jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mark Leon | Yuriy Perezhohin | Fernando Peres | Aleš Popovič | Mauro Castelli
Source: Scientific Reports (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling Life sciences research and experimentation are resource-intensive, requiring extensive trials and considerable time. Often, experiments do not achieve their intended objectives, but progress is made through trial and error, eventually leading to breakthroughs. Machine learning is transforming this traditional approach, providing methods to expedite processes and accelerate discoveries. Deep Learning is becoming increasingly prominent in chemistry, with Convolutional Graph Networks (CGN) being a key focus, though other approaches also show significant potential. This research explores the application of Natural Language Processing (NLP) to evaluate the effectiveness of chemical language representations, specifically SMILES and SELFIES, using tokenization methods such as Byte Pair Encoding (BPE) and a novel approach developed in this study, Atom Pair Encoding (APE), in BERT-based models. The primary objective is to assess how these tokenization techniques influence the performance of chemical language models in biophysics and physiology classification tasks. The findings reveal that APE, particularly when used with SMILES representations, significantly outperforms BPE by preserving the integrity and contextual relationships among chemical elements, thereby enhancing classification accuracy. Performance was evaluated in downstream classification tasks using three distinct datasets for HIV, toxicology, and blood-brain barrier penetration, with ROC-AUC serving as the evaluation metric. This study highlights the critical role of tokenization in processing chemical language and suggests that refining these techniques could lead to significant advancements in drug discovery and material science. Lexical analysis ; Computer science ; Natural language processing ; Data science ; World Wide Web Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Deqi Tang | Rangsiman Ketkaew | Sandra Luber
Source: Chemistry - A European Journal (review, 2024-08-07)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials for Heterogeneous Catalysis Atomistic modeling can provide valuable insights into the design of novel heterogeneous catalysts as needed nowadays in the areas of, e. g., chemistry, materials science, and biology. Classical force fields and ab initio calculations have been widely adopted in molecular simulations. However, these methods usually suffer from the drawbacks of either low accuracy or high cost. Recently, the development of machine learning interatomic potentials (MLIPs) has become more and more popular as they can tackle the problems in question and can deliver rather accurate results at significantly lower computational cost. In this review, the atomistic modeling of catalytic systems with the aid of MLIPs is discussed, showcasing recently developed MLIP models and selected applications for the modeling of heterogeneous catalytic systems. We also highlight the best practices and challenges for MLIPs and give an outlook for future works on MLIPs in the field of heterogeneous catalysis. Interatomic potential ; Field (mathematics) ; Computer science ; Ab initio ; Biochemical engineering ; Force field (fiction) ; Catalysis ; Nanotechnology ; Molecular dynamics ; Management science ; Computational chemistry ; Artificial intelligence ; Chemistry ; Materials science ; Engineering ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: Md. Habibur Rahman | Prince Gollapalli | Panayotis Manganaris | Satyesh Kumar Yadav | Ghanshyam Pilania | Brian DeCost | Kamal Choudhary | Arun Mannodi‐Kanakkithodi
Source: APL Machine Learning (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Accelerating defect predictions in semiconductors using graph neural networks First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors. Semiconductor ; Artificial neural network ; Graph ; Computer science ; Materials science ; Artificial intelligence ; Optoelectronics ; Theoretical computer science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Chalcogenide Semiconductor Thin Films
Authors: Mingjian Wen | Matthew K. Horton | Jason M. Munro | Patrick Huck | Kristin A. Persson
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

An equivariant graph neural network for the elasticity tensors of all seven crystal systems An equivariant graph neural network model enables the rapid and accurate prediction of complete fourth-rank elasticity tensors of inorganic materials, facilitating the discovery of materials with exceptional mechanical properties. Equivariant map ; Elasticity (physics) ; Graph ; Mathematics ; Pure mathematics ; Materials science ; Discrete mathematics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Material Properties and Failure Mechanisms
Authors: Jing Lin | Tao Ban | Li Tian | Ye Sun | Shenglan Zhou | Rushuo Li | Yanjing Su | Jitti Kasemchainan | Hongyi Gao | Lei Shi | Ge Wang
Source: Materials Genome Engineering Advances (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Machine‐learning‐assisted intelligent synthesis of UiO‐66(Ce): Balancing the trade‐off between structural defects and thermal stability for efficient hydrogenation of Dicyclopentadiene Abstract Metal‐organic frameworks (MOFs), renowned for structural diversity and design flexibility, exhibit potential in catalysis. However, the pursuit of higher catalytic activity through defects often compromises stability, requiring a delicate balance. Traditional trial‐and‐error method for optimizing synthesis parameters within the complex chemical space is inefficient. Herein, taking the typical MOF UiO‐66(Ce) as an illustrative example, a closed loop workflow is built, which integrates machine learning (ML)‐assissted prediction, multi‐objective optimization (MOO) and experimental preparation to synergistically optimize the defect content and thermal stability of UiO‐66(Ce) for efficient hydrogenation of dicyclopentadiene (DCPD). An automatic data extraction program ensures data accuracy, establishing a high‐quality database. ML is employed to explore the intricate synthesis‐structure‐property correlations, enabling precise delineation of pure‐phase subspace and accurate predictions of properties. After two iterations, MOO model identifies optimal protocols for high defect content (&gt;40%) and thermal stability (&gt;300°C). The optimized UiO‐66(Ce) exhibits superior catalytic performance in hydrogenation of DCPD, validating the precision and reliability of our methodology. This ML‐assisted approach offers a valuable paradigm for solving the trade‐off riddle in materials field. Dicyclopentadiene ; Thermal stability ; Materials science ; Chemical engineering ; Composite material ; Engineering ; Polymerization ; Polymer Machine Learning in Materials Science ; Radioactive element chemistry and processing ; Nuclear Materials and Properties
Authors: Zechen Tang | He Li | Peize Lin | Xiaoxun Gong | Gan Jin | Lixin He | Hong Jiang | Xinguo Ren | Wenhui Duan | Yong Xu
Source: Nature Communications (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

A deep equivariant neural network approach for efficient hybrid density functional calculations Hybrid density functional calculations are essential for accurate description of electronic structure, yet their widespread use is restricted by the substantial computational cost. Here we develop DeepH-hybrid, a deep equivariant neural network method for learning the hybrid-functional Hamiltonian as a function of material structure, which circumvents the time-consuming self-consistent field iterations and enables the study of large-scale materials with hybrid-functional accuracy. Our extensive experiments demonstrate good reliability as well as effective transferability and efficiency of the method. As a notable application, DeepH-hybrid is applied to study large-supercell Moiré-twisted materials, offering the first case study on how the inclusion of exact exchange affects flat bands in magic-angle twisted bilayer graphene. The work generalizes deep-learning electronic structure methods to beyond conventional density functional theory, facilitating the development of deep-learning-based ab initio methods. Equivariant map ; Computer science ; Artificial neural network ; Deep neural networks ; Density functional theory ; Artificial intelligence ; Physics ; Mathematics ; Pure mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Shicheng Li | Santa Jansone‐Popova | De‐en Jiang
Source: Scientific Reports (article, 2024-05-17)
Field: Chemistry — Inorganic Chemistry

Insights into coordination and ligand trends of lanthanide complexes from the Cambridge Structural Database Understanding lanthanide coordination chemistry can help develop new ligands for more efficient separation of lanthanides for critical materials needs. The Cambridge Structural Database (CSD) contains tens of thousands of single crystal structures of lanthanide complexes that can serve as a training ground for both fundamental chemical insights and future machine learning and generative artificial intelligence models. This work aims to understand the currently available structures of lanthanide complexes in CSD by analyzing the coordination shell, donor types, and ligand types, from the perspective of rare-earth element (REE) separations. We obtain four sets of lanthanide complexes from CSD: Subset 1, all Ln-containing complexes (49472 structures); Subset 2, mononuclear Ln complexes (27858 structures); Subset 3, mononuclear Ln complexes without cyclopentadienyl ligands (Cp) (26156 structures); Subset 4, Ln complexes with at least one 1,10-phenanthroline (phen) or its derivative as a coordinating ligand (2226 structures). The subsequent analysis of lanthanide complexes in these subsets examines the trends in coordination numbers and first shell distances as well as identifies and characterizes the ligands and donor groups. In addition, examples of Ln-complexes with commercially available complexants and phen-based ligands are interrogated in detail. This systematic investigation lays the groundwork for future data-driven ligand designs for REE separations based on the structural insights into the lanthanide coordination chemistry. Lanthanide ; Ligand (biochemistry) ; Coordination complex ; Coordination number ; Coordination sphere ; Chemistry ; Crystal structure ; Database ; Crystallography ; Metal ; Computer science ; Ion ; Organic chemistry ; Biochemistry ; Receptor Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing ; Lanthanide and Transition Metal Complexes
Authors: Kara D. Fong | Barbara Sumić | Niamh O’Neill | Christoph Schran | Clare P. Grey | Angelos Michaelides
Source: Nano Letters (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

The Interplay of Solvation and Polarization Effects on Ion Pairing in Nanoconfined Electrolytes The nature of ion-ion interactions in electrolytes confined to nanoscale pores has important implications for energy storage and separation technologies. However, the physical effects dictating the structure of nanoconfined electrolytes remain debated. Here we employ machine-learning-based molecular dynamics simulations to investigate ion-ion interactions with density functional theory level accuracy in a prototypical confined electrolyte, aqueous NaCl within graphene slit pores. We find that the free energy of ion pairing in highly confined electrolytes deviates substantially from that in bulk solutions, observing a decrease in contact ion pairing but an increase in solvent-separated ion pairing. These changes arise from an interplay of ion solvation effects and graphene's electronic structure. Notably, the behavior observed from our first-principles-level simulations is not reproduced even qualitatively with the classical force fields conventionally used to model these systems. The insight provided in this work opens new avenues for predicting and controlling the structure of nanoconfined electrolytes. Electrolyte ; Chemical physics ; Solvation ; Ion ; Pairing ; Molecular dynamics ; Graphene ; Density functional theory ; Chemistry ; Potential of mean force ; Nanotechnology ; Materials science ; Computational chemistry ; Physical chemistry ; Physics ; Electrode ; Condensed matter physics Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Yang Zhong | Shixu Liu | Binhua Zhang | Zhiguo Tao | Yuting Sun | Weibin Chu | Xingao Gong | Ji-Hui Yang | Hongjun Xiang
Source: Nature Computational Science (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Accelerating the calculation of electron–phonon coupling strength with machine learning Superconductivity ; Hamiltonian (control theory) ; Phonon ; Computer science ; Coupling (piping) ; Electron ; Materials science ; Coupling strength ; Statistical physics ; Physics ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Topological Materials and Phenomena
Authors: Jiaxing Qu | Yuxuan Richard Xie | Kamil Ciesielski | Claire E. Porter | Eric S. Toberer | Elif Ertekin
Source: npj Computational Materials (article, 2024-03-21)
Field: Materials Science — Materials Chemistry

Leveraging language representation for materials exploration and discovery Abstract Data-driven approaches to materials exploration and discovery are building momentum due to emerging advances in machine learning. However, parsimonious representations of crystals for navigating the vast materials search space remain limited. To address this limitation, we introduce a materials discovery framework that utilizes natural language embeddings from language models as representations of compositional and structural features. The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share information across related properties. Applying this framework to thermoelectrics, we demonstrate diversified recommendations of prototype crystal structures and identify under-studied material spaces. Validation through first-principles calculations and experiments confirms the potential of the recommended materials as high-performance thermoelectrics. Language-based frameworks offer versatile and adaptable embedding structures for effective materials exploration and discovery, applicable across diverse material systems. Computer science ; Representation (politics) ; Natural language processing ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Christian M. Clausen | Jan Rossmeisl | Zachary W. Ulissi
Source: The Journal of Physical Chemistry C (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

Adapting OC20-Trained EquiformerV2 Models for High-Entropy Materials Computational high-throughput studies, especially in research on high-entropy materials and catalysts, are hampered by high-dimensional composition spaces and myriad structural microstates. They present bottlenecks to the conventional use of density functional theory calculations, and consequently, the use of machine-learned potentials is becoming increasingly prevalent in atomic structure simulations. In this communication, we show the results of adjusting and fine-tuning the pretrained EquiformerV2 model from the Open Catalyst Project to infer adsorption energies of *OH and *O on the out-of-domain high-entropy alloy Ag–Ir–Pd–Pt–Ru. By applying an energy filter based on the local environment of the binding site, the zero-shot inference is markedly improved, and through few-shot fine-tuning the model yields state-of-the-art accuracy. It is also found that EquiformerV2, assuming the role of general machine learning potential, is able to inform a smaller, more focused direct inference model. This knowledge distillation setup boosts performance on complex binding sites. Collectively, this shows that foundational knowledge learned from ordered intermetallic structures can be extrapolated to the highly disordered structures of solid-solutions. With the vastly accelerated computational throughput of these models, hitherto infeasible research in the high-entropy material space is now readily accessible. Intermetallic ; Inference ; Computer science ; Entropy (arrow of time) ; Density functional theory ; Distillation ; Artificial intelligence ; Machine learning ; Statistical physics ; Algorithm ; Materials science ; Alloy ; Physics ; Chemistry ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High Entropy Alloys Studies
Authors: Bohayra Mortazavi
Source: Materials (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Goldene: An Anisotropic Metallic Monolayer with Remarkable Stability and Rigidity and Low Lattice Thermal Conductivity In a recent breakthrough in the field of two-dimensional (2D) nanomaterials, the first synthesis of a single-atom-thick gold lattice of goldene has been reported through an innovative wet chemical removal of Ti<sub>3</sub>C<sub>2</sub> from the layered Ti<sub>3</sub>AuC<sub>2</sub>. Inspired by this advancement, in this communication and for the first time, a comprehensive first-principles investigation using a combination of density functional theory (DFT) and machine learning interatomic potential (MLIP) calculations has been conducted to delve into the stability, electronic, mechanical and thermal properties of the single-layer and free-standing goldene. The presented results confirm thermal stability at 700 K as well as remarkable dynamical stability of the stress-free and strained goldene monolayer. At the ground state, the elastic modulus and tensile strength of the goldene monolayer are predicted to be over 226 and 12 GPa, respectively. Through validated MLIP-based molecular dynamics calculations, it is found that at room temperature, the goldene nanosheet can exhibit anisotropic tensile strength over 9 GPa and a low lattice thermal conductivity around 10 ± 2 W/(m.K), respectively. We finally show that the native metallic nature of the goldene monolayer stays intact under large tensile strains. The combined insights from DFT and MLIP-based results provide a comprehensive understanding of the stability, mechanical, thermal and electronic properties of goldene nanosheets. Materials science ; Monolayer ; Nanosheet ; Thermal stability ; Ultimate tensile strength ; Density functional theory ; Nanomaterials ; Anisotropy ; Bulk modulus ; Thermal conductivity ; Molecular dynamics ; Interatomic potential ; Nanotechnology ; Composite material ; Computational chemistry ; Chemical engineering ; Chemistry MXene and MAX Phase Materials ; Graphene research and applications ; 2D Materials and Applications
Authors: R. C. Xin | Chaohai Wang | Yingchao Zhang | Rongfu Peng | Rui Li | Junning Wang | Yanli Mao | Xinfeng Zhu | Wenkai Zhu | Minjun Kim | Ho Ngoc Nam | Yusuke Yamauchi
Source: ACS Nano (article, 2024-07-01)
Field: Chemistry — Inorganic Chemistry

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs. Greenhouse gas ; Space (punctuation) ; Materials science ; Metal-organic framework ; Process engineering ; Environmental science ; Waste management ; Nanotechnology ; Computer science ; Engineering ; Chemistry ; Adsorption ; Geology ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications