jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yaoguang Zhai | Alessandro Caruso | Sigbjørn Løland Bore | Zhishang Luo | Francesco Paesani
Source: ChemRxiv (preprint, 2023-01-18)
Field: Materials Science — Materials Chemistry

A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions? Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties. Computer science ; Process (computing) ; Artificial neural network ; Range (aeronautics) ; Scale (ratio) ; State (computer science) ; Artificial intelligence ; Liquid water ; Statistical physics ; Physics ; Algorithm ; Aerospace engineering ; Engineering Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Quantum many-body systems
Authors: Jing Wu | Jiyuan Yang | Yuan-Jinsheng Liu | Duo Zhang | Yudi Yang | Yuzhi Zhang | Linfeng Zhang | Shi Liu
Source: arXiv (Cornell University) (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Universal interatomic potential for perovskite oxides With their celebrated structural and chemical flexibility, perovskite oxides have served as a highly adaptable material platform for exploring emergent phenomena arising from the interplay between different degrees of freedom. Molecular dynamics (MD) simulations leveraging classical force fields, commonly depicted as parameterized analytical functions, have made significant contributions in elucidating the atomistic dynamics and structural properties of crystalline solids including perovskite oxides. However, the force fields currently available for solids are rather specific and offer limited transferability, making it time-consuming to use MD to study new materials systems since a new force field must be parameterized and tested first. The lack of a generalized force field applicable to a broad spectrum of solid materials hinders the facile deployment of MD in computer-aided materials discovery (CAMD). Here, by utilizing a deep-neural network with a self-attention scheme, we have developed a unified force field that enables MD simulations of perovskite oxides involving 14 metal elements and conceivably their solid solutions with arbitrary compositions. Notably, isobaric-isothermal ensemble MD simulations with this model potential accurately predict the experimental phase transition sequences for several markedly different ferroelectric oxides, including a 6-element ternary solid solution, Pb(In$_{1/2}$Nb$_{1/2}$)O$_3$--Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_3$--PbTiO$_3$. We believe the universal interatomic potential along with the training database, proposed regression tests, and the auto-testing workflow, all released publicly, will pave the way for a systematic improvement and extension of a unified force field for solids, potentially heralding a new era in CAMD. Force field (fiction) ; Perovskite (structure) ; Molecular dynamics ; Interatomic potential ; Parameterized complexity ; Ternary operation ; Materials science ; Field (mathematics) ; Workflow ; Solid solution ; Computer science ; Chemical physics ; Statistical physics ; Nanotechnology ; Physics ; Chemistry ; Computational chemistry ; Algorithm ; Crystallography ; Artificial intelligence ; Mathematics ; Database Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials
Authors: Tetsuya Yamada | Hiromasa Kaneko | Fumitaka Hayashi | Tatsuya Doi | Michihisa Koyama | Katsuya Teshima
Source: Crystal Growth & Design (article, 2023-10-24)
Field: Materials Science — Materials Chemistry

Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba<sub>5</sub>Nb<sub>4</sub>O<sub>15</sub> Crystals Composition selection and crystal-growth control are the most important issues in material development. Crystal growth using a molten salt as the solvent, which is otherwise known as the flux method, is expected to be one of the key technologies for developing crystalline materials, because this method can grow single crystals of inorganic materials with control of the crystal habit and size. In the flux method, the number of combinations of experimental conditions usually exceeds 10,000 owing to the wide variety of experimental variables, such as solvents and heating conditions. Due to such large combinations, the conventional trial-and-error approach based on hypothesis testing would take several years to obtain the optimal crystal, and the crystal control region is also limited. As a result, the development of crystalline materials is risky, and their use is limited. To overcome this issue, we propose a novel crystal growth approach called flux-method process informatics (FPI). The FPI approach generates a data-driven experimental proposal based on machine learning predictions. More specifically, regression analyses are carried out using the experimental process as the explanatory variable and the crystal shape/size as the objective variable. In this study, FPI was applied to the layered perovskite-type oxide Ba5Nb4O15. It was found that the experimental efficiency of the FPI approach was five times higher than that of humans, and it was able to quantitatively control the shape of low-aspect-ratio crystals, which is usually difficult for anisotropic structures. These achievements provide new insights into the rapid development of high-performance crystalline materials. Crystal (programming language) ; Crystal growth ; Perovskite (structure) ; Materials science ; Flux (metallurgy) ; Crystallography ; Computer science ; Chemistry Machine Learning in Materials Science
Authors: Jacob J. Wardzala | Daniel S. King | Lawal Adewale Ogunfowora | Brett M. Savoie | Laura Gagliardi
Source: ChemRxiv (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

Organic Reactivity Made Easy and Accurate withAutomated Multireference Calculations In organic reactivity studies, quantum chemical calculations play a pivotal role in serving as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g., CCSD(T)) have significantly advanced our understanding of chemical reactivity, they frequently fall short in accurately describing multiconfigurational transition states and intermediates. Achieving a more accurate description necessitates the use of multireference methods. However, these methods have not been used at scale due to their often-faulty predictions without expert input. Here, we overcome this deficiency through the use of automated multiconfigurational pair-density functional theory (MC-PDFT) calculations. We apply this method to 908 automatically generated main-group organic reactions. We find that 68% of these reactions present significant multiconfigurational character, in which the automated multiconfigurational approach often provides a more accurate and/or efficient description than DFT and CCSD(T). This work presents the first high-throughput application of automated multiconfigurational methods to chemical reactivity, and is enabled by automated active space selection algorithms and the computation of electronic correlation with the on-top functionals of MC-PDFT. The combination of these two new developments can be used in a black-box fashion, avoiding significant active space inconsistency error in both single- and multireference cases and providing accurate multiconfigurational descriptions when needed. Chemical space ; Coupled cluster ; Reactivity (psychology) ; Density functional theory ; Computer science ; Computational chemistry ; Space (punctuation) ; Computation ; Chemistry ; Statistical physics ; Quantum mechanics ; Physics ; Algorithm ; Drug discovery ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Sowmya Natarajan | Vijayakumar Ponnusamy
Source: Algorithms for intelligent systems (review, 2023-01-01)
Field: Chemistry — Analytical Chemistry

A Review on Quality Determination for Fruits and Vegetables Grading (engineering) ; Quality (philosophy) ; sort ; Sorting ; Identification (biology) ; Artificial intelligence ; Product (mathematics) ; Computer science ; Agricultural engineering ; Mathematics ; Pattern recognition (psychology) ; Computer vision ; Engineering ; Botany ; Information retrieval ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Chaoqin Chu | Qinkun Xiao | Chaozheng He | Chen Chen | Lu Li | Junyan Zhao | Jinzhou Zheng | Yinhuan Zhang
Source: Chinese Chemical Letters (article, 2023-10-17)
Field: Materials Science — Materials Chemistry

A novel method for atomization energy prediction based on natural-parameter network Computer science ; Stability (learning theory) ; Field (mathematics) ; Matrix (chemical analysis) ; Energy (signal processing) ; Data pre-processing ; Set (abstract data type) ; Test set ; Artificial neural network ; Algorithm ; Process (computing) ; Sensitivity (control systems) ; Preprocessor ; Data mining ; Artificial intelligence ; Machine learning ; Chemistry ; Mathematics ; Statistics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Dan-Yan Qin | Saiya Zhao | Zhixin Liu | Jing Zhang | Xinghua Zhang
Source: Chinese Journal of Polymer Science (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Microphase Separation of Semiflexible Ring Diblock Copolymers Ring (chemistry) ; Structure factor ; Monte Carlo method ; Statistical physics ; Polymer ; Copolymer ; Materials science ; Field (mathematics) ; Field theory (psychology) ; Phase (matter) ; Chemical physics ; Physics ; Mathematics ; Statistics ; Chemistry ; Quantum mechanics ; Condensed matter physics Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Sara Tkaczyk | Johannes Karwounopoulos | Andreas Schöller | H. Lee Woodcock | Thierry Langer | Stefan Boresch | Marcus Wieder
Source: ChemRxiv (preprint, 2023-05-02)
Field: Materials Science — Materials Chemistry

Using Neural Network Potentials to efficiently calculate indirect free energy estimates To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have limited range of applicability and are significantly slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. In this study, we systematically investigate the accuracy, robustness, and efficiency of free energy calculations between a MM reference and a neural network target potential (ANI-2x) using free energy perturbation (FEP) and non-equilibrium (NEQ) switching simulation in vacuum. We investigate the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution. We compare the results to multistage equilibrium free energy calculations (MFES) for an established dataset. Our results demonstrate that free energy calculations between NNPs and MM potentials should not be performed using FEP but require NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between MM and NNPs are highly efficient, robust, and trivial to implement. Free energy perturbation ; Energy (signal processing) ; Robustness (evolution) ; Outlier ; Perturbation (astronomy) ; Artificial neural network ; Molecular dynamics ; Work (physics) ; Potential energy ; Range (aeronautics) ; Statistical physics ; Computer science ; Computational physics ; Physics ; Mathematics ; Chemistry ; Materials science ; Statistics ; Artificial intelligence ; Atomic physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Protein Structure and Dynamics
Authors: Pengjie Shi | Shizhe Feng | Zhiping Xu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Metals and Alloys

Non-Equilibrium Nature of Fracture Determines the Crack Paths Fracture (geology) ; Geology ; Economics ; Geotechnical engineering Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Microstructure and mechanical properties
Authors: Fabio E. A. Albertani | Alex J. W. Thom
Source: arXiv (Cornell University) (preprint, 2023-01-05)
Field: Materials Science — Materials Chemistry

Optimised Morse transform of a Gaussian process feature space Morse projections are well-known in chemistry and allow one, within a Morse potential approximation, to redefine the potential in a simple quadratic form. The latter, being a non-linear transform, is also very helpful for machine learning methods as they improve the performance of models by projecting the feature space onto more well-suited coordinates. Usually, the Morse projection parameters are taken from numerical benchmarks. We investigate the effect of changing these parameters latter on the model learning, as well as using the machine learning method itself to make the parameters decision. We find that learning is not necessarily improved by the latter and that general Morse projections are extremely susceptible to changes in the training data. Morse code ; Projection (relational algebra) ; Feature (linguistics) ; Artificial intelligence ; Morse potential ; Space (punctuation) ; Gaussian ; Process (computing) ; Computer science ; Quadratic equation ; Feature vector ; Machine learning ; Algorithm ; Mathematics ; Physics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Computational Drug Discovery Methods
Authors: Yang Zhong | Zhiguo Tao | Weibin Chu | Xin-Gao Gong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2023-02-01)
Field: Materials Science — Materials Chemistry

Accelerating the calculation of electron-phonon coupling by machine learning methods Electron-phonon coupling (EPC) plays an important role in many fundamental physical phenomena, but the high computational cost of the EPC matrix hinders the theoretical research on them. In this paper, an analytical formula is derived to calculate the EPC matrix in terms of the Hamiltonian and its gradient in the nonorthogonal atomic orbital bases. The recently-developed E(3) equivariant neural network is used to directly predict the Hamiltonian and its gradient needed by the formula, thus bypassing the expensive self-consistent iterations in DFT. The correctness of the proposed EPC calculation formula and the accuracy of the predicted EPC values of the network are illustrated by the tests on a water molecule and a MoS2 crystal. Correctness ; Hamiltonian (control theory) ; Artificial neural network ; Computer science ; Electron ; Coupling (piping) ; Physics ; Matrix (chemical analysis) ; Applied mathematics ; Statistical physics ; Quantum mechanics ; Mathematical optimization ; Mathematics ; Materials science ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Fluorides and Related Compounds
Authors: Elena Gazzarrini | Rose K. Cersonsky | Marnik Bercx | Simon Adorf | Nicola Marzari
Source: Research Square (preprint, 2023-08-18)
Field: Materials Science — Materials Chemistry

The rule of four: anomalous stoichiometries of inorganic compounds Stoichiometry ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: King C. Lai | Sebastian Matera | Christoph Scheurer | Karsten Reuter
Source: The Journal of Chemical Physics (article, 2023-07-13)
Field: Materials Science — Materials Chemistry

A fuzzy classification framework to identify equivalent atoms in complex materials and molecules The nature of an atom in a bonded structure-such as in molecules, in nanoparticles, or in solids, at surfaces or interfaces-depends on its local atomic environment. In atomic-scale modeling and simulation, identifying groups of atoms with equivalent environments is a frequent task, to gain an understanding of the material function, to interpret experimental results, or to simply restrict demanding first-principles calculations. However, while routine, this task can often be challenging for complex molecules or non-ideal materials with breaks in symmetries or long-range order. To automatize this task, we here present a general machine-learning framework to identify groups of (nearly) equivalent atoms. The initial classification rests on the representation of the local atomic environment through a high-dimensional smooth overlap of atomic positions (SOAP) vector. Recognizing that not least thermal vibrations may lead to deviations from ideal positions, we then achieve a fuzzy classification by mean-shift clustering within a low-dimensional embedded representation of the SOAP points as obtained through multidimensional scaling. The performance of this classification framework is demonstrated for simple aromatic molecules and crystalline Pd surface examples. Representation (politics) ; Scaling ; Molecule ; Atom (system on chip) ; Ideal (ethics) ; Cluster analysis ; Task (project management) ; Computer science ; Function (biology) ; Fuzzy logic ; Materials science ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Pascal T. Salzbrenner | Se Hun Joo | Lewis J. Conway | Peter I. C. Cooke | Bonan Zhu | Milosz P. Matraszek | William C. Witt | Chris J. Pickard
Source: arXiv (Cornell University) (preprint, 2023-06-10)
Field: Materials Science — Materials Chemistry

Developments and Further Applications of Ephemeral Data Derived Potentials Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction. Computer science ; Suite ; Computational science ; Ternary operation ; Parameterized complexity ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Pál Perjési | Fatemeh Kenari | Szilárd Molnár | Igor D. Borges | Hamilton B. Napolitano
Source: Preprints.org (preprint, 2023-04-21)
Field: Chemistry — Organic Chemistry

(E)-2-Benzylidenecyclanones: Part XVIII.[1] Study the Possible Link Between Glutathione Reactivity and Cancer Cell Cytotoxic Effects of Some Cyclic Chalcone Analogs. A Comparison of the Reactivity of the Open-Chain and the Seven-Membered Homologs Non-enzymatic thiol addition into α,ß-unsaturated carbonyl system is associated with several biological effects. In vivo, the reactions can form small-molecule thiol (e.g., glutathione) or protein thiol adducts. The reaction of two synthetic (4’-methyl- and 4’-methoxy substituted) cyclic chalcone analogs with glutathione and N-acetylcysteine was studied by HPLC-UV method. The selected compounds displayed in vitro cancer cell cytotoxicity (IC50) of different orders of magnitude. The structure of the formed adducts was confirmed by HPLC-MS. The incubations were performed under three different pH conditions (pH 3.5/3.8, 6.3/6.7, and 8.0/7.4). The chalcones intrinsically reacted with both thiols under all incubation conditions. The initial rates and compositions of the final mixtures depended on the substitution and the pH. The frontier molecular orbitals and the Fukui function were carried out to investigate the effects on open-chain and seven-membered cyclic analogs. Furthermore, machine learning protocols were used to provide more insights into physicochemical properties and to support the different thiol-reactivity. HPLC analysis indicated diastereoselectivity of the reactions. The observed reactivities do not directly relate to the different in vitro cancer cell cytotoxicity of the compounds. Chemistry ; Chalcone ; Thiol ; Glutathione ; Reactivity (psychology) ; Cytotoxicity ; Adduct ; High-performance liquid chromatography ; Stereochemistry ; In vitro ; Organic chemistry ; Enzyme ; Biochemistry Synthesis and biological activity ; Sulfur-Based Synthesis Techniques ; Synthesis of Organic Compounds
Authors: Thi Bao Chau Bui | Daiki IIDA | Yutaka Kitamura | Mito Kokawa
Source: Food Chemistry (article, 2023-12-12)
Field: Chemistry — Analytical Chemistry

Utilization of multiple-dilution fluorescence fingerprint facilitates prediction of chemical attributes in spice extracts Spice ; Fingerprint (computing) ; Polyphenol ; Biological system ; Dilution ; Chemistry ; Computer science ; Process engineering ; Artificial intelligence ; Antioxidant ; Engineering ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Dye analysis and toxicity ; Pesticide Residue Analysis and Safety