jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marcel F. Langer | J. Thorben Frank | Florian Knoop
Source: arXiv (Cornell University) (preprint, 2023-05-02)
Field: Materials Science — Materials Chemistry

Stress and heat flux via automatic differentiation Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface. This potential determines many materials properties and simulation techniques usually require its gradients, in particular forces and stress for molecular dynamics, and heat flux for thermal transport properties. Recently developed potentials feature high body order and can include equivariant semi-local interactions through message-passing mechanisms. Due to their complex functional forms, they rely on automatic differentiation (AD), overcoming the need for manual implementations or finite-difference schemes to evaluate gradients. This study demonstrates a unified AD approach to obtain forces, stress, and heat flux for such potentials, and provides a model-independent implementation. The method is tested on the Lennard-Jones potential, and then applied to predict cohesive properties and thermal conductivity of tin selenide using an equivariant message-passing neural network potential. Computer science ; Stress (linguistics) ; Thermal ; Heat flux ; Artificial neural network ; Message passing ; Feature (linguistics) ; Thermal conductivity ; Automatic differentiation ; Flux (metallurgy) ; Statistical physics ; Mechanics ; Artificial intelligence ; Distributed computing ; Heat transfer ; Physics ; Algorithm ; Chemistry ; Thermodynamics Machine Learning in Materials Science ; Thermal properties of materials ; thermodynamics and calorimetric analyses
Authors: Adela Habib | Nicholas Lubbers | Sergei Tretiak | Benjamin Nebgen
Source: arXiv (Cornell University) (preprint, 2023-03-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realization of their full energy generating potential. Addressing this challenge requires detailed understanding of physical processes from plasmon excitation in metal to their collection in a molecule or a semiconductor, where atomistic theoretical investigation may be particularly beneficial. Unfortunately, first-principles theoretical modeling of these processes is extremely costly, limiting the analysis to systems with a few 100s of atoms. Recent advances in machine learned interatomic potentials suggest that dynamics can be accelerated with surrogate models which replace the full solution of the Schroedinger Equation. Here, we modify an existing neural network, Hierarchically Interacting Particle Neural Network (HIP-NN), to predict plasmon dynamics in Ag nanoparticles. We demonstrate the model's capability to accurately predict plasmon dynamics in large nanoparticles of up to 561 atoms not present in the training dataset. More importantly, with machine learning models we gain a speed-up of about 200 times as compared with the rt-TDDFT calculations when predicting important physical quantities such as dynamic dipole moments in Ag55 and about 4000 times for extended nanoparticles that are 10 times larger. This underscores the promise of future machine learning accelerated electron/nuclear dynamics simulations for understanding fundamental properties of plasmon-driven hot carrier devices. Plasmon ; Artificial neural network ; Nanoparticle ; Computer science ; Nanotechnology ; Materials science ; Physics ; Artificial intelligence ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture
Authors: Sehan Lee | Jaechang Lim | Woo Youn Kim
Source: arXiv (Cornell University) (preprint, 2023-09-27)
Field: Materials Science — Materials Chemistry

C3Net: interatomic potential neural network for prediction of physicochemical properties in heterogenous systems Understanding the interactions of a solute with its environment is of fundamental importance in chemistry and biology. In this work, we propose a deep neural network architecture for atom type embeddings in its molecular context and interatomic potential that follows fundamental physical laws. The architecture is applied to predict physicochemical properties in heterogeneous systems including solvation in diverse solvents, 1-octanol-water partitioning, and PAMPA with a single set of network weights. We show that our architecture is generalized well to the physicochemical properties and outperforms state-of-the-art approaches based on quantum mechanics and neural networks in the task of solvation free energy prediction. The interatomic potentials at each atom in a solute obtained from the model allow quantitative analysis of the physicochemical properties at atomic resolution consistent with chemical and physical reasoning. The software is available at https://github.com/SehanLee/C3Net. Solvation ; Context (archaeology) ; Computer science ; Artificial neural network ; Atom (system on chip) ; Set (abstract data type) ; Statistical physics ; Biological system ; Theoretical computer science ; Chemistry ; Artificial intelligence ; Molecule ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Pablo A. M. Casares | Jack S. Baker | Matija Medvidović | Roberto dos Reis | Juan Miguel Arrazola
Source: arXiv (Cornell University) (preprint, 2023-09-23)
Field: Materials Science — Materials Chemistry

Grad DFT: a software library for machine learning enhanced density functional theory Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT; an endeavor with many open questions and technical challenges. In this work, we present Grad DFT: a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange-correlation energy functionals. Grad DFT employs a pioneering parametrization of exchange-correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, Grad DFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy. Density functional theory ; Computer science ; Scalability ; Differentiable function ; Artificial intelligence ; Software ; Machine learning ; Parametrization (atmospheric modeling) ; Artificial neural network ; Hybrid functional ; Benchmarking ; Theoretical computer science ; Computational chemistry ; Quantum mechanics ; Chemistry ; Mathematics ; Physics ; Database ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Eugène Sanscartier | Félix Saint-Denis | Karl-Étienne Bolduc | Normand Mousseau
Source: The Journal of Chemical Physics (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Evaluating approaches for on-the-fly machine learning interatomic potentials for activated mechanisms sampling with the activation-relaxation technique nouveau In the last few years, much effort has gone into developing general machine-learning potentials capable of describing interactions for a wide range of structures and phases. Yet, as attention turns to more complex materials, including alloys and disordered and heterogeneous systems, the challenge of providing reliable descriptions for all possible environments becomes ever more costly. In this work, we evaluate the benefits of using specific vs general potentials for the study of activated mechanisms in solid-state materials. More specifically, we test three machine-learning fitting approaches using the moment-tensor potential to reproduce a reference potential when exploring the energy landscape around a vacancy in Stillinger-Weber silicon crystal and silicon-germanium zincblende structures using the activation-relaxation technique nouveau (ARTn). We find that a targeted on-the-fly approach specific to and integrated into ARTn generates the highest precision on the energetics and geometry of activated barriers while remaining cost-effective. This approach expands the types of problems that can be addressed with high-accuracy ML potential. Relaxation (psychology) ; Computer science ; Range (aeronautics) ; On the fly ; Work (physics) ; Silicon ; Tensor (intrinsic definition) ; Statistical physics ; Artificial intelligence ; Materials science ; Physics ; Nanotechnology ; Machine learning ; Neuroscience ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: E. O. Hazieva | R. E. Ryltsev | N. M. Chtchelkatchev | A. O. Tipeev | Е. О. Хазиева | Р. Е. Рыльцев | Н. М. Щелкачев | А. О. Типеев
Source: Electronic scientific archive of UrFU (Ural Federal University) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

COMPUTER MODELING OF THE STRUCTURE AND PHYSICOCHEMICAL PROPERTIES OF Al-Cu ALLOYS USING INTERPARTICLE POTENTIALS BASED ON NEURAL NETWORKS We show that DP reproduces very well ab initio data for energies, forces and virials, as well as radial distribution functions and velocity autocorrelation functions extracted from AIMD. The experimental data are generally in good agreement with the experiment. Autocorrelation ; Artificial neural network ; Ab initio ; Distribution (mathematics) ; Experimental data ; Work (physics) Machine Learning in Materials Science ; Microstructure and mechanical properties ; Metallurgy and Material Forming
Authors: Casey L. Olen | Andrew F. Zahrt | Sean W. Reilly | Danielle M. Schultz | Khateeta M. Emerson | David A. Candito | Neil A. Strotman | Scott E. Denmark
Source: ChemRxiv (preprint, 2023-05-29)
Field: Chemistry — Inorganic Chemistry

Chemoinformatic Catalyst Selection Methods for the Optimization of Copper-Bis(oxazoline) Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions A catalyst selection method for the optimization of an asymmetric, vinylogous Mukaiyama aldol reaction is described. A large library of commercially available and synthetically accessible copper-bis(oxazoline) catalysts was constructed in silico. Conformer-dependent, grid-based descriptors were calculated for each catalyst, defining a chemical feature space suitable for machine learning. Selection of a diverse subset of catalyst space produced an initial training set of 26 novel bis(oxazoline) ligands which were synthesized and tested for stereoselectivity in the copper-catalyzed, vinylogous Mukaiyama aldol reaction for five substrate combinations. One ligand in the training set provided 88% average enantiomeric excess, exceeding the performance of catalysts identified through an initial optimization campaign. Supervised and semi-supervised catalyst selection methods, including quantitative structure-selectivity relationship modelling, nearest neighbors analysis, and a focused analogue clustering strategy, were employed to identify an additional 12 novel bis(oxazoline) ligands. The selected ligands outperformed the initial training set hit in four out of five product classes, and in some cases demonstrated excellent enantiocontrol exceeding 95% ee. The effectiveness of the unsupervised training set selection process is discussed, and the expediency of the nearest neighbor and focused analogue approaches are contrasted with the supervised quantitative structure-selectivity relationship modelling approach. Oxazoline ; Aldol reaction ; Chemical space ; Chemistry ; Stereoselectivity ; Catalysis ; Combinatorial chemistry ; Computer science ; Machine learning ; Artificial intelligence ; Organic chemistry ; Drug discovery Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Raquel López-Ríos de Castro | Robert M. Ziolek | Christian D. Lorenz
Source: ChemRxiv (preprint, 2023-02-13)
Field: Chemistry — Organic Chemistry

Topology-Controlled Self-Assembly of Amphiphilic Block Copolymers Contemporary synthetic chemistry approaches can be used to yield a range of distinct polymer topologies with precise control. The topology of a polymer strongly influences its self-assembly into complex nanostructures however a clear mechanistic understanding of the relationship between polymer topology and self-assembly has not yet been developed. In this manuscript, we utilize classical all-atom molecular dynamics simulations of three different topologies (two triblock linear polymers and one ring polymer) of poly(ethylene oxide) - poly(methyl acrylate) block copolymers to provide a mechanistic understanding of the influence of topology in micelle size and stability. We find that the topology affects the ability of the micelle to form a compact hydrophobic core, which directly affects its stability. Also, we apply unsupervised machine learning techniques to show that the topology of a polymer affects its ability to take a conformation in response to the local environment within the micelles. This work provides foundations for the rational design of polymer nanostructures based on their underlying topology. Topology (electrical circuits) ; Micelle ; Polymer ; Amphiphile ; Copolymer ; Materials science ; Network topology ; Ethylene oxide ; Self-assembly ; Nanotechnology ; Chemistry ; Computer science ; Organic chemistry ; Mathematics Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Machine Learning in Materials Science
Authors: Rong Zhang | Rongqing Yuan | Boxue Tian
Source: arXiv (Cornell University) (preprint, 2023-10-08)
Field: Materials Science — Materials Chemistry

PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry Predicting quantum chemical properties is a fundamental challenge for computational chemistry. While the development of graph neural networks has advanced molecular representation learning and property prediction, their performance could be further enhanced by incorporating 3D structural geometry into 2D molecular graph representation. In this study, we introduce the PointGAT model for quantum molecular property prediction, which integrates 3D molecular coordinates with graph-attention modeling. Comparison with other current models in molecular prediction tasks showed that PointGAT could provide higher predictive accuracy in various benchmark datasets from MoleculeNet, including ESOL, FreeSolv, Lipop, HIV, and 10 out of 12 tasks of the QM9 dataset. To further examine PointGAT prediction of quantum mechanical (QM) energies, we constructed a C10 dataset comprising 11,841 charged and chiral carbocation intermediates with QM energies calculated at the DM21/6-31G*//B3LYP/6-31G* levels. Notably, PointGAT achieved an R2 value of 0.950 and an MAE of 1.616 kcal/mol, outperforming other models. Additional ablation studies indicated that incorporating molecular geometry into the model resulted in markedly higher predictive accuracy, reducing the MAE value from 1.802 kcal/mol to 1.616 kcal/mol. Moreover, visualization of PointGAT atomic attention weights suggested its predictions were interpretable. Findings in this study support the application of PointGAT as a powerful and versatile tool for quantum chemical property prediction that can facilitate high-accuracy modeling for fundamental exploration of chemical space as well as drug design and molecular engineering. Molecular graph ; Quantum chemical ; Graph ; Benchmark (surveying) ; Molecular descriptor ; Quantum chemistry ; Computer science ; Quantum ; Property (philosophy) ; Representation (politics) ; Molecular geometry ; Visualization ; Quantitative structure–activity relationship ; Computational chemistry ; Algorithm ; Chemistry ; Theoretical computer science ; Machine learning ; Artificial intelligence ; Molecule ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Haoyue Guo | Matthew R. Carbone | Chuntian Cao | Jianzhou Qu | Yonghua Du | Seong‐Min Bak | Conan Weiland | Feng Wang | Shinjae Yoo | Nongnuch Artrith | Alexander E. Urban | Deyu Lu
Source: arXiv (Cornell University) (preprint, 2023-01-31)
Field: Materials Science — Ceramics and Composites

Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra of crystalline and amorphous lithium thiophosphate materials based on the atomic structures reported in Chem. Mater., 34, 6702 (2022). The XAS database is based on simulations using the excited electron and core-hole pseudopotential approach implemented in the Vienna Ab initio Simulation Package. Our database contains 2681 S K-edge XAS spectra for 66 crystalline and glassy structure models, making it the largest collection of first-principles computational XAS spectra for glass/ceramic lithium thiophosphates to date. This database can be used to correlate S spectral features with distinct S species based on their local coordination and short-range ordering in sulfide-based solid electrolytes. The data is openly distributed via the Materials Cloud, allowing researchers to access it for free and use it for further analysis, such as spectral fingerprinting, matching with experiments, and developing machine learning models. X-ray absorption spectroscopy ; Fast ion conductor ; XANES ; Lithium (medication) ; Amorphous solid ; Absorption spectroscopy ; X-ray absorption fine structure ; Pseudopotential ; Spectroscopy ; Coordination number ; Chemistry ; Materials science ; Database ; Electrolyte ; Physical chemistry ; Crystallography ; Ion ; Atomic physics ; Computer science ; Optics ; Physics Glass properties and applications ; X-ray Spectroscopy and Fluorescence Analysis ; Advanced Battery Materials and Technologies
Authors: Dario Massa | Efthimios Kaxiras | Stefanos Papanikolaou
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Alloy Informatics Through Ab Initio Charge Density Profiles: Case Study of Hydrogen Effects in Face-Centered Cubic Crystals Ab initio ; Cubic crystal system ; Alloy ; Face (sociological concept) ; Materials science ; Hydrogen ; Charge (physics) ; Crystallography ; Charge density ; Condensed matter physics ; Molecular physics ; Chemical physics ; Chemistry ; Metallurgy ; Physics ; Quantum mechanics ; Sociology ; Organic chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Tzuhsiung Yang | T. C. Lee | Yuting Chen | Hao-Ting Chang
Source: Research Square (preprint, 2023-10-02)
Field: Materials Science — Materials Chemistry

A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes Abstract Deep generative models (DGMs) have shown great promise in the generation of organic molecules and inorganic materials with chemical sensible structures and optimized properties. However, there is a lack of their applications in transition metal (TM) complexes due to their flexible coordination environment, multiple accessible oxidation and spin states, despite the importance of these complexes in fine chemical synthesis, commodity production, and optical applications. Herein, we propose a joint semi-supervised junction-tree variational autoencoder (SSVAE) and artificial neural network (ANN) classifier model, coined as LiveTransForM (Ligand variational auto-encoder and Transfer learning For transition Metal complexes), for the design of octahedral TM complexes. LiveTransForM allows the design of ligands that build up TM complexes and the prediction of the spin states of the assembled complexes. We show that the accuracy of the classifier is improved when the latent variables from the SSVAE are used as input for the ANN model compared to those from the unsupervised VAE. Input augmentation using the three molecular axes also improves the accuracy of the classifier. 58 complexes with predicted spin states are then generated by LiveTransForM and the accuracy of their spin state labels are validated by density functional theory methods. Two design strategies, single mutation and seeded generation, are also introduced to allow the directed evolution of parent complexes towards desirable spin states and local modification of seed complexes to generate complexes with similar spin states, respectively. Autoencoder ; Classifier (UML) ; Artificial intelligence ; Deep learning ; Artificial neural network ; Computer science ; Transition metal ; Density functional theory ; Encoder ; Materials science ; Biological system ; Pattern recognition (psychology) ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis