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- Arismendi Arrieta, Daniel Jose, et al.
(författare)
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H2O2(s) and H2O22H2O(s) crystals compared with ices : DFT functional assessment and D3 analysis
- 2023
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Ingår i: Journal of Chemical Physics. - : American Institute of Physics (AIP). - 0021-9606 .- 1089-7690. ; 159:19
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Tidskriftsartikel (refereegranskat)abstract
- The H2O and H2O2 molecules resemble each other in a multitude of ways as has been noted in the literature. Here, we present density functional theory (DFT) calculations for the H2O2(s) and H2O2·2H2O(s) crystals and make selected comparisons with ice polymorphs. The performance of a number of dispersion-corrected density functionals—both self-consistent and a posteriori ones—are assessed, and we give special attention to the D3 correction and its effects. The D3 correction to the lattice energies is large: for H2O2(s) the D3 correction constitutes about 25% of the lattice energy using PBE, much more for RPBE, much less for SCAN, and it primarily arises from non-H-bonded interactions out to about 5 Å.The large D3 corrections to the lattice energies are likely a consequence of several effects: correction for missing dispersion interaction, the ability of D3 to capture and correct various other kinds of limitations built into the underlying DFT functionals, and finally some degree of cell-contraction-induced polarization enhancement. We find that the overall best-performing functionals of the twelve examined are optPBEvdW and RPBE-D3. Comparisons with DFT assessments for ices in the literature show that where the same methods have been used, the assessments largely agree.
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| 2. |
- Heinen, Stefan, et al.
(författare)
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Reducing training data needs with minimal multilevel machine learning (M3L)
- 2024
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Ingår i: Machine Learning. - : Institute of Physics Publishing (IOPP). - 2632-2153. ; 5:2
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Tidskriftsartikel (refereegranskat)abstract
- For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating them with basis sets TZ, cc-pVTZ, and AVTZ-F12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of ∼1.01, 1.1, 3.8, 13.8, and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9LCCSD (T), Electrolyte Genome Project, QM9CACESD(T), and QM9CECASD(T), respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder: LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and τ-HCTH, B3LYP*(VWN5), and TPSSH.
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| 3. |
- Karandashev, Konstantin, et al.
(författare)
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Evolutionary Monte Carlo of QM Properties in Chemical Space : Electrolyte Design
- 2023
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Ingår i: Journal of Chemical Theory and Computation. - : American Chemical Society (ACS). - 1549-9618 .- 1549-9626. ; 19:23, s. 8861-8870
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Tidskriftsartikel (refereegranskat)abstract
- Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science but also a very difficult one due to the vast number of possible molecular systems. We propose an evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favorable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely, minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO–LUMO gap; optimization was carried out over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) data sets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus feasible only thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches.
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