science ; Artificial intelligence ; Chemistry ; Electrochemistry ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Huiju Lee | Yi Xia
Source: arXiv (Cornell University) (preprint, 2024-02-17)
Field: Materials Science — Materials Chemistry

Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids Phonons, as quantized vibrational modes in crystalline materials, play a crucial role in determining a wide range of physical properties, such as thermal and electrical conductivity, making their study a cornerstone in materials science. In this study, we present a simple yet effective strategy for deep learning harmonic phonons in crystalline solids by leveraging existing phonon databases and state-of-the-art machine learning techniques. The key of our method lies in transforming existing phonon datasets, primarily represented in interatomic force constants, into a force-displacement representation suitable for training machine learning universal interatomic potentials. By applying our approach to one of the largest phonon databases publicly available, we demonstrate that the resultant machine learning universal harmonic interatomic potential not only accurately predicts full harmonic phonon spectra but also calculates key thermodynamic properties with remarkable precision. Furthermore, the restriction to a harmonic potential energy surface in our model provides a way of assessing uncertainty in machine learning predictions of vibrational properties, essential for guiding further improvements and applications in materials science. Phonon ; Interatomic potential ; Harmonic ; Condensed matter physics ; Physics ; Materials science ; Statistical physics ; Computer science ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science
Authors: Nihang Fu | Lai Wei | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2024-01-10)
Field: Materials Science — Materials Chemistry

Physics guided dual Self-supervised learning for structure-based materials property prediction Deep learning (DL) models have now been widely used for high-performance material property prediction for properties such as formation energy and band gap. However, training such DL models usually requires a large amount of labeled data, which is usually not available for most materials properties such as exfoliation energy and elastic properties. Self-supervised learning (SSL) methods have been proposed to address this data scarcity issue by learning inherent representations from unlabeled data in various research fields. Herein, we present DSSL, a physics-guided Dual SSL framework, for graph neural networks (GNNs) based material property prediction. This hybrid framework combines node-masking based predictive SSL with atomic coordinate perturbation based contrastive SSL strategies, allowing it to learn structural embeddings that capture both local and global information of input crystals. Especially, we propose to use predicting the macroproperty (e.g. elasticity) related microproperty such as atomic stiffness as an additional pretext task to achieve physics-guided pretraining process. We pretrain our DSSL model on the Materials Project database with unlabeled data and finetune it with ten extra datasets with different material properties. The experimental results demonstrate that teaching neural networks some physics using the SSL strategy can bring up to 26.89\% performance improvement compared to the baseline GNN models. Our source code is now freely available at https://github.com/usccolumbia/DSSL Computer science ; Artificial intelligence ; Machine learning ; Property (philosophy) ; Interpretability ; Deep learning ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Sara Arif Alzaabi | Ali Elkamel | Ali Alhammadi | Georgios N. Karanikolos
Source:  (article, 2024-02-12)
Field: Materials Science — Materials Chemistry

Accelerating Sodium-Ion Electrode Material Development Through Artificial Intelligence Rechargeable batteries have proven to be the most efficient energy storage solution for the intermittent renewable energy sources. Currently, the lithium-ion battery (LIB) is regarded as the leading energy storage system because of its power and energy density. Nevertheless, LIBs are facing issues such as the limited availability of lithium precursor materials and the cost attributed to this issue. Thus, sodium ion batteries (SIBs) are regarded as a highly promising alternative to LIBs, but they are still under research. Battery research and especially battery material design is challenging because of the complex structure-property relationships, where different materials interact unpredictably and uncontrollably. Hence, artificial intelligence (AI) can be used to accelerate the process of battery electrode material discovery. There are many parameters that determine whether the battery electrode material is suitable or not, such as the voltage and the specific capacity which both contribute to the overall energy density. Additionally, the volume change throughout the cycles contribute to the safety, efficiency and cyclability of the battery. Thus, several AI techniques such as decision trees, support vector machine, random forest and deep neural network (DNN) models have been used to predict the average voltage, maximum specific capacity, and volume change of the battery. The results showed that the DNN model performed best with an average mean absolute error of around 0.164. Based on the results obtained, machine learning has proven to be a crucial tool towards revolutionizing the battery material industry. Electrode ; Ion ; Sodium ; Computer science ; Materials science ; Chemistry ; Metallurgy Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Semiconductor materials and devices
Authors: Frederik Ørsted Kjeldal | Janus J. Eriksen
Source: arXiv (Cornell University) (preprint, 2024-09-26)
Field: Materials Science — Materials Chemistry

Transferability of Atom-Based Neural Networks Machine-learning models in chemistry - when based on descriptors of atoms embedded within molecules - face essential challenges in transferring the quality of predictions of local electronic structures and their associated properties across chemical compound space. In the present work, we make use of adversarial validation to elucidate certain intrinsic complications related to machine inferences of unseen chemistry. On this basis, we employ invariant and equivariant neural networks - both trained either exclusively on total molecular energies or a combination of these and data from atomic partitioning schemes - to evaluate how such models scale performance-wise between datasets of fundamentally different functionality and composition. We find the inference of local electronic properties to improve significantly when training models on augmented data that appropriately expose local functional features. However, molecular datasets for training purposes must themselves be sufficiently comprehensive and rich in composition to warrant any generalizations to larger systems, and even then, transferability can still only genuinely manifest if the body of atomic energies available for training purposes exposes the uniqueness of different functional moieties within molecules. We demonstrate this point by comparing machine models trained on atomic partitioning schemes based on the spatial locality of either native atomic or molecular orbitals. Transferability ; Atom (system on chip) ; Artificial neural network ; Computer science ; Deep neural networks ; Artificial intelligence ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Yangshuo Liu | Keke Huang | Yao Meng | Chubo Wang | Liang Qiao | Yaotian Yan | Xiao Hang Zheng
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Ag-Based Electrocatalysts Design for Boosting Electrocatalytic Hydrogen Evolution Boosting (machine learning) ; Computer science ; Materials science ; Nanotechnology ; Artificial intelligence Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Zakariya El‐Machachi | Damyan Frantzov | A. Nijamudheen | Tigany Zarrouk | A. Miguel | Volker L. Deringer
Source: Angewandte Chemie (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

Accelerated First‐Principles Exploration of Structure and Reactivity in Graphene Oxide Abstract Graphene oxide (GO) materials are widely studied, and yet their atomic‐scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine‐learning methods, combining on‐the‐fly acceleration for first‐principles molecular dynamics with message‐passing neural‐network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state‐of‐the‐art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten‐nanometre scale) structural model. Our simulations are consistent with recent experimental findings, including X‐ray photoelectron spectroscopy (XPS), and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials. Graphene ; Oxide ; X-ray photoelectron spectroscopy ; Molecular dynamics ; On the fly ; Reactivity (psychology) ; Nanotechnology ; Materials science ; Computer science ; Nanometre ; Chemical physics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: R Meissner
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science [version 2; peer review: 3 approved, 1 approved with reservations] Suite ; Feature (linguistics) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Electron Microscopy Techniques and Applications
Authors: Lasse Kreimendahl | Mikhail Karnaukh | Merle I. S. Röhr
Source: ChemRxiv (preprint, 2024-12-22)
Field: Materials Science — Materials Chemistry

Diffusion Generative Models for Designing Efficient Singlet Fission Dimers Diffusion generative models, a class of machine learning techniques, have shown remarkable promise in materials science and chemistry by enabling the precise generation of complex molecular structures. In this paper, we propose a novel application of diffusion generative models for stabilizing reactive molecular structures identified through quantum mechanical screening. Specifically, we focus on the design challenge presented by Singlet Fission (SF), a phenomenon crucial for advancing solar cell efficiency beyond theoretical limits. While theoretical chemistry has been successful in predicting intermolecular arrangements with enhanced SF coupling, the practical implementation of these configurations faces challenges due to discrepancies between favorable and stabilized structures. To address this gap, we introduce a three-step strategy combining quantum mechanical screening for identifying optimal molecular arrangements and diffusion generative models for predicting stabilizing linkers. Through a case study on cibalackrot dimers, a promising SF material, we demonstrate the efficacy of our approach in enhancing SF efficiency by stabilizing the desired molecular arrangements. Generative grammar ; Intermolecular force ; Generative model ; Computer science ; Coupling (piping) ; Diffusion ; Quantum ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Molecule ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Fuel Cells and Related Materials
Authors: Veronika Jurásková | Gers Tusha | Hanwen Zhang | Lars V. Schäfer | Fernanda Duarte
Source: ChemRxiv (preprint, 2024-06-28)
Field: Materials Science — Materials Chemistry

Modelling ligand exchange in metal complexes with machine learning potentials Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and ab initio methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal-ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg2+ in water and Pd2+ in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies. Supramolecular chemistry ; Metal ions in aqueous solution ; Ligand (biochemistry) ; Ab initio ; Chemistry ; Energy landscape ; Computer science ; Metal ; Computational chemistry ; Chemical physics ; Nanotechnology ; Materials science ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Sauer, Joachim
Source: edoc Publication server (Humboldt University of Berlin) (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

The future of computational catalysis The future of computational heterogeneous catalysis is shaped by machine learning in two different but equally important areas: (i) development of atomistic potentials that closely approximate DFT and wavefunction based ab initio methods (MP2, CCSD(T)), but are computationally more efficient, and (ii) finding structure and reactivity descriptors for predicting catalytic materials and reactions. Machine Learning Potentials will enable improved sampling of the potential energy surface (PES) for reaction conditions, but they will not do better than the data on which they are trained. Therefore, this perspective focusses on ways of improving the quality of the PES beyond the generalized gradient approximation (GGA) climbing the Jacob’s ladder of functionals in DFT up to RPA and using wavefunction methods such as MP2 and CCSD(T). It is problem solving in close collaboration with experiment that has made computational methodology relevant and this remains an important aspect of data science methods in heterogeneous catalysis. Ab initio ; Wave function ; Perspective (graphical) ; Potential energy surface ; Potential energy ; Computational model ; Quality (philosophy) ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions