jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mikhail Suyetin
Source: ChemRxiv (preprint, 2023-06-21)
Field: Chemistry — Inorganic Chemistry

Exploring Propane/Propylene Separation through Molecular Dynamics Simulations of Flexible Metal-Organic Framework Models Propylene is a vital component for the petrochemical industry. The overwhelming majority of the studies is focused on propylene/propane separation. Nevertheless, as the ultimate goal is to obtain propylene as the end product, propylene-selective MOFs necessitate a demanding and complex desorption process, which consumes a substantial amount of energy and involves intricate operations. Consequently, having an adsorbent that preferentially adsorbs propane instead would be more advantageous. This approach would enable the production of high-purity propylene in a single step and lead to significant reductions in energy consumption and the quantity of adsorbent required. The Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 database is utilized for the computational screening of flexible MOFs using molecular dynamics simulations to identify materials with strong potential for separating propane/propylene via self-diffusion. This screening process is the first to fully consider the impact of framework flexibility on the discovery of guest self-diffusion coefficients. The study underscores the significance of accounting for framework flexibility in investigations of gas molecules transport in MOFs, showcases the potential of using data-driven approaches to identify high-performance materials, and suggests methods for enhancing the predictive capabilities of screening workflows. The top 5 MOFs from the CoRE MOF database for propane and propylene separation at a temperature of 298 K are identified. It is observed that the presence of carbonyl groups greatly enhances the separation properties between propane and propylene. Subsequently, a machine learning approach is utilized to develop a model and uncover important features. The machine learning model's predicted values for self-diffusion are reasonably consistent with the data obtained from molecular dynamics simulations. Propane ; Metal-organic framework ; Flexibility (engineering) ; Materials science ; Petrochemical ; Molecular dynamics ; Process engineering ; Biochemical engineering ; Computer science ; Adsorption ; Chemistry ; Organic chemistry ; Computational chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Lanthanide and Transition Metal Complexes
Authors: Ilia Kevlishvili | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

Discovery of Hemilabile Ligands Using MachineLearning Discovery of hemilabile ligands that optimally balance reactivity and stability is important for identifying novel catalyst structures. We design a workflow for identifying ligands in the Cambridge Structural Database (CSD) that have been crystalized with distinct denticities and are thus identifiable as hemilabile ligands. To overcome the difficulty of identifying negative example, non-hemilabile ligands in our data set, we implement a semi-supervised learning approach using a label-spreading algorithm together with a set of heuristic rules based on ligand frequency of appearance. We show that a heuristic based on coordinating atom identity alone is not sufficient to identify whether a ligand is hemilabile and our trained machine-learning classification models are instead needed to predict whether a bi-, tri-, or tetradentate ligand is hemilabile with high accuracy and precision. We gain deeper insight into the factors that govern ligand hemilability by conducting feature importance analysis on our models, finding that the second, third, and fourth coordination spheres all play an important role in ligand hemilability. Ligand (biochemistry) ; Set (abstract data type) ; Heuristic ; Computer science ; Feature (linguistics) ; Stability (learning theory) ; Workflow ; Artificial intelligence ; Machine learning ; Chemistry ; Philosophy ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Raul Ortega Ochoa | Bardi Benediktsson | Renata Sechi | Peter Bjørn Jørgensen | Arghya Bhowmik
Source: ChemRxiv (preprint, 2023-09-26)
Field: Materials Science — Materials Chemistry

Materials Funnel 2.0 - Data-driven hierarchical search for exploration of vast chemical spaces Innovating ways to explore the materials phase space accelerates functional materials discovery. For breakthrough materials, faster exploration of larger phase spaces is a key goal. High-throughput computational screening (HTCS) is widely used to rapidly search for materials with the desired functional property. This article redefines the HTCS methods to combine multiple deep learning models and physics-based simulation to explore much larger chemical spaces than possible by pure physics-driven HTCS. Deep generative models are used to autonomously create materials libraries with a high likelihood of desired properties, inverting the standard design paradigm. Additionally, machine-learned surrogates enable the next layer of screening to prune the set further so that high-quality quantum-mechanical simulations can be performed. With organic photovoltaic (OPV) molecules as a test bench, the power of this redesigned HTCS approach is shown in the inverse design of OPV molecules with very limited computational expense using only 1% of the original physics-based screening dataset. Chemical space ; Set (abstract data type) ; Computer science ; Space (punctuation) ; Property (philosophy) ; Phase space ; Key (lock) ; Nanotechnology ; Generative model ; Artificial intelligence ; Generative grammar ; Materials science ; Physics ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Mgcini Keith Phuthi | Archie Mingze Yao | Simon Batzner | Albert Musaelian | Boris Kozinsky | Ekin D. Cubuk | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2023-04-24)
Field: Materials Science — Materials Chemistry

Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at which lithium exists in batteries where it is found to have enhanced strength, with implications for dendrite suppression strategies. Computationally, there is a lack of empirical potentials that are consistently quantitatively accurate across all properties and ab-initio calculations are too costly. In this work, we train Machine Learning Interaction Potentials (MLIPs) on Density Functional Theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab-initio results across a wide range of simulations at large length and time scales. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants and various surface properties inaccessible using DFT. We establish that there exists a Bell-Evans-Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets. Lithium (medication) ; Ab initio ; Density functional theory ; Materials science ; Thermodynamics ; Statistical physics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Xuecheng Shao | Lukas Paetow | Mark E. Tuckerman | Marcelo Antonio Pavanello
Source: arXiv (Cornell University) (preprint, 2023-02-21)
Field: Materials Science — Materials Chemistry

Machine Learning Electronic Structure Methods Based On The One-Electron Reduced Density Matrix The theorems of density functional theory (DFT) and reduced density matrix functional theory (RDMFT) establish a bijective map between the external potential of a many-body system and its electron density or one-particle reduced density matrix. Building on this foundation, we show that machine learning can be used to generate surrogate electronic structure methods. In particular, we generate surrogates of local and hybrid DFT as well as Hartree-Fock and full configuration interaction theories for systems ranging from small molecules such as water to more complex compounds like propanol and benzene. The surrogate models use the one-electron reduced density matrix as the central quantity to be learned. From the predicted density matrices, we show that either standard quantum chemistry or a second machine-learning model can be used to compute molecular observables, energies, and atomic forces. We show that the surrogate models can generate essentially anything that a standard electronic structure method can, ranging from band gaps and Kohn-Sham orbitals to energy-conserving ab-initio molecular dynamics simulations and IR spectra, which account anharmonicity and thermal effects, without the need of computationally expensive algorithms such as self-consistent field theory. The algorithms developed here are packaged in an efficient and easy to use Python code, QMLearn, accessible by the broader community on popular platforms. Density functional theory ; Orbital-free density functional theory ; Density matrix ; Electronic structure ; Fock matrix ; Statistical physics ; Ab initio ; Time-dependent density functional theory ; Electronic density ; Python (programming language) ; Computer science ; Physics ; Quantum mechanics ; Computational chemistry ; Quantum ; Chemistry ; Fock space Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Raquel López-Ríos de Castro | Robert M. Ziolek | Martin B. Ulmschneider | Christian D. Lorenz
Source: ChemRxiv (preprint, 2023-11-15)
Field: Materials Science — Biomaterials

Therapeutic peptides are preferentially solubilized in specific microenvironments within PEG-PLGA polymer nanoparticles Polymeric nanoparticles represent a highly promising drug delivery formulation. However, a lack of understanding of the molecular mechanisms that underlie their drug solubilization and controlled release capabilities has hindered efficient clinical translation of such technologies. Polyethylene glycol-poly(lactic-co-glycolic) acid (PEG-PLGA) nanoparticles have been widely studied as cancer drug delivery vehicles. In this manuscript we use unbiased coarse-grained molecular dynamics simulations to model the self-assembly of a PEG-PLGA nanoparticle and its solubulization of the anticancer peptide, EEK. This nanoformulation has been shown to be efficacious against triple negative breast cancer cells \textit{in vivo}. The physical characteristics of our simulated PEG-PLGA nanoparticles are in good agreement with the previously reported experimentalquantities. To describe the internal structure of the nanoparticles, we apply unsupervised machine learning techniques to quantify the conformations that polymers adopt at various locations within the nanoparticle. We find that the local microenvironments formed by the various polymer conformations promote preferential EEK solubilization within specific regions of the NP. This demonstrates that these microenvironments are key in controlling drug storage locations within nanoparticles, implying that the individual polymer conformations within such nanoparticles are potentially tunable parameters for the rational design of new polymer nanoparticles for therapeutic applications. Nanoparticle ; PLGA ; PEG ratio ; Polyethylene glycol ; Polymer ; Nanotechnology ; Drug delivery ; Rational design ; Materials science ; Biophysics ; Chemistry ; Combinatorial chemistry ; Organic chemistry Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; RNA Interference and Gene Delivery
Authors: Juan Vélez-Álvarez | Wilfred Cordero | Kheythor Arboleda | Neiron Rios
Source: Results in Optics (article, 2023-09-04)
Field: Chemistry — Analytical Chemistry

Determination of cement quality via optical spectroscopy and machine learning-based image recognition Recent events in Colombia like the collapse of the Space building in Medellin, or the Chirajara motorway bridge have undermined public opinion generating a bad perception regarding the construction industry. Some of these problems were related to structural design flaws but also poor quality materials. To partially tackle this problem a novel instrument was designed to assess the quality of the cement by capturing inelastic scattering vibrational modes of the C2S, C3S, and CSH. By combining the spectral information with a machine learning model able to recognize the intensity levels of the spectral bands associated with a high quality cement it was possible to detect low quality samples. After the training process of the neural network, the tool successfully recognized 95% of the samples provided. Quality (philosophy) ; Cement ; Perception ; Artificial neural network ; Artificial intelligence ; Computer science ; Process (computing) ; Machine learning ; Bridge (graph theory) ; Pattern recognition (psychology) ; Materials science ; Composite material ; Physics ; Psychology Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Water Quality Monitoring and Analysis
Authors: Michael Scherbela | Leon Gerard | Philipp Grohs
Source: arXiv (Cornell University) (preprint, 2023-07-15)
Field: Materials Science — Materials Chemistry

Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions Obtaining accurate solutions to the Schrödinger equation is the key challenge in computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) has recently outperformed conventional approaches in terms of accuracy, but only at large computational cost. Whereas in many domains models are trained once and subsequently applied for inference, accurate DL-VMC so far requires a full optimization for every new problem instance, consuming thousands of GPUhs even for small molecules. We instead propose a DL-VMC model which has been pre-trained using self-supervised wavefunction optimization on a large and chemically diverse set of molecules. Applying this model to new molecules without any optimization, yields wavefunctions and absolute energies that outperform established methods such as CCSD(T)-2Z. To obtain accurate relative energies, only few fine-tuning steps of this base model are required. We accomplish this with a fully end-to-end machine-learned model, consisting of an improved geometry embedding architecture and an existing SE(3)-equivariant model to represent molecular orbitals. Combining this architecture with continuous sampling of geometries, we improve zero-shot accuracy by two orders of magnitude compared to the state of the art. We extensively evaluate the accuracy, scalability and limitations of our base model on a wide variety of test systems. Wave function ; Variational Monte Carlo ; Quantum Monte Carlo ; Embedding ; Monte Carlo method ; Computer science ; Atomic orbital ; Scalability ; Statistical physics ; Algorithm ; Artificial intelligence ; Physics ; Quantum mechanics ; Mathematics ; Electron Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Farhana Sultana | Elhassan Mostafa Abdallah | Shaohua Dong | Yasir Mukhtar
Source: Journal of Physics Conference Series (article, 2023-10-01)
Field: Chemistry — Analytical Chemistry

Development of Predictive Model for Wax Formation in Deep-water Pipeline Using Machine learning Abstract Wax deposition in deep sea pipelines can affect oil and gas production. It happens when the pipeline temperature drops below the wax appearance temperature, causing the wax to solidify and stick to the pipeline walls. This paper discusses the limitations of current models in predicting wax deposition in pipelines and proposes the development of more complex, intelligent models that are accurate, resilient, and adaptable to novel input-output scenarios. It also highlights various methods for preventing wax deposition, including the use of chemical inhibitors and thermal remediation techniques. The paper aims to develop a neural network modeling approach for predicting wax deposition potential in producing reservoirs to prevent accumulation before removal. The parameters that were used are wax molecule concentration gradient at pipe wall, temperature gradient at pipe wall, reported wax deposition rate, the pipe wall shear stress, and crude oil viscosity. The radial basis function neural network (RBFNN), is based on the machine learning technique. RBFNN is a good choice for building a predictive wax deposition rate model because it is highly accurate, robust to noise and outliers, computationally efficient, can model complex nonlinear relationships, suitable for real-time applications and requires less training data. The RBF neural network is constructed using MATLAB. The outcomes and experimental data are consistent with a 1.5 percent relative inaccuracy when compared to prior studies. The R^2 value was 0.9906, R value was 0.9953 and the RMSE value was 0.8598. The RBF neural network-based approach for predicting wax deposition rate was successful. Wax ; Pipeline transport ; Artificial neural network ; Pipeline (software) ; Petroleum engineering ; Deposition (geology) ; Artificial intelligence ; Computer science ; Biological system ; Materials science ; Machine learning ; Environmental science ; Mechanical engineering ; Engineering ; Geology ; Composite material ; Environmental engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydraulic Fracturing and Reservoir Analysis
Authors: Sergei Manzhos | Manabu Ihara
Source: arXiv (Cornell University) (preprint, 2023-01-13)
Field: Materials Science — Materials Chemistry

Neural network with optimal neuron activation functions based on additive Gaussian process regression Feed-forward neural networks (NN) are a staple machine learning method widely used in many areas of science and technology. While even a single-hidden layer NN is a universal approximator, its expressive power is limited by the use of simple neuron activation functions (such as sigmoid functions) that are typically the same for all neurons. More flexible neuron activation functions would allow using fewer neurons and layers and thereby save computational cost and improve expressive power. We show that additive Gaussian process regression (GPR) can be used to construct optimal neuron activation functions that are individual to each neuron. An approach is also introduced that avoids non-linear fitting of neural network parameters. The resulting method combines the advantage of robustness of a linear regression with the higher expressive power of a NN. We demonstrate the approach by fitting the potential energy surfaces of the water molecule and formaldehyde. Without requiring any non-linear optimization, the additive GPR based approach outperforms a conventional NN in the high accuracy regime, where a conventional NN suffers more from overfitting. Overfitting ; Sigmoid function ; Artificial neural network ; Computer science ; Kriging ; Activation function ; Robustness (evolution) ; Artificial intelligence ; Gaussian process ; Gaussian ; Biological system ; Machine learning Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jolla Kullgren | Andreas Röckert | Kersti Hermansson
Source: The Journal of Physical Chemistry C (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Water in Crystals: A Database for ML and a Knowledge Base for Vibrational Prediction Hydrate crystals are excellent reference systems to learn about aqueous systems. We have created a database of density functional theory (DFT)-optimized (optPBE-vdW) structures and vibrational frequencies for 101 crystalline hydrate and hydroxide bulk systems and over 300 unique oscillators and use it to explore and discuss the tradeoff between prediction accuracy and insight. Starting from a machine-learning geometrical descriptor, we gradually include more physics/chemistry flavor in the descriptor and examine how the frequency prediction power varies. The most accurate models are the machine-learned model (of modest insight) and a physically motivated model containing the electric field and field gradient. Furthermore, detailed comparisons with experimental correlations show that, where available data exists, our DFT results largely overlap with the experiment. A small blind-test showed that our machine-learned (ML) descriptor model can be used to predict experimental vibrational frequencies based only on the experimental structures and our best-regressed model, with encouraging results. Hydrate ; Field (mathematics) ; Experimental data ; Base (topology) ; Computer science ; Density functional theory ; Hydroxide ; Aqueous solution ; Materials science ; Artificial intelligence ; Database ; Chemistry ; Computational chemistry ; Mathematics ; Physical chemistry ; Statistics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Methane Hydrates and Related Phenomena
Authors: Jie Xiao | Guodong Fang | Xiangyu Jin | Bing Wang | Songhe Meng
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Spectroscopy

A Comprehensive Study of Pyrolysis Characteristics of Silicone-Modified Phenolic Aerogel Matrix Nanocomposites: Kinetic Analysis, Reaxff Md Simulations, and Ann Prediction Nanocomposite ; Materials science ; Pyrolysis ; Chemical engineering ; Thermal decomposition ; Methanol ; Fourier transform infrared spectroscopy ; Thermal stability ; Chemistry ; Composite material ; Organic chemistry Aerogels and thermal insulation ; Flame retardant materials and properties ; Polymer Nanocomposites and Properties