jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergei Izvekov | Betsy M. Rice
Source: Journal of Chemical Theory and Computation (article, 2023-05-31)
Field: Materials Science — Materials Chemistry

Hierarchical Machine Learning of Low-Resolution Coarse-Grained Free Energy Potentials A force-matching-based method for supervised machine learning (ML) of coarse-grained (CG) free energy (FE) potentials─known as multiscale coarse-graining via force-matching (MSCG/FM)─is an efficient method to develop microscopically informed CG models that are thermodynamically and statistically equivalent to the reference microscopic models. For low-resolution models, when the coarse-graining is at supramolecular scales, objective-oriented clustering of nonbonded particles is required and the reduced description becomes a function of the clustering algorithm. In the present work, we explore the dependence of the ML of the CG Helmholtz FE potential on the clustering algorithm. We consider coarse-graining based on partitional (k-means, leading to Voronoi diagram) and hierarchical agglomerative (bottom-up) clustering algorithms common in unsupervised ML and develop theory connecting the MSCG/FM learned CG Helmholtz potential and the clustering statistics. By combining the agglomerative clustering and the MSCG/FM learning in a recursive manner, we propose an efficient ML methodology to develop the fine-to-low resolution hierarchies of the CG models. The methodology does not suffer from degrading accuracy or increased computational cost to construct larger hierarchies and as such does not impose an upper size limitation of the CG particles resulting from the extended hierarchies. The utility of the methodology is demonstrated by obtaining the bottom-up agglomerative hierarchy for liquid nitromethane from all-atom molecular dynamics (MD) simulations. For agglomerative hierarchies, we prove the existence of renormalization group transformations that indicate self-similarity and allow for learning the low-resolution MSCG/FM potentials at low computational cost by rescaling and renormalizing the certain finer-resolution members of the hierarchy. The hierarchies of the CG models can be used to carry out simulations under constant-pressure conditions. Cluster analysis ; Hierarchical clustering ; Granularity ; Computer science ; Helmholtz free energy ; Unsupervised learning ; Voronoi diagram ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics
Authors: Duo Zhang | Xinzijian Liu | Xiangyu Zhang | Chengqian Zhang | Chun Cai | Hangrui Bi | Yiming Du | Xuejian Qin | Peng, Anyang | Jiameng Huang | Bowen Li | Yifan Shan | Jinzhe Zeng | Yuzhi Zhang | Siyuan Liu | Yifan Li | Junhan Chang | Xinyan Wang | Shuo Zhou | Jianchuan Liu | Xiao‐San Luo | Zhenyu Wang | Wanrun Jiang | Jing Wu | Yudi Yang | Jiyuan Yang | Manyi Yang | Fu‐Qiang Gong | Linshuang Zhang | Mengchao Shi | Fu‐Zhi Dai | Darrin M. York | Liu Shi | Tong Zhu | Zhicheng Zhong | Jian Lv | Jun Cheng | Weile Jia | Mohan Chen | Guolin Ke | E Weinan | Linfeng Zhang | Han Wang
Source: arXiv (Cornell University) (preprint, 2023-12-24)
Field: Materials Science — Materials Chemistry

DPA-2: a large atomic model as a multi-task learner The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Computer science ; Workflow ; Pipeline (software) ; Field (mathematics) ; Atomic model ; Process (computing) ; Atomic units ; Multiscale modeling ; Scale (ratio) ; Systems engineering ; Computational science ; Nanotechnology ; Materials science ; Chemistry ; Engineering ; Database ; Physics ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Protein Structure and Dynamics
Authors: J. Christian Schön
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (review, 2023-05-21)
Field: Materials Science — Materials Chemistry

Structure prediction in low dimensions: concepts, issues and examples Structure prediction of stable and metastable polymorphs of chemical systems in low dimensions has become an important field, since materials that are patterned on the nano-scale are of increasing importance in modern technological applications. While many techniques for the prediction of crystalline structures in three dimensions or of small clusters of atoms have been developed over the past three decades, dealing with low-dimensional systems-ideal one-dimensional and two-dimensional systems, quasi-one-dimensional and quasi-two-dimensional systems, as well as low-dimensional composite systems-poses its own challenges that need to be addressed when developing a systematic methodology for the determination of low-dimensional polymorphs that are suitable for practical applications. Quite generally, the search algorithms that had been developed for three-dimensional systems need to be adjusted when being applied to low-dimensional systems with their own specific constraints; in particular, the embedding of the (quasi-)one-dimensional/two-dimensional system in three dimensions and the influence of stabilizing substrates need to be taken into account, both on a technical and a conceptual level. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'. Computer science ; Ideal (ethics) ; Complex system ; Field (mathematics) ; Embedding ; Scale (ratio) ; Metastability ; Nanotechnology ; Management science ; Theoretical computer science ; Materials science ; Artificial intelligence ; Mathematics ; Physics ; Engineering ; Epistemology Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; X-ray Diffraction in Crystallography
Authors: Bingqing Ge | Fenfei Wei | Qiang Wan | Hongwei Zhang | Pei Yuan | Sen Lin
Source: Precision Chemistry (article, 2023-04-13)
Field: Chemistry — Inorganic Chemistry

Peripheral Coordination-Dependent Descriptor for Selective Interactions between Near-Frontier Molecular Orbitals and Single-Atom Catalysts Selective adsorption of α,β-unsaturated aldehydes (α,β-UALs) is a prerequisite for the hydrogenation of α,β-UALs to high-value unsaturated alcohols, but a quantitative description of the interactions between the CC/CO bond of α,β-UALs and the catalysts is still lacking. Herein, based on comprehensive density functional theory calculations, we developed a descriptor that combines the near-frontier molecular orbitals of the CC/CO bonds of α,β-UALs with the fundamental physical properties of single-atom catalysts (SACs) and considers the inner/outer coordination environment. All of the parameters used in this descriptor are easily accessible and interpretable, enabling an efficient assessment of the selectivity of SACs for the CC/CO bonds of α,β-UALs. Catalysis ; Atom (system on chip) ; Atomic orbital ; Selectivity ; Chemistry ; Molecular orbital ; Adsorption ; Computational chemistry ; Molecule ; Stereochemistry ; Physical chemistry ; Physics ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Roman Ezhov | Gabriel Bury | Olga V. Maximova | Elliot Daniel Grant | Mio Kondo | Shigeyuki Masaoka | Yulia Pushkar
Source: Journal of Catalysis (article, 2023-11-29)
Field: Chemistry — Electrochemistry

Pentanuclear iron complex for water oxidation: Spectroscopic analysis of reactive intermediates in solution and catalyst immobilization into the MOF-based photoanode Chemistry ; Catalysis ; Electrolysis of water ; Electrolysis ; Electrocatalyst ; Bulk electrolysis ; Electrochemistry ; Electron paramagnetic resonance ; Inorganic chemistry ; Photochemistry ; Reaction intermediate ; Water splitting ; Physical chemistry ; Electrode ; Cyclic voltammetry ; Photocatalysis ; Organic chemistry Electrochemical Analysis and Applications ; Metal complexes synthesis and properties ; Electrocatalysts for Energy Conversion
Authors: Zheng Shu | Yongqing Cai
Source: Journal of Physics Condensed Matter (article, 2023-03-07)
Field: Materials Science — Materials Chemistry

Thickness-dependent catalytic activity of hydrogen evolution based on single atomic catalyst of Pt above MXene Hydrogen as the cleanest energy carrier is a promising alternative renewable resource to fossil fuels. There is an ever-increasing interest in exploring efficient and cost-effective approaches of hydrogen production. Recent experiments have shown that single platinum atom immobilized on the metal vacancies of MXenes allows a high-efficient hydrogen evolution reaction (HER). Here using<i>ab initio</i>calculations, we design a series of substitutional Pt-doped Ti<i><sub>n</sub></i><sub>+ 1</sub>C<i><sub>n</sub></i>T<i><sub>x</sub></i>(Ti<i><sub>n</sub></i><sub>+ 1</sub>C<i><sub>n</sub></i>T<i><sub>x</sub></i>-Pt<sub>SA</sub>) with different thicknesses and terminations (<i>n</i>= 1, 2 and 3, T<i><sub>x</sub></i>= O, F and OH), and investigate the quantum-confinement effect on the HER catalytic performance. Surprisingly, we reveal a strong thickness effect of the MXene layer on the HER performance. Among the various surface-terminated derivatives, Ti<sub>2</sub>CF<sub>2</sub>-Pt<sub>SA</sub>and Ti<sub>2</sub>CH<sub>2</sub>O<sub>2</sub>-Pt<sub>SA</sub>are found to be the best HER catalysts with the change of Gibbs free energy Δ<i>G</i><sub>H*</sub>∼ 0 eV, complying with the thermoneutral condition. The<i>ab initio</i>molecular dynamics simulations reveal that Ti<sub>2</sub>CF<sub>2</sub>-Pt<sub>SA</sub>and Ti<sub>2</sub>CH<sub>2</sub>O<sub>2</sub>-Pt<sub>SA</sub>possess a good thermodynamic stability. The present work shows that the HER catalytic activity of the MXene is not solely governed by the local environment of the surface such as Pt single atom. We point out the critical role of thickness control and surface decoration of substrate in achieving a high-performance HER catalytical activity. Catalysis ; MXenes ; Platinum ; Tin ; Hydrogen ; Materials science ; Gibbs free energy ; Chemical physics ; Chemical engineering ; Hydrogen atom ; Hydrogen production ; Substrate (aquarium) ; Nanotechnology ; Physical chemistry ; Chemistry ; Thermodynamics ; Alkyl ; Organic chemistry ; Metallurgy MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Enrique Maciá
Source: Symmetry (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Alloy Quasicrystals: Perspectives and Some Open Questions at Forty Years Four decades have elapsed since the first quasiperiodic crystal was discovered in the Al–Mn alloy system, and much progress has been made during this time on the science of quasicrystals (QCs). Notwithstanding this, a significant number of open questions still remain regarding both fundamental and technological aspects. For instance: What are QCs good for? How can we improve the current provisional QC definition? What is the role of the underlying quasiperiodic order and the characteristic inflation symmetry of these compounds in the emergence of their unusual physicochemical properties? What is the nature of chemical bonding in QCs formed in different sorts of materials such as alloys, oxides, or organic polymers? Herein these and other closely related issues are discussed from an interdisciplinary perspective as well as prospective future work in the field in the years to come. Quasicrystal ; Quasiperiodic function ; Field (mathematics) ; Perspective (graphical) ; Alloy ; Order (exchange) ; Materials science ; Theoretical physics ; Nanotechnology ; Computer science ; Physics ; Condensed matter physics ; Metallurgy ; Mathematics ; Business ; Pure mathematics ; Artificial intelligence Quasicrystal Structures and Properties ; Mineralogy and Gemology Studies
Authors: Taisiya S. Sukhikh | Radmir M. Khisamov | Sergey N. Konchenko
Source: Symmetry (article, 2023-01-05)
Field: Chemistry — Inorganic Chemistry

Synthesis, Crystal Packing Aspects and Pseudosymmetry in Coordination Compounds with a Phosphorylamide Ligand This work reports the synthesis and crystal structure of new closely related coordination compounds, [ML2]·nTHF, where M is Zn or Mn; L is a phosphorylmethylamide derivative of benzothiadiazole; n = 1 (M = Zn) and 1, 2 (M = Mn); and THF is tetrahydrofuran. The zinc compound, 1·THF, crystallizes in a high-symmetry space group, I41/a, that is relatively rare for compounds with organic ligands. The corresponding manganese congener, 2·THF, with a similar crystal packing, features a pseudosymmetrical structure P21/c of the doubled volume of the unit cell as compared to 1·THF. The main difference between the structures lies in a different arrangement of solvate THF molecules, which likely modulates the crystal packing of the complexes. Another manganese solvatomorph, 2·2THF, reveals a fundamentally different crystal packing while exhibiting a similar geometry of the complex. We consider the problem of localization of solvate THF molecules and the types of their disorder by the example of compounds 1–2. Crystal structure ; Tetrahydrofuran ; Crystallography ; Manganese ; Ligand (biochemistry) ; Chemistry ; Molecule ; Zinc ; Crystal (programming language) ; Derivative (finance) ; Space group ; Stereochemistry ; X-ray crystallography ; Organic chemistry ; Physics ; Solvent ; Receptor ; Diffraction Crystal structures of chemical compounds ; Magnetism in coordination complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Francesco Marin | Alessandro Zappi | Dora Melucci | Lucia Maini
Source: Molecular Systems Design & Engineering (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Self-organizing maps as a data-driven approach to elucidate the packing motifs of perylene diimide derivatives The efficient classification or prediction of crystal structures into a small number of families of related structures can be extremely important in the design of materials with specific packing and properties. Perylene ; Diimide ; Materials science ; Self-assembly ; Computer science ; Biological system ; Nanotechnology ; Chemistry ; Organic chemistry ; Molecule ; Biology Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Youzhi Luo | Chengkai Liu | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2023-07-06)
Field: Materials Science — Materials Chemistry

Towards Symmetry-Aware Generation of Periodic Materials We consider the problem of generating periodic materials with deep models. While symmetry-aware molecule generation has been studied extensively, periodic materials possess different symmetries, which have not been completely captured by existing methods. In this work, we propose SyMat, a novel material generation approach that can capture physical symmetries of periodic material structures. SyMat generates atom types and lattices of materials through generating atom type sets, lattice lengths and lattice angles with a variational auto-encoder model. In addition, SyMat employs a score-based diffusion model to generate atom coordinates of materials, in which a novel symmetry-aware probabilistic model is used in the coordinate diffusion process. We show that SyMat is theoretically invariant to all symmetry transformations on materials and demonstrate that SyMat achieves promising performance on random generation and property optimization tasks. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS). Homogeneous space ; Invariant (physics) ; Lattice (music) ; Symmetry (geometry) ; Computer science ; Probabilistic logic ; Encoder ; Atom (system on chip) ; Algorithm ; Mathematics ; Physics ; Geometry ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Polydiacetylene-based materials and applications
Authors: Junjie Hao | Haochen Liu | Xijian Duan | Ziming Zhou | Bingxin Zhao | Wenda Zhang | Bing Xu | Xiao Wei Sun | Marie‐Hélène Delville
Source: Science China Materials (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Shape control of CdSe/CdS nanocrystals during shell formation and growth: Dominating effects of surface ligands over core crystal structure Wurtzite crystal structure ; Materials science ; Nanocrystal ; Shell (structure) ; Crystallography ; Crystal (programming language) ; Nanotechnology ; Cadmium ; Crystal growth ; Crystal structure ; Core (optical fiber) ; Nanostructure ; Ligand (biochemistry) ; Chemical physics ; Chemical engineering ; Zinc ; Chemistry ; Composite material Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Copper-based nanomaterials and applications
Authors: Song Gao | Zhanchen Guo | Zhen Liu
Source: Chemosensors (article, 2023-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Advances in Rational Design and Engineering of Signal-Amplifying Substrates for Surface-Enhanced Raman Scattering-Based Bioassays In recent decades, surface-enhanced Raman spectroscopy (SERS) has become a powerful detection scheme for many applications, particularly bioassays, due to its unique strengths, such as its ultrasensitive performance. Due to the development of various SERS substrates, more SERS-based bioassays with improved sensitivity and reproducibility have been designed and manufactured. SERS is able to provide the intrinsic vibration information of molecules through the unique Raman fingerprint to enable direct detection and quantitation. Meanwhile, with the assistance of Raman-active labels, biomolecules, like proteins and nucleic acids, can be detected by the immunosandwich assay. In this review, we focus on the rational design and engineering of signal-enhancing substrates for SERS-based bioassays. Those substrates are classified into two categories, i.e., nanoparticles in colloidal suspension and nanostructures on a solid support. Each category is discussed in detail with stress on their biomedical application potential. Afterward, we summarize the SERS-based assays of proteins, nucleic acids, and viruses, for which both label-free and labeled approaches play important roles. Finally, we present the remaining challenges in the field of SERS-based bioassays and sketch out promising directions for future development. Biomolecule ; Nanotechnology ; Surface-enhanced Raman spectroscopy ; Bioassay ; Raman scattering ; Rational design ; Raman spectroscopy ; Nanomedicine ; Nucleic acid ; Materials science ; Computer science ; Biochemical engineering ; Chemistry ; Nanoparticle ; Biology ; Engineering ; Biochemistry ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Ilya E. Nifant’ev | S.' A. Korchagina | M. S. Chinova | Alexander N. Tavtorkin
Source: Polymers (review, 2023-08-15)
Field: Chemistry — Organic Chemistry

Polyisobutylenes with Controlled Molecular Weight and Chain-End Structure: Synthesis and Actual Applications The polymerization of isobutylene allows us to obtain a wide spectrum of polyisobutylenes (PIBs) which differ in their molecular weight characteristics and the chemical structure of chain-end groups. The bulk of the PIBs manufactured worldwide are highly reactive polyisobutylenes (HRPIBs) with -C(Me)=CH<sub>2</sub> end-groups and low-molecular weights (<i>M</i><sub>n</sub> < 5 kDa). HRPIBs are feedstocks that are in high demand in the manufacturing of additives for fuels and oils, adhesives, detergents, and other fine chemicals. In addition, HRPIBs and CMe<sub>2</sub>Cl-terminated PIBs are intensively studied with the aim of finding biomedical applications and for the purpose of developing new materials. Both chain control (molecular weight and dispersity) and chemoselectivity (formation of <i>exo</i>-olefinic or -CMe<sub>2</sub>Cl groups) should be achieved during polymerization. This review highlights the fundamental issues in the mechanisms of isobutylene polymerization and PIB analysis, examines actual catalytic approaches to PIBs, and describes recent studies on the functionalization and applications of HRPIBs and halogen-terminated PIBs. Isobutylene ; Dispersity ; Polymerization ; End-group ; Polymer chemistry ; Materials science ; Molar mass distribution ; Chain transfer ; Polymer science ; Chemistry ; Organic chemistry ; Radical polymerization ; Polymer ; Copolymer Advanced Polymer Synthesis and Characterization ; Organometallic Complex Synthesis and Catalysis ; biodegradable polymer synthesis and properties
Authors: Rasmus Fromsejer | Bjørn Maribo‐Mogensen | Georgios M. Kontogeorgis | Xiaodong Liang
Source: Computational Materials Science (article, 2023-03-27)
Field: Materials Science — Materials Chemistry

Evaluating the harmonic approximation for the prediction of thermodynamic formation properties of solids Thermodynamic stability is regularly quantified using the enthalpy of formation (ΔfH) and the Gibbs free energy of formation (ΔfG). Although knowledge of these properties is crucial for many applications, literature data are often missing for many solids. We evaluate the harmonic approximation (HA) for the prediction of ΔfH and ΔfG at constant volume and pressure, based on phonon calculations. Using density functional theory to carry out phonon calculations, we show that the HA excellently describes the temperature-dependence of ΔfH and ΔfG for 14 compounds; mean absolute error (MAE) of 2.1 kJ⋅mol−1 and 1.1 kJ⋅mol−1, respectively, in the temperature interval 0–2500 K. Moreover, the performance of the HA was evaluated using computational data from the Materials Project database for 69 compounds yielding an MAE of 1.1 kJ⋅mol−1 and 1.1 kJ⋅mol−1, respectively, in the temperature interval 100–800 K. Very good performance is also observed for a number of additional compounds, including several hydrated salts, at 298.15 K. The model is subsequently applied to a number of phase-transition phenomena that demonstrate the strengths and weaknesses of the model. In addition, it is demonstrated that the HA model can provide quantitative performance that rivals that of the conventional quasi-harmonic approximation for the prediction of formation properties, at a significantly reduced computational effort (∼5–10 times faster). Thermodynamics ; Enthalpy ; Gibbs free energy ; Phonon ; Chemistry ; Harmonic mean ; Standard enthalpy of formation ; Interval (graph theory) ; Harmonic ; Stability (learning theory) ; Density functional theory ; Physics ; Computational chemistry ; Mathematics ; Machine learning ; Condensed matter physics ; Computer science ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Crystallography and molecular interactions