jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianchuan Liu | Xingchen Zhang | Tao Chen | Yuzhi Zhang | Duo Zhang | Linfeng Zhang | Mohan Chen
Source: Journal of Chemical Theory and Computation (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal interatomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning interatomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independent deep potential models for each semiconductor are prepared for detailed comparison. The training data are obtained by performing density functional theory calculations with numerical atomic orbitals basis sets to reduce the computational costs. We systematically compare various properties of the solid and liquid phases of semiconductors between different machine-learning models. We conclude that the DPA-Semi model achieves GGA exchange-correlation functional quality accuracy and can be regarded as a pretrained model toward a universal model to study group IIB to VIA semiconductors. Semiconductor ; Atomic orbital ; Density functional theory ; Computer science ; Hybrid functional ; Machine learning ; Ab initio ; Artificial intelligence ; Cadmium telluride photovoltaics ; Deep learning ; Materials science ; Artificial neural network ; Computational chemistry ; Chemistry ; Nanotechnology ; Optoelectronics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: Rinon Iwasaki | Naoto Tanibata | Hayami Takeda | Masanobu Nakayama
Source: Communications Materials (article, 2024-08-10)
Field: Materials Science — Materials Chemistry

Universal-neural-network-potential molecular dynamics for lithium metal and garnet-type solid electrolyte interface Abstract All-solid-state Li-metal batteries can conceivably improve the safety and extend the driving ranges of electric vehicles. In this regard, the garnet-type solid electrolyte Li 7 La 3 Zr 2 O 12 (LLZ) has garnered considerable attention because of its high Li-ion conductivity and nonreactivity towards molten Li metal. Here, we perform molecular dynamics (MD) simulations using a universal neural network potential (UNNP) to analyse the Li-ion exchange at the LLZ/Li interface at the atomic scale. The UNNP-MD calculations show that Li ions traverse the LLZ/Li interface and that excess Li ions relative to the stoichiometric composition accumulate in an approximately 1 nm-thick zone near the LLZ phase interface, signifying the formation of a space-charge layer. Electronic structural analysis of the UNNP-MD-derived configuration, performed using density functional theory calculations, reveals band bending near the LLZ phase interface and the simultaneous suppression of Li metal reduction. These findings can help expedite the development of rationally designed all-solid-state Li-metal batteries. Electrolyte ; Molecular dynamics ; Interface (matter) ; Lithium (medication) ; Type (biology) ; Lithium metal ; Materials science ; Artificial neural network ; Dynamics (music) ; Fast ion conductor ; Chemical physics ; Nanotechnology ; Computer science ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Artificial intelligence ; Adsorption ; Medicine ; Electrode ; Biology ; Acoustics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Solid-state spectroscopy and crystallography
Authors: Vinayak Bhat | Baskar Ganapathysubramanian | Chad Risko
Source: The Journal of Physical Chemistry Letters (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline Organic semiconductors (OSC) offer tremendous potential across a wide range of (opto)electronic applications. OSC development, however, is often limited by trial-and-error design, with computational modeling approaches deployed to evaluate and screen candidates through a suite of molecular and materials descriptors that generally require hours to days of computational time to accumulate. Such bottlenecks slow the pace and limit the exploration of the vast chemical space comprising OSC. When considering charge-carrier transport in OSC, a key parameter of interest is the intermolecular electronic coupling. Here, we introduce a machine learning (ML) model to predict intermolecular electronic couplings in organic crystalline materials from their three-dimensional (3D) molecular geometries. The ML predictions take only a few seconds of computing time compared to hours by density functional theory (DFT) methods. To demonstrate the utility of the ML predictions, we deploy the ML model in conjunction with mathematical formulations to rapidly screen the charge-carrier mobility anisotropy for more than 60,000 molecular crystal structures and compare the ML predictions to DFT benchmarks. Intermolecular force ; Organic semiconductor ; Charge (physics) ; Coupling (piping) ; Semiconductor ; Density functional theory ; Pipeline (software) ; Chemical physics ; Computer science ; Materials science ; Charge carrier ; Chemistry ; Computational chemistry ; Statistical physics ; Molecule ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Weijian Chen | Jiahe Zhou | Shuting Li | Chuanyang Lu | Huaxin Li | Yafei Li | Yuwen Cheng | Jianguo Yang | Yanming He
Source: Journal of Alloys and Compounds (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Accelerated discovery of novel inorganic solid-state electrolytes through machine learning-assisted hierarchical screening Solid-state ; Electrolyte ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Engineering ; Engineering physics ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Daniel Schwalbe‐Koda | Nitish Govindarajan | Joel B. Varley
Source: Digital Discovery (article, 2024-12-09)
Field: Materials Science — Materials Chemistry

Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models A combination of generalization in neural networks and fast data pipelines enables comprehensive sampling coverage and co-adsorption effects in heterogeneous catalyst models. Generalization ; Artificial neural network ; Sampling (signal processing) ; Computer science ; Artificial intelligence ; Machine learning ; Data mining ; Mathematics ; Telecommunications Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yun‐Pei Liu | Qiyuan Fan | Fu‐Qiang Gong | Jun Cheng
Source: The Journal of Physical Chemistry C (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

CatFlow: An Automated Workflow for Training Machine Learning Potentials to Compute Free Energies in Dynamic Catalysis Dynamic effects of catalysts play a crucial role in catalytic reactions, necessitating the incorporation of statistical sampling and understanding of the impact of dynamic structures in free energy calculations. However, the complexity of catalytic systems poses challenges in effectively exploring the vast configurational space effectively. In this work, we propose CatFlow, an automated workflow for training machine learning potentials (MLPs) to compute free energies of catalytic reactions. CatFlow combines constrained molecular dynamics (MD) simulation with concurrent training of MLPs and sequential calculation of free energies with well trained MLPs. By rapidly generating reliable MLPs, CatFlow facilitates rigorous free energy calculations, enabling the determination of the reaction profiles in an end-to-end manner. We showcased the capabilities of CatFlow by investigating the activation of O2 catalyzed by Pt clusters and demonstrated the effects of phase transition on the activities of the catalytic reaction. CatFlow offers an efficient and automated solution for studying the catalytic elementary reaction processes. It reduces the need for human intervention and provides researchers with a powerful tool to investigate free energies of dynamic catalysis. Workflow ; Computer science ; Training (meteorology) ; Artificial intelligence ; Physics ; Database Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: M. Husnain Zeb | Abdul Rehman | Mariyam Siddiqah | Qiaoliang Bao | Babar Shabbir | M. Z. Kabir
Source: Advanced Theory and Simulations (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

Machine Learning‐Enhanced Prediction of Inorganic Semiconductor Bandgaps for Advancing Optoelectronic Technologies Abstract A pivotal challenge in advancing inorganics optoelectronic technologies, is the precise characterization of materials' electronic attributes, with the bandgap being a critical property. Conventional approaches, heavily reliant on time‐intensive and financially demanding experimental and computational methods, such as density functional theory (DFT) calculations, face limitations due to inherent estimation errors. Machine learning methodologies are developed for the prediction of bandgaps of inorganic semiconductors but most of them are employed for datasets created by DFT calculations, hence limiting their performance. Addressing this, the study leverages machine learning methodologies, harnessing both compositional and structural features, to predict the band gaps of inorganic semiconductors with enhanced accuracy. This advancement is reinforced by the employment of an experimental bandgap dataset, which, when integrated with structural descriptors obtained from the Materials Project, significantly improves prediction capabilities. This is evidenced by the model's exceptional performance across two distinct benchmark datasets. Furthermore, the model's adeptness in predicting formation energies underscores its versatility and applicability to a broad spectrum of electronic properties. These findings suggest that the predictive accuracy of this model can be further augmented through the inclusion of additional experimental bandgap measurements and the refinement of structural descriptors. This approach offers a promising and efficient alternative to traditional methodologies, potentially accelerating the development of optoelectronic technologies. Semiconductor ; Optoelectronics ; Materials science ; Semiconductor materials ; Computer science ; Engineering physics ; Engineering Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Semiconductor materials and interfaces
Authors: Weiqiang Fu | Yujie Mo | Xiao Yi | Chang Liu | Feng Zhou | Yan Wang | Jielong Zhou | Yingsheng Zhang
Source: Journal of Chemical Theory and Computation (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFFs), alongside evaluating the pragmatic utility of these MLFFs. This study introduces SWANI, an optimized neural network potential stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared with that of the ANI model. Additionally, a comprehensive comparison is conducted between SWANI and a prominent graph neural network-based model. The findings indicate that SWANI outperforms the latter, particularly for molecules exceeding the dimensions of the training set. This outcome underscores SWANI's exceptional capacity for generalization and its proficiency in handling larger molecular systems. Artificial neural network ; Generalization ; Computer science ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Potential energy ; Energy (signal processing) ; Training set ; Graph ; Rationality ; Focus (optics) ; Theoretical computer science ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Taixi Feng | Bo Yang | Jia Zhao | Guimin Lu
Source: Chemical Engineering Science (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Elucidating the local structure and properties of molten Na2CO3-K2CO3 salts using Machine Learning-Driven molecular dynamics Molecular dynamics ; Molecular machine ; Dynamics (music) ; Chemistry ; Chemical engineering ; Materials science ; Process engineering ; Computer science ; Mechanical engineering ; Nanotechnology ; Computational chemistry ; Engineering ; Physics Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; X-ray Diffraction in Crystallography
Authors: Yuchen Lou | Alex M. Ganose
Source: Faraday Discussions (article, 2024-07-25)
Field: Materials Science — Materials Chemistry

Discovery of highly anisotropic dielectric crystals with equivariant graph neural networks Anisotropy in crystals plays a pivotal role in many technological applications. For example, anisotropic electronic and thermal transport are thought to be beneficial for thermoelectric applications, while anisotropic mechanical properties are of interest for emerging metamaterials, and anisotropic dielectric materials have been suggested as a novel platform for dark matter detection. Understanding and tailoring anisotropy in crystals is therefore essential for the design of next-generation functional materials. To date, however, most data-driven approaches have focused on the prediction of scalar crystal properties, such as the spherically averaged dielectric tensor or the bulk and shear elastic moduli. Here, we adopt the latest approaches in equivariant graph neural networks to develop a model that can predict the full dielectric tensor of crystals. Our model, trained on the Materials Project dataset of <i>c.a.</i> 6700 dielectric tensors, achieves state-of-the-art accuracy in scalar dielectric prediction in addition to capturing the directional response. We showcase the performance of the model by discovering crystals with almost isotropic connectivity but highly anisotropic dielectric tensors, thereby broadening our knowledge of the structure-property relationships in dielectric crystals. Anisotropy ; Dielectric ; Materials science ; Artificial neural network ; Condensed matter physics ; Scalar (mathematics) ; Tensor (intrinsic definition) ; Statistical physics ; Computer science ; Theoretical physics ; Physics ; Artificial intelligence ; Optoelectronics ; Mathematics ; Optics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Siqi Zhan | Wanhui Huang | Caibo Dong | Qionghai Chen | Hengheng Zhao | Pengwei Duan | Anwen Hu | Qian Li | Ying Li | Jun Liu | Liqun Zhang
Source: Materials Today Communications (article, 2024-08-01)
Field: Materials Science — Polymers and Plastics

Predicting glass transition temperature of polymers by combining molecular dynamics simulations and machine learning techniques Materials science ; Glass transition ; Molecular dynamics ; Polymer ; Dynamics (music) ; Chemical physics ; Statistical physics ; Nanotechnology ; Composite material ; Computational chemistry Polymer crystallization and properties ; Material Dynamics and Properties ; Polymer Nanocomposites and Properties
Authors: Shuyi Jia | Akaash R. Parthasarathy | Rui Feng | Guojing Cong | Chengqi Zhang | Victor Fung
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Derivative-based pre-training of graph neural networks for materials property predictions General pre-training strategy of graph neural networks for materials science. Property (philosophy) ; Artificial neural network ; Derivative (finance) ; Training (meteorology) ; Computer science ; Graph ; Artificial intelligence ; Theoretical computer science ; Business ; Geography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mona Amrihesari | Joseph Kern | Hilary Present | Sarah Briceño | Rampi Ramprasad | Blair Brettmann
Source: The Journal of Physical Chemistry B (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Machine Learning Models for Predicting Polymer Solubility in Solvents across Concentrations and Temperatures Artificial intelligence and machine learning have become essential tools in predicting material properties to aid in the accelerated design of new materials. Polymer solubility, critical for new formulations and solution processing, is one such property. However, current models are limited by inadequate experimental data sets that cannot capture the complexity and detail for many features contributing to polymer solubility. Here, we provide a data set for polymer solution behavior based on Crystal16 turbidity measurements that includes high quality percent transmission data for polymer solutions for a variety of polymers, solvents, concentrations and temperatures. We use this data set to train a model that predicts the experimental transmission data at many temperatures and multiple concentrations. From this, we are able to classify the polymer/solvent pairs into three solubility categories providing a level of granularity to predictions beyond prior binary classification models considering only solvent/nonsolvent classes. The inclusion of multiple concentrations, temperatures and partially soluble data expands solubility prediction capability beyond prior work into predictions more attractive for use by formulators and process designers working with industrial polymer solutions. Solubility ; Polymer ; Granularity ; Solvent ; Process (computing) ; Computer science ; Process engineering ; Materials science ; Work (physics) ; Biological system ; Machine learning ; Artificial intelligence ; Chemistry ; Organic chemistry ; Mechanical engineering ; Engineering ; Composite material Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Xiaofeng Cao | Wenjia Luo | Huimin Liu
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A prediction model for CO<sub>2</sub>/CO adsorption performance on binary alloys based on machine learning Despite the rapid development of computational methods, including density functional theory (DFT), predicting the performance of a catalytic material merely based on its atomic arrangements remains challenging. Although quantum mechanics-based methods can model 'real' materials with dopants, grain boundaries, and interfaces with acceptable accuracy, the high demand for computational resources no longer meets the needs of modern scientific research. On the other hand, Machine Learning (ML) method can accelerate the screening of alloy-based catalytic materials. In this study, an ML model was developed to predict the CO<sub>2</sub> and CO adsorption affinity on single-atom doped binary alloys based on the thermochemical properties of component metals. By using a greedy algorithm, the best combination of features was determined, and the ML model was trained and verified based on a data set containing 78 alloys on which the adsorption energy values of CO<sub>2</sub> and CO were calculated from DFT. Comparison between predicted and DFT calculated adsorption energy values suggests that the extreme gradient boosting (XGBoost) algorithm has excellent generalization performance, and the <i>R</i>-squared (<i>R</i><sup>2</sup>) for CO<sub>2</sub> and CO adsorption energy prediction are 0.96 and 0.91, respectively. The errors of predicted adsorption energy are 0.138 eV and 0.075 eV for CO<sub>2</sub> and CO, respectively. This model can be expected to advance our understanding of structure-property relationships at the fundamental level and be used in large-scale screening of alloy-based catalysts. Adsorption ; Binary number ; Range (aeronautics) ; Binary classification ; Materials science ; Thermodynamics ; Computer science ; Artificial intelligence ; Chemistry ; Mathematics ; Physical chemistry ; Support vector machine ; Physics ; Composite material ; Arithmetic Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Machine Learning in Materials Science
Authors: Saaketh Desai | Manish Jain | Sadhvikas Addamane | David P. Adams | Rémi Dingreville | Frank W. DelRio‬ | Brad Boyce
Source: Materials & Design (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning • High throughput deposition and materais characterization form a rich process-structure-property dataset for Pt-Au alloys. • Neural networks leverage this data to identify process windows capable of maximizing material performance. • Pt-Au alloys with 18-24 at.% Au, deposited at specific conditions, have a nanoindentation hardness up to 7.2 GPa. For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance. Materials science ; Nanocrystalline material ; Process (computing) ; Property (philosophy) ; Metallurgy ; Nanotechnology ; Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; nanoparticles nucleation surface interactions