Accurate prediction of the sensitivity properties of high-energy materials (HEMs) and the study of their decomposition mechanisms are two major focuses within energetics research. Due to the hazards associated with the synthesis and handling of energetic materials, predictive models for HEM sensitivity are of great importance in enabling the safe and efficient development of future HEMs. Traditional predictive modeling of HEM decomposition via machine learning algorithms generally displays limited interpretability, while mechanistic studies of HEMs typically focus on small subsets of structurally analogous compounds lacking generalizability. This study aims to bridge the gap between predictive modeling and computational mechanistic analysis of HEMs, with the goal of providing chemically interpretable models for HEM sensitivity property prediction. Herein, we disclose the use of multivariate linear regression (MLR) modeling for the prediction of the decomposition temperature and impact sensitivity of HEMs. We report an explosophore-based approach to sensitivity property prediction featuring an ensemble of quantum mechanical parameters and computational workflows that enable rapid parameterization and modeling of energetic functional groups. We then employ these methods to accurately predict sensitivity properties of nitrogen-rich tetrazole and azide HEMs. These statistical MLR models are readily interpreted based on the principles of physical organic chemistry, producing structure-property relationships to guide the rational design of new HEMs. Furthermore, we extend our explosophore-based approach to predict the sensitivity properties of HEMs containing multiple, non-equivalent energetic functional groups through the identification of molecular triggers for the bulk decomposition of HEMs. Finally, we showcase the viability of our methods towards ab initio virtual screening of HEMs through predictive modeling of external test sets of tetrazole HEMs using structures and parameters generated exclusively in silico.
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Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach
The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.
more » « less- Award ID(s):
- 1753167
- NSF-PAR ID:
- 10197328
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Chemistry
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2399-3669
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This dataset consists of 800 coordinate files (in the CHARMM psf/cor format) for the QM/MM minimum energy pathways of the acylation reactions between a Class A beta-lactamases (Toho-1) and two beta-lactam antibiotic molecules (ampicillin and cefalexin).
These files are:
- toho_amp.r1-ae.zip: The R1-AE acylation pathways for Toho-1/Ampicillin (200 pathways);
- toho_amp.r2-ae.zip: The R2-AE acylation pathways for Toho-1/Ampicillin (200 pathways);
- toho_cex.r1-ae.zip: The R1-AE acylation pathways for Toho-1/Cefalexin (200 pathways);
- toho_cex.r2-ae.zip: The R2-AE acylation pathways for Toho-1/Cefalexin (200 pathways);
- energies.zip: the replica energies at B3LYP-D3/6-31+G**/C36 level;
- chelpgs.zip: the ChElPG charges of all reactant replicas at B3LYP-D3/6-31+G**/C36 level;
- farrys.zip: the featurzied NumPy arrays for model training;
- peephole.zip: an example file for how the optimized MEPs look like;
- dftb3_benchmark.zip: the reference calculations to justify the use of DFTB3/3OB-F/C36 in MEP optimizations, the reference level of theory is B3LYP-D3/6-31G**/C36.
The R1-AE pathways are the acylation uses Glu166 as the general base; the R2-AE pathways uses Lys73 and Glu166 as the concerted base.
All QM/MM pathways are optimized at the DFTB3/3OB-f/CHARMM36 level of theory.
Z. Song et al Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways. ACS Physical Chemistry Au, in press. DOI: 10.1021/acsphyschemau.2c00005
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Abstract Chemical energy ferroelectrics are generally solid macromolecules showing spontaneous polarization and chemical bonding energy. These materials still suffer drawbacks, including the limited control of energy release rate, and thermal decomposition energy well below total chemical energy. To overcome these drawbacks, we report the integrated molecular ferroelectric and energetic material from machine learning-directed additive manufacturing coupled with the ice-templating assembly. The resultant aligned porous architecture shows a low density of 0.35 g cm−3, polarization-controlled energy release, and an anisotropic thermal conductivity ratio of 15. Thermal analysis suggests that the chlorine radicals react with macromolecules enabling a large exothermic enthalpy of reaction (6180 kJ kg−1). In addition, the estimated detonation velocity of molecular ferroelectrics can be tuned from 6.69 ± 0.21 to 7.79 ± 0.25 km s−1by switching the polarization state. These results provide a pathway toward spatially programmed energetic ferroelectrics for controlled energy release rates.
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Abstract A catalytic surface should be stable under reaction conditions to be effective. However, it takes significant effort to screen many surfaces for their stability, as this requires intensive quantum chemical calculations. To more efficiently estimate stability, we provide a general and data-efficient machine learning (ML) approach to accurately and efficiently predict the surface energies of metal alloy surfaces. Our ML approach introduces an element-centered fingerprint (ECFP) which was used as a vector representation for fitting models for predicting surface formation energies. The ECFP is significantly more accurate than several existing feature sets when applied to dilute alloy surfaces and is competitive with existing feature sets when applied to bulk alloy surfaces or gas-phase molecules. Models using the ECFP as input can be quite general, as we created models with good accuracy over a broad set of bimetallic surfaces including most d-block metals, even with relatively small datasets. For example, using the ECFP, we developed a kernel ridge regression ML model which is able to predict the surface energies of alloys of diverse metal combinations with a mean absolute error of 0.017 eV atom−1. Combining this model with an existing model for predicting adsorption energies, we estimated segregation trends of 596 single-atom alloys (SAAs)with and without CO adsorbed on these surfaces. As a simple test of the approach, we identify specific cases where CO does not induce segregation in these SAAs.
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Abstract The mineral apatite, Ca10(PO4)6(F,OH,Cl)2, incorporates sulfur (S) during crystallization from S-bearing hydrothermal fluids and silicate melts. Our previous studies of natural and experimental apatite demonstrate that the oxidation state of S in apatite varies systematically as a function of oxygen fugacity (fO2). The S oxidation states –1 and –2 were quantitatively identified in apatite crystallized from reduced, S-bearing hydrothermal fluids and silicate melts by using sulfur K-edge X-ray absorption near-edge structure spectroscopy (S-XANES) where S 6+/ΣS in apatite increases from ~0 at FMQ-1 to ~1 at FMQ+2, where FMQ refers to the fayalite-magnetite-quartz fO2 buffer. In this study, we employ quantum-mechanical calculations to investigate the atomistic structure and energetics of S(-I) and S(-II) incorporated into apatite and elucidate incorporation mechanisms.
One S(-I) species (disulfide, S22−) and two S(-II) species (bisulfide, HS−, and sulfide, S2−) are investigated as possible forms of reduced S species in apatite. In configuration models for the simulation, these reduced S species are positioned along the c-axis channel, originally occupied by the column anions F, Cl, and OH in the end-member apatites. In the lowest-energy configurations of S-incorporated apatite, disulfide prefers to be positioned halfway between the mirror planes at z = 1/4 and 3/4. In contrast, the energy-optimized bisulfide is located slightly away from the mirror planes by ~0.04 fractional units in the c direction. The energetic stability of these reduced S species as a function of position along the c-axis can be explained by the geometric and electrostatic constraints of the Ca and O planes that constitute the c-axis channel.
The thermodynamics of incorporation of disulfide and bisulfide into apatite is evaluated by using solid-state reaction equations where the apatite host and a solid S-bearing source phase (pyrite and Na2S2(s) for disulfide; troilite and Na2S(s) for sulfide) are the reactants, and the S-incorporated apatite and an anion sink phase are the products. The Gibbs free energy (ΔG) is lower for incorporation with Na-bearing phases than with Fe-bearing phases, which is attributed to the higher energetic stability of the iron sulfide minerals as a source phase for S than the sodium sulfide phases. The thermodynamics of incorporation of reduced S is also evaluated by using reaction equations involving dissolved disulfide and sulfide species [HnS(aq)(2−n) and HnS(aq)(2−n); n = 0, 1, and 2] as a source phase. The ΔG of S-incorporation increases for fluorapatite and chlorapatite, and decreases for hydroxylapatite, as these species are protonated (i.e., as n changes from 0 to 2). These thermodynamic results demonstrate that the presence of reduced S in apatite is primarily controlled by the chemistry of magmatic and hydrothermal systems where apatite forms (e.g., an abundance of Fe; solution pH). Ultimately, our methodology developed for evaluating the thermodynamics of S incorporation in apatite as a function of temperature, pH, and composition is highly applicable to predicting the trace and volatile element incorporation in minerals in a variety of geological systems. In addition to solid-solid and solid-liquid equilibria treated here at different temperatures and pH, the methodology can be easily extended to different pressure conditions by just performing the quantum-mechanical calculations at elevated pressures.
