jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Huang | Shih‐Han Wang | Luke E. K. Achenie | Kamal Choudhary | Hongliang Xin
Source: The Journal of Chemical Physics (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Origin of unique electronic structures of single-atom alloys unraveled by interpretable deep learning We uncover the origin of unique electronic structures of single-atom alloys (SAAs) by interpretable deep learning. The approach integrates tight-binding moment theory with graph neural networks to accurately describe the local electronic structure of transition and noble metal sites upon perturbation. We emphasize the complex interplay of interatomic orbital coupling and on-site orbital resonance, which shapes the d-band characteristics of an active site, shedding light on the origin of free-atom-like d-states that are often observed in SAAs involving d10 metal hosts. This theory-infused neural network approach significantly enhances our understanding of the electronic properties of single-site catalytic materials beyond traditional theories. Electronic structure ; Atom (system on chip) ; Artificial neural network ; Chemical physics ; Tight binding ; Atomic orbital ; Materials science ; Physics ; Molecular physics ; Computer science ; Condensed matter physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Yiru Huang | Lei Zhang | Hangyuan Deng | Junfei Mao
Source: Journal of Chemical Information and Modeling (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

NJmat: Data-Driven Machine Learning Interface to Accelerate Material Design Machine learning techniques have significantly transformed the way materials scientists conduct research. However, the widespread deployment of machine learning software in daily experimental and simulation research for materials and chemical design has been limited. This is partly due to the substantial time investment and learning curve associated with mastering the necessary codes and computational environments. In this paper, we introduce a user-friendly, data-driven machine learning interface featuring multiple "button-clicking" functionalities to streamline the design of materials and chemicals. This interface automates the processes of transforming materials and molecules, performing feature selection, constructing machine learning models, making virtual predictions, and visualizing results. Such automation accelerates materials prediction and analysis in the inverse design process, aligning with the time criteria outlined by the Materials Genome Initiative. With simple button clicks, researchers can build machine learning models and predict new materials once they have gathered experimental or simulation data. Beyond the ease of use, NJmat offers three additional features for data-driven materials design: (1) automatic feature generation for both inorganic materials (from chemical formulas) and organic molecules (from SMILES), (2) automatic generation of Shapley plots, and (3) automatic construction of "white-box" genetic models and decision trees to provide scientific insights. We present case studies on surface design for halide perovskite materials encompassing both inorganic and organic species. These case studies illustrate general machine learning models for virtual predictions as well as the automatic featurization and Shapley/genetic model construction capabilities. We anticipate that this software tool will expedite materials and molecular design within the scope of the Materials Genome Initiative, particularly benefiting experimentalists. Computer science ; Interface (matter) ; Machine learning ; Artificial intelligence ; Software ; Feature (linguistics) ; Process (computing) ; Human–computer interaction ; Software engineering ; Programming language ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Abrar U. Hassan | Syed Shoaib Ahmad Shah | Hala M. Abo‐Dief | Sumaira Naeem | Numan Khan | Eman Alzahrani | Zeinhom M. El‐Bahy
Source: Synthetic Metals (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Polymer design using machine learning: A quest for high glass transition temperature Glass transition ; Polymer ; Polymer science ; Materials science ; Polymer chemistry ; Chemical engineering ; Composite material ; Engineering physics ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Polymer crystallization and properties
Authors: Mohammed Wasay Mudassir | Sriram Goverapet Srinivasan | Mahesh Mynam | Beena Rai
Source: ACS Applied Materials & Interfaces (article, 2024-12-28)
Field: Materials Science — Materials Chemistry

High-Throughput Exploration of Ti–V–Nb–Mo Carbide MXenes Using Neural Network Potentials and Their Evaluation as Catalysts for Hydrogen Evolution Reaction Realization of a sustainable hydrogen economy in the future requires the development of efficient and cost-effective catalysts for its production at scale. MXenes (M<sub>n+1</sub>X<sub>n</sub>) are a class of 2D materials with 'n' layers of carbon or nitrogen (X) interleaved by 'n+1' layers of transition metal (M) and have emerged as promising materials for various applications including catalysts for hydrogen evolution reaction (HER). Their properties are intimately related to both their composition and their atomic structure. Recently, high entropy MXenes were synthesized, opening a vast compositional space of potentially stable and functionally superior materials. Detailed atomistic modeling enables us to systematically explore this extensive design space, which is otherwise infeasible in experiments. We have developed a Neural Network Potential (NNP) to model (Ti<sub><i>x</i></sub>V<sub><i>y</i></sub>Nb<sub><i>z</i></sub>Mo<sub>p</sub>)<sub>n+1</sub>C<sub>n</sub> MXenes (x+y+z+p = 1; n = 1,2,3) by training against Density Functional Theory (DFT) data in an active learning fashion. We then used the developed NNP to perform hybrid Monte Carlo-Molecular Dynamics (MC-MD) simulations to identify thermodynamically stable compositions and investigate the relative arrangement of transition metal atoms within and across layers. Thermodynamic stability increased with Mo content and its presence on the surface layer. We further investigated the catalytic performance of stable MXenes for the HER and observed that the center of the oxygen p-band (ε<sub>p</sub>) correlated well with the energy of adsorption of a hydrogen atom ΔG(*H). Subsurface metal atoms significantly influenced the ΔG(*H) values at the surface via both ligand and strain effects. Our work expands the space of potentially stable MXene compositions, providing targets for synthesis and their evaluation in various applications. MXenes ; Catalysis ; Materials science ; Hydrogen ; Density functional theory ; Carbide ; Transition metal ; Water splitting ; Molecular dynamics ; Chemical physics ; Nanotechnology ; Computational chemistry ; Chemistry MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Son Gyo Jung | Guwon Jung | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors In the quest for advanced superconducting materials, the accurate prediction of critical temperatures (<i>T</i><sub>c</sub>) poses a formidable challenge, largely due to the complex interdependencies between superconducting properties and the chemical and structural characteristics of a given material. To address this challenges, we have developed a machine-learning framework that aims to elucidate these complicated and hitherto poorly understood structure-property and property-property relationships. This study introduces a novel machine-learning-based workflow, termed the Gradient Boosted Feature Selection (GBFS), which has been tailored to predict <i>T</i><sub>c</sub> for superconductors by employing a distributed gradient-boosting framework. This approach integrates exploratory data analyses, statistical evaluations, and multicollinearity reduction techniques to select highly relevant features from a high-dimensional feature space, derived solely from the chemical composition of materials. Our methodology was rigorously tested on a data set comprising approximately 16,400 chemical compounds with around 12,000 unique chemical compositions. The GBFS workflow enabled the development of a classification model that distinguishes compositions likely to exhibit <i>T</i><sub>c</sub> values greater than 10 K. This model achieved a weighted average F1-score of 0.912, an AUC-ROC of 0.986, and an average precision score of 0.919. Additionally, the GBFS workflow underpinned a regression model that predicted <i>T</i><sub>c</sub> values with an <i>R</i><sup>2</sup> of 0.945, an MAE of 3.54 K, and an RMSE of 6.57 K on a test set obtained via random splitting. Further exploration was conducted through out-of-sample <i>T</i><sub>c</sub> predictions, particularly those exceeding the liquid nitrogen temperature, and out-of-distribution predictions for (Ca<sub>1-<i>x</i></sub>La<sub><i>x</i></sub>)FeAs<sub>2</sub> based on varying lanthanum content. The outcome of our study underscores the significance of systematic feature analysis and selection in enhancing predictive model performance, offering various advantages over models that rely primarily on algorithmic complexity. This research not only advances the field of superconductivity but also sets a precedent for the application of machine learning in materials science. Superconductivity ; Materials science ; Chemistry ; Physics ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Frédéric Célerse | Veronika Jurásková | Shubhajit Das | Matthew D. Wodrich | Clémence Corminbœuf
Source: Journal of Chemical Theory and Computation (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Capturing Dichotomic Solvent Behavior in Solute–Solvent Reactions with Neural Network Potentials Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time step integration. These NNPs serve to explore a puzzling solute-solvent reactivity route involving the ring opening of <i>N</i>-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in agreement with experiment. These barriers are associated with an ensemble of transition states involving the direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example underscores the critical role that dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions. Metadynamics ; Transition state ; Solvent ; Chemical physics ; Chemistry ; Solvent models ; Reactivity (psychology) ; Molecule ; Density functional theory ; Solvent effects ; Hydrogen bond ; Computational chemistry ; Solvation ; Molecular dynamics ; Statistical physics ; Physics ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Russlan Jaafreh | Jung‐Gu Kim | Kotiba Hamad
Source: Journal of Power Sources (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Utilizing machine learning and phonon density of states for innovative approaches to design and optimize high-performance solid-state Mg-ion electrolytes Phonon ; Electrolyte ; Ion ; Solid-state ; Materials science ; State (computer science) ; Engineering physics ; Computer science ; Nanotechnology ; Chemical physics ; Engineering ; Condensed matter physics ; Chemistry ; Physics ; Physical chemistry ; Electrode ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advancements in Battery Materials
Authors: Ali Davariashtiyani | Busheng Wang | Samad Hajinazar | Eva Zurek | Sara Kadkhodaei
Source: Machine Learning Science and Technology (article, 2024-11-15)
Field: Materials Science — Materials Chemistry

Impact of data bias on machine learning for crystal compound synthesizability predictions Abstract Machine learning models are susceptible to being misled by biases in training data that emphasize incidental correlations over the intended learning task. In this study, we demonstrate the impact of data bias on the performance of a machine learning model designed to predict the likelihood of synthesizability of crystal compounds. The model performs a binary classification on labeled crystal samples. Despite using the same architecture for the machine learning model, we showcase how the model’s learning and prediction behavior differs once trained on distinct data. We use two data sets for illustration: a mixed-source data set that integrates experimental and computational crystal samples and a single-source data set consisting of data exclusively from one computational database. We present simple procedures to detect data bias and to evaluate its effect on the model’s performance and generalization. This study reveals how inconsistent, unbalanced data can propagate bias, undermining real-world applicability even for advanced machine learning techniques. Artificial intelligence ; Computer science ; Materials science ; Machine learning ; Data mining ; Pattern recognition (psychology) Crystallization and Solubility Studies ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Han Peng | Jingtong Zhang | Shengbin Shi | Yunhong Zhao | Yajun Zhang | Jie Wang
Source: npj Computational Materials (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Machine learning assisted screening of two dimensional chalcogenide ferromagnetic materials with Dzyaloshinskii Moriya interaction Abstract Magnetic skyrmions are potential candidates for high-density storage and logic devices because of their inherent topological stability and nanoscale size. Two-dimensional (2D) Janus transition metal chalcogenides (TMDs) are widely used to induce skyrmions due to the breaking of inversion symmetry. However, the experimental synthesis of Janus TMDs is rare, which indicates that the Janus configuration might not be the most stable MXY structure. Here, through machine-learning-assisted high-throughput first-principles calculations, we demonstrate that not all MXY compounds can be stabilized in Janus layered structure and a large proportion prefer to form other configurations with lower energy than the Janus configuration. Interestingly, these new configurations exhibit a strong Dzyaloshinskii–Moriya interaction (DMI), which can generate and stabilize skyrmions even under a strong magnetic field. This work provides not only an efficient method for obtaining ferromagnetic materials with strong DMI but also a theoretical guidance for the synthesis of TMDs via experiments. Chalcogenide ; Ferromagnetism ; Condensed matter physics ; Materials science ; Physics ; Optoelectronics Phase-change materials and chalcogenides ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Longjian Li | Jinchuan Jie | Xiaoyu Guo | Gaojie Liu | Huijun Kang | Zongning Chen | Enyu Guo | Tongmin Wang
Source: Materials Research Letters (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization Designing new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. Specifically, feature engineering was applied to screen the major features, and a three-step alloy design strategy was employed to extract the required composition. The material design strategy for the multi-performance optimization of Cu-Ni-Si alloy through Bayesian optimization was proposed. This work provides novel insights into the comprehensive properties of Cu-Ni-Si alloys using machine learning with small data, with potential applicability to other materials systems. Bayesian optimization ; Materials science ; Copper ; Precipitation ; Process (computing) ; Metallurgy ; Bayesian probability ; Mechanical engineering ; Machine learning ; Process engineering ; Artificial intelligence ; Computer science ; Engineering Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Advanced Materials Characterization Techniques
Authors: B. Hemavathi | G. Vidya | Vaibhav | K.S. Anantharaju | Ranjith Krishna Pai
Source: e-Prime - Advances in Electrical Engineering Electronics and Energy (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

Machine learning in the era of smart automation for renewable energy materials Exploration of smart sustainable renewable energy material with the aid of artificial intelligence is gaining momentum as next-generation research. The advantage of high throughput screening of the material is not only increasing the efficiency of the discovery but also can reduce the conventional process. In this review, the machine learning method of investigation of energy material for the application in energy conversion, storage, and energy-efficient materials has been discussed. Various ML tools and closed-loop screening techniques are discussed keeping the emphasis on both material and device optimization. Further, challenges and future prospects of smart automation in the exploration of energy material are elaborated. Renewable energy ; Automation ; Computer science ; Efficient energy use ; Process (computing) ; Smart material ; Process engineering ; Manufacturing engineering ; Engineering ; Systems engineering ; Mechanical engineering ; Nanotechnology ; Electrical engineering ; Materials science Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Gas Sensing Nanomaterials and Sensors
Authors: D.S. Williamson | S. Lopez-Ponte | Isabel Iglesias | Nicola M. Tonge | Carlos Cobas | E. Kate Kemsley
Source: Journal of Magnetic Resonance (article, 2024-10-30)
Field: Chemistry — Spectroscopy

Chemical shift prediction in 13C NMR spectroscopy using ensembles of message passing neural networks (MPNNs) This study reports a deep learning approach that utilises message passing neural networks (MPNNs) for predicting chemical shifts in <sup>13</sup>C NMR spectra of small molecules. MPNNs were trained on two distinct datasets: one with approximately 4000 labelled structures and another with over 40,000. To reduce stochastic variation, an ensemble framework was implemented, which is simple to deploy on multiple nodes of a High-Performance Computing facility. The results emphasise the critical role of training set size and diversity. While prediction performance was comparable on test sets drawn from each dataset, the ensemble trained on the larger dataset retained its accuracy when these sets were crossed over, and when applied to a further collection of approximately 12,000 previously unseen structures introduced after all development work had been completed. In contrast, the ensemble trained on the smaller dataset showed a notable decline in generalisation ability. This difference is attributed to the greater diversity of atomic environments captured in the larger dataset. The larger dataset also enabled more robust modelling of various error properties, providing a quantitative foundation for spectral assignment and verification. This was achieved in two ways. First, a clear relationship was observed between prediction errors and the frequency of different node feature vectors in the training data, allowing error estimates to be associated with individual nodes based on their type. These estimates can be used as weights in a modified cityblock distance metric when assigning observed to predicted shifts. Second, the mean absolute prediction error calculated at the structure level is well-fitted by a Gaussian kernel cumulative distribution. This enabled a probabilistic assessment of whether the predicted shifts and assigned observations are consistent with originating from the same molecular structure. Nuclear magnetic resonance spectroscopy ; Artificial neural network ; Spectroscopy ; Chemical physics ; Materials science ; Nuclear magnetic resonance ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Artificial intelligence Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: I. A. Balyakin | Maxim I. Vlasov | С. В. Першина | Dmitry Tsymbarenko | А. А. Rempel
Source: Computational Materials Science (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Neural network molecular dynamics study of LiGe2(PO4)3: Investigation of structure Molecular dynamics ; Dynamics (music) ; Artificial neural network ; Chemical physics ; Statistical physics ; Biological system ; Chemistry ; Computer science ; Materials science ; Computational chemistry ; Physics ; Artificial intelligence ; Biology Solid-state spectroscopy and crystallography ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies