cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/25527766.v1
title: Development of Advanced Computational Methods for Accurate Property Prediction of Fluids: Application to Hydrofluorocarbon/Ionic Liquid Mixtures

description: Hydrofluorocarbon (HFC) refrigerants with zero ozone-depleting potential have replaced chlorofluorocarbons and are widely used in the heating, ventilation, air-conditioning, and refrigeration industries. However, some HFCs exhibit high global warming potential, which has led to calls by governments for their phaseout. Technologies to recycle, repurpose, and separate these HFCs (which are often used as mixtures) need to be developed. Ionic liquids (ILs) show promise as efficient entrainers for separating HFC mixtures via extractive distillation. Therefore, thermophysical properties of HFCs, ILs, and their mixtures are needed over a wide range of conditions. Molecular simulations can help understand and predict various properties effectively. The first half of this work explores the application of molecular dynamics (MD) simulations in understanding fluid properties and method development of solubility calculations, with a focus on HFC refrigerants and IL systems. To be specific, we proposed a new all-atom force field (FF) for tris(pentafluoroethyl)trifluorophosphate ([FAP]) anion and investigated the effect of [FAP] isomer content on different properties of 1-n-hexyl-3-methylimidazolium tris(pentafluoroethyl)trifluorophosphate. Thermophysical, dynamic, and structural properties were also systematically studied for HFC-32 and HFC-125 in imidazolium-based ILs by MD simulations. At the same time, we also developed a workflow that combines Hamiltonian replica exchange MD simulations with alchemical free energy calculations to accurately compute the full solubility isotherm of HFC/IL mixtures. Accurate FFs are essential for meaningful property prediction. Therefore, the second half of this work focuses on calibrating Lennard-Jones parameters of classical FFs using machine learning techniques for five refrigerants and developing the General Amber FF-based polarizable models for ILs using Drude oscillators.
DOI: 10.7274/25528075
title: Investigation of Plasma Surface Interactions: Methane and Nitrogen Plasma on Model Catalytic Surfaces

description: Atmospheric pressure non-thermal plasmas (NTPs) produce a highly reactive chemical environment, including electrons, ions, radicals, vibrationally- and electronically-excited atoms and/or molecules, at low bulk temperatures at which such species are thermally inaccessible. The integration of NTPs and heterogeneous catalysis has recently gained substantial interest to carry out chemical transformations that are difficult using conventional thermal catalysis (e.g., methane (CH4) and nitrogen (N2) activation). The synergy between plasma and a catalytic surface originates from their interactions at the interface. Thus, to better control the plasma-catalyst coupling, it is essential to understand how the reactive chemical environment produced in the plasma phase interacts with catalytic materials.
However, progress in plasma catalysis is hindered by the lack of fundamental understanding of plasma-catalytic surface interactions due to the complexity of plasma catalysis which includes co-existence of plasma-phase and surface chemistry, and heterogeneity of catalytic materials. In this work, the interactions between NTP-activated methane and nitrogen and various model catalytic and non-catalytic surfaces (Ni, Pd, Cu, Ag, Au, SiO2, and KBr) are studied using a newly developed multimodal spectroscopic tool combining polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS), optical emission spectroscopy (OES), and mass spectrometry (MS).
First, this work describes the development of the novel multimodal spectroscopic tool and demonstrates its utility by probing plasma-assisted non-oxidative coupling of CH4 over various surfaces. The results unveil the activation of carbonaceous surface species by an argon NTP on Ni and SiO2, forming hydrogen and C2 hydrocarbons, through distinct pathways. Following this study, the interactions between NTP-activated N2 and various model catalytic surfaces were investigated thoroughly to identify the surface-adsorbed nitrogen-containing species. The results show the potential of non-thermal plasmas to access thermally inaccessible adsorbates and the challenges posed by ppm-level impurities to corrupt the interpretation of plasma catalytic chemistry. Finally, the formation of C-N containing compounds from the plasma-catalytic coupling of CH4 and N2 together over model catalytic surfaces was investigated. C-N coupled products formed were further analyzed with X-ray photoelectron spectroscopy and liquid chromatography-mass spectrometry. The results show the importance of surface chemistry and exposure conditions in surface C-N coupling with plasma stimulation.
DOI: 10.7274/25528075.v1
title: Investigation of Plasma Surface Interactions: Methane and Nitrogen Plasma on Model Catalytic Surfaces

description: Atmospheric pressure non-thermal plasmas (NTPs) produce a highly reactive chemical environment, including electrons, ions, radicals, vibrationally- and electronically-excited atoms and/or molecules, at low bulk temperatures at which such species are thermally inaccessible. The integration of NTPs and heterogeneous catalysis has recently gained substantial interest to carry out chemical transformations that are difficult using conventional thermal catalysis (e.g., methane (CH4) and nitrogen (N2) activation). The synergy between plasma and a catalytic surface originates from their interactions at the interface. Thus, to better control the plasma-catalyst coupling, it is essential to understand how the reactive chemical environment produced in the plasma phase interacts with catalytic materials.
However, progress in plasma catalysis is hindered by the lack of fundamental understanding of plasma-catalytic surface interactions due to the complexity of plasma catalysis which includes co-existence of plasma-phase and surface chemistry, and heterogeneity of catalytic materials. In this work, the interactions between NTP-activated methane and nitrogen and various model catalytic and non-catalytic surfaces (Ni, Pd, Cu, Ag, Au, SiO2, and KBr) are studied using a newly developed multimodal spectroscopic tool combining polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS), optical emission spectroscopy (OES), and mass spectrometry (MS).
First, this work describes the development of the novel multimodal spectroscopic tool and demonstrates its utility by probing plasma-assisted non-oxidative coupling of CH4 over various surfaces. The results unveil the activation of carbonaceous surface species by an argon NTP on Ni and SiO2, forming hydrogen and C2 hydrocarbons, through distinct pathways. Following this study, the interactions between NTP-activated N2 and various model catalytic surfaces were investigated thoroughly to identify the surface-adsorbed nitrogen-containing species. The results show the potential of non-thermal plasmas to access thermally inaccessible adsorbates and the challenges posed by ppm-level impurities to corrupt the interpretation of plasma catalytic chemistry. Finally, the formation of C-N containing compounds from the plasma-catalytic coupling of CH4 and N2 together over model catalytic surfaces was investigated. C-N coupled products formed were further analyzed with X-ray photoelectron spectroscopy and liquid chromatography-mass spectrometry. The results show the importance of surface chemistry and exposure conditions in surface C-N coupling with plasma stimulation.
DOI: 10.7274/25545994
title: Thermodynamics-Informed Machine Learning for the Design of Sustainable Materials: The Dawn of Digital Molecular Discovery

description: Scientists have traditionally employed trial-and-error methodologies to design novel materials, often complemented by basic heuristic rules or chemical intuition (e.g., “like dissolves like”). However, to date, this simplistic approach has led to the discovery and characterization of only a small fraction of all synthesizable compounds. Data-driven approaches such as machine learning are promising alternative routes to these traditional trial-and-error methodologies. Unfortunately, most machine learning models proposed so far do not embed chemical or thermodynamic information in their architectures and molecular descriptors. In turn, this leads to overly complex models that require a tremendous volume of experimental data to be properly trained.

At the interface between artificial intelligence and green chemistry, the work developed throughout this dissertation uses thermodynamics-informed machine learning to bridge the gap between small, scarce datasets and data-driven approaches. This is accomplished using two major avenues. The first is through the development of active learning workflows, based on Gaussian process machine learning models, that target the description of activity coefficients. This unique approach was particularly directed at capturing the physicochemical properties of mixtures, namely deep eutectic solvents. Active learning was able to efficiently guide the acquisition of experimental data, and, in many cases, a single data point was sufficient to accurately describe mixture properties (namely phase equilibria diagrams), dramatically reducing the effort and cost necessary to characterize novel sustainable materials.

The second major avenue lies in the development of a digital molecular space based on sigma profiles. These molecular descriptors, derived from quantum chemistry, were shown to be a powerful feature set for neural networks, leading to the accurate prediction of assorted physicochemical properties (e.g., boiling points and aqueous solubilities) for organic and inorganic molecules. A graph neural network was also developed to predict sigma profiles, bypassing the need for expensive quantum chemistry calculations. Finally, sigma profiles were shown to behave as a digital molecular space where optimization tasks can be performed. A remarkable example of this was that of Bayesian optimization towards boiling point optimization. Holding no knowledge of chemistry except for the sigma profile and normal boiling temperature of carbon monoxide (the worst possible initial guess), Bayesian optimization found the global maximum of the available normal boiling temperature dataset (over 1000 molecules encompassing more than 40 families of organic and inorganic compounds) in just fifteen iterations (i.e., fifteen property measurements), cementing sigma profiles as an ideal digital chemical space for molecular optimization and discovery, particularly when little experimental data is available.
DOI: 10.7274/25545994.v1
title: Thermodynamics-Informed Machine Learning for the Design of Sustainable Materials: The Dawn of Digital Molecular Discovery

description: Scientists have traditionally employed trial-and-error methodologies to design novel materials, often complemented by basic heuristic rules or chemical intuition (e.g., “like dissolves like”). However, to date, this simplistic approach has led to the discovery and characterization of only a small fraction of all synthesizable compounds. Data-driven approaches such as machine learning are promising alternative routes to these traditional trial-and-error methodologies. Unfortunately, most machine learning models proposed so far do not embed chemical or thermodynamic information in their architectures and molecular descriptors. In turn, this leads to overly complex models that require a tremendous volume of experimental data to be properly trained.

At the interface between artificial intelligence and green chemistry, the work developed throughout this dissertation uses thermodynamics-informed machine learning to bridge the gap between small, scarce datasets and data-driven approaches. This is accomplished using two major avenues. The first is through the development of active learning workflows, based on Gaussian process machine learning models, that target the description of activity coefficients. This unique approach was particularly directed at capturing the physicochemical properties of mixtures, namely deep eutectic solvents. Active learning was able to efficiently guide the acquisition of experimental data, and, in many cases, a single data point was sufficient to accurately describe mixture properties (namely phase equilibria diagrams), dramatically reducing the effort and cost necessary to characterize novel sustainable materials.

The second major avenue lies in the development of a digital molecular space based on sigma profiles. These molecular descriptors, derived from quantum chemistry, were shown to be a powerful feature set for neural networks, leading to the accurate prediction of assorted physicochemical properties (e.g., boiling points and aqueous solubilities) for organic and inorganic molecules. A graph neural network was also developed to predict sigma profiles, bypassing the need for expensive quantum chemistry calculations. Finally, sigma profiles were shown to behave as a digital molecular space where optimization tasks can be performed. A remarkable example of this was that of Bayesian optimization towards boiling point optimization. Holding no knowledge of chemistry except for the sigma profile and normal boiling temperature of carbon monoxide (the worst possible initial guess), Bayesian optimization found the global maximum of the available normal boiling temperature dataset (over 1000 molecules encompassing more than 40 families of organic and inorganic compounds) in just fifteen iterations (i.e., fifteen property measurements), cementing sigma profiles as an ideal digital chemical space for molecular optimization and discovery, particularly when little experimental data is available.
DOI: 10.7274/25563168
title: Development and Implementation of an Enhanced Free Energy Perturbation Approach to Rapidly Assess Gas Solubility in Charged Liquids

description: Gas solubility simulations are useful for guiding the discovery of useful materials and conditions for processes involving gas absorption or adsorption. We present an efficient, general-purpose variant of the test particle insertion method for computing chemical potentials of gaseous solutes in fluids or porous solids. The method is implemented in the Monte Carlo molecular simulation engine Cassandra, but receiving phase configurations are independent of this process and may be pre-sampled by other molecular simulation engines such as molecular dynamics codes. Efficiency enhancement options present in this method include configurational biasing, various accelerated atomic overlap detection methods, and bitmapped cavity biasing. The method's implementation is extremely optimized compared to prior configurational biasing implementations in Cassandra. When applied to the estimation of Henry's law constants of atomistic hydrofluorocarbons in ionic liquids, the speed enhancements and implementation optimizations can result in a speedup of more than three orders of magnitude compared to conventional configurational biasing methods without sacrificing accuracy. We found good agreement between this method and Hamiltonian replica exchange (HREX) for Henry's law constant and absorption isotherm estimation. This embarrassingly parallel method is especially well suited for screening Henry's law constants of many small gases in the same solvents, since a liquid trajectory can be reused for as many solutes as desired. We apply this method to fast, high-throughput calculation of Henry's law constant for 16 small gases in 23 ionic liquids. We recommend future use of this method for additional studies of gas solubility in liquids and porous solids due to its extreme efficiency.
DOI: 10.7274/25563168.v1
title: Development and Implementation of an Enhanced Free Energy Perturbation Approach to Rapidly Assess Gas Solubility in Charged Liquids

description: Gas solubility simulations are useful for guiding the discovery of useful materials and conditions for processes involving gas absorption or adsorption. We present an efficient, general-purpose variant of the test particle insertion method for computing chemical potentials of gaseous solutes in fluids or porous solids. The method is implemented in the Monte Carlo molecular simulation engine Cassandra, but receiving phase configurations are independent of this process and may be pre-sampled by other molecular simulation engines such as molecular dynamics codes. Efficiency enhancement options present in this method include configurational biasing, various accelerated atomic overlap detection methods, and bitmapped cavity biasing. The method's implementation is extremely optimized compared to prior configurational biasing implementations in Cassandra. When applied to the estimation of Henry's law constants of atomistic hydrofluorocarbons in ionic liquids, the speed enhancements and implementation optimizations can result in a speedup of more than three orders of magnitude compared to conventional configurational biasing methods without sacrificing accuracy. We found good agreement between this method and Hamiltonian replica exchange (HREX) for Henry's law constant and absorption isotherm estimation. This embarrassingly parallel method is especially well suited for screening Henry's law constants of many small gases in the same solvents, since a liquid trajectory can be reused for as many solutes as desired. We apply this method to fast, high-throughput calculation of Henry's law constant for 16 small gases in 23 ionic liquids. We recommend future use of this method for additional studies of gas solubility in liquids and porous solids due to its extreme efficiency.
DOI: 10.7274/25604625
title: Molecular Models to Predict the Influence of Templating Molecules on the Structure and Organization of Zeolites

description: Zeolites are crystalline aluminosilicate materials that are crucial in the modern chemical industry. Their material properties are defined mainly by their framework (FW) connectivity and the local arrangements of heteroatoms like Al or defects. Structure-directing agents (SDAs) are used during synthesis to control zeolite structure. In this dissertation, I used novel techniques to develop computational models and gain insights into the relationships between SDAs and their impact on the crystallization patterns of zeolite. First, I improve the efficacy of the traditional thermodynamic model in predicting the phase selectivity of organic SDAs between full siliceous CHA and AEI. Then, I introduced the Al into zeolite and explored the influence of SDAs on Al distributions in CHA with supercell density functional theory (DFT) models. Finally, I extended the model to further include charged vacancy defects and demonstrated their interaction with SDAs and other Al is indistinguishable from other framework Al. These works will serve as a guide for the industry, enabling the synthesis of zeolites with enhanced catalytic performance at reduced costs.
DOI: 10.7274/25604625.v1
title: Molecular Models to Predict the Influence of Templating Molecules on the Structure and Organization of Zeolites

description: Zeolites are crystalline aluminosilicate materials that are crucial in the modern chemical industry. Their material properties are defined mainly by their framework (FW) connectivity and the local arrangements of heteroatoms like Al or defects. Structure-directing agents (SDAs) are used during synthesis to control zeolite structure. In this dissertation, I used novel techniques to develop computational models and gain insights into the relationships between SDAs and their impact on the crystallization patterns of zeolite. First, I improve the efficacy of the traditional thermodynamic model in predicting the phase selectivity of organic SDAs between full siliceous CHA and AEI. Then, I introduced the Al into zeolite and explored the influence of SDAs on Al distributions in CHA with supercell density functional theory (DFT) models. Finally, I extended the model to further include charged vacancy defects and demonstrated their interaction with SDAs and other Al is indistinguishable from other framework Al. These works will serve as a guide for the industry, enabling the synthesis of zeolites with enhanced catalytic performance at reduced costs.
DOI: 10.18742/26668330
title: PSYD_0602: BANGALORE (NIMHANS)

description: <i>This record is part of the Psy-ShareD database. The Psy-ShareD database will host pre-existing structural MRI data collected at different global sites that will be suitable for region of interest, voxel-based morphometry, cortical thickness and surface area analytical approaches. To access this database, complete the Data Access Request Form by following the link on this page under the Licence ‘Psy-ShareD Data Access Request’. For more information, follow the link listed in Related Materials.</i><b>Intermittent Theta Burst Stimulation of Cerebellar Vermis in Schizophrenia: Impact on Negative Symptoms and Brain Connectivity</b><b>Contributors and PIs:</b> Dr. Rakshathi BasavarajuDepartment of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, Karnataka, India<b>Key Details & Variables</b><b>Data Type</b>Image data/Feature data<b>Age Range</b>20-53<b>Number of Participants</b>59<b>Diagnostic Criteria</b>SCZ<b>Number of Clinical Subjects</b>59<b>Number of Clinical Subjects (follow-up)</b>N/A<b>Number of Control Subjects</b>N/A<b>Number of Control Subjects (follow-up)</b>N/A<b>Demographic Information</b>Age, Sex, Education<b>Clinical Measures</b>SAPS, SANS, CGSICH, CDSS, SAS<b>Cognitive Measures</b>Working Memory, Executive Function, Episodic Memory, Social Cognition<b>Clinical Functioning Measures</b>N/A<b>Antipsychotic Medication Information Recorded</b>No<b>Antipsychotic Medication Name/ Antipsychotic Type</b>N/A<b>Antipsychotic Dose Recorded</b>N/A<b>CPZ Equivalents Recorded</b>N/A<b>Other Medication Variables Recorded</b>No<b>Recreational Drug/ Alcohol Use</b>Cigarette Smoking<i>Note: the above table features key details and variables and is not an exhaustive list.</i>
DOI: 10.18742/26668330.v1
title: PSYD_0602: BANGALORE (NIMHANS)

description: <i>This record is part of the Psy-ShareD database. The Psy-ShareD database will host pre-existing structural MRI data collected at different global sites that will be suitable for region of interest, voxel-based morphometry, cortical thickness and surface area analytical approaches. To access this database, complete the Data Access Request Form by following the link on this page under the Licence ‘Psy-ShareD Data Access Request’. For more information, follow the link listed in Related Materials.</i><b>Intermittent Theta Burst Stimulation of Cerebellar Vermis in Schizophrenia: Impact on Negative Symptoms and Brain Connectivity</b><b>Contributors and PIs:</b> Dr. Rakshathi BasavarajuDepartment of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, Karnataka, India<b>Key Details & Variables</b><b>Data Type</b>Image data/Feature data<b>Age Range</b>20-53<b>Number of Participants</b>59<b>Diagnostic Criteria</b>SCZ<b>Number of Clinical Subjects</b>59<b>Number of Clinical Subjects (follow-up)</b>N/A<b>Number of Control Subjects</b>N/A<b>Number of Control Subjects (follow-up)</b>N/A<b>Demographic Information</b>Age, Sex, Education<b>Clinical Measures</b>SAPS, SANS, CGSICH, CDSS, SAS<b>Cognitive Measures</b>Working Memory, Executive Function, Episodic Memory, Social Cognition<b>Clinical Functioning Measures</b>N/A<b>Antipsychotic Medication Information Recorded</b>No<b>Antipsychotic Medication Name/ Antipsychotic Type</b>N/A<b>Antipsychotic Dose Recorded</b>N/A<b>CPZ Equivalents Recorded</b>N/A<b>Other Medication Variables Recorded</b>No<b>Recreational Drug/ Alcohol Use</b>Cigarette Smoking<i>Note: the above table features key details and variables and is not an exhaustive list.</i>