Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions DOI Creative Commons
Michael G. Taylor, Tzuhsiung Yang,

Sean Lin

et al.

The Journal of Physical Chemistry A, Journal Year: 2020, Volume and Issue: 124(16), P. 3286 - 3299

Published: March 28, 2020

Determination of ground-state spins open-shell transition-metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how alone can be used guide assignment spin from experimentally determined crystal structures complexes. We first identify the limits distance-based heuristics distributions metal-ligand bond lengths over 2000 unique mononuclear Fe(II)/Fe(III) To overcome these limits, employ artificial neural networks (ANNs) predict spin-state-dependent classify experimental based on agreement ANN predictions. Although trained hybrid density functional theory data, exploit method-insensitivity geometric enable ground states for majority (ca. 80-90%) structures. utility by data-mining literature spin-crossover (SCO) complexes, which have observed temperature-dependent changes, correctly assigning almost all (>95%) in 46 Fe(II) SCO complex set. This approach represents a promising complement more conventional energy-based spin-state at low cost machine learning model.

Language: Английский

Paving the road towards automated homogeneous catalyst design DOI Creative Commons
Adarsh V. Kalikadien,

A.H. Mirza,

Aydin Najl Hossaini

et al.

ChemPlusChem, Journal Year: 2024, Volume and Issue: 89(7)

Published: Jan. 26, 2024

In the past decade, computational tools have become integral to catalyst design. They continue offer significant support experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning garnered considerable attention their expansive capabilities. This Perspective provides an overview of diverse initiatives in realm design introduces our automated tailored high-throughput silico exploration chemical space. While valuable insights are gained through methods analysis space, degree automation modularity key. We argue that integration data-driven, modular workflows is key enhancing homogeneous on unprecedented scale, contributing advancement research.

Language: Английский

Citations

14
Directional multiobjective optimization of metal complexes at the billion-system scale DOI
Hannes Kneiding, Ainara Nova, David Balcells

et al.

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(4), P. 263 - 273

Published: March 29, 2024

Language: Английский

Citations

14
A Practical Guide to Computational Tools for Engineering Biocatalytic Properties DOI Open Access
Aitor Vega, Antoni Planas, Xevi Biarnés

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 980 - 980

Published: Jan. 24, 2025

The growing demand for efficient, selective, and stable enzymes has fueled advancements in computational enzyme engineering, a field that complements experimental methods to accelerate discovery. With plethora of software tools available, researchers from different disciplines often face challenges selecting the most suitable method meets their requirements available starting data. This review categorizes engineering based on capacity enhance following specific biocatalytic properties biotechnological interest: (i) protein–ligand affinity/selectivity, (ii) catalytic efficiency, (iii) thermostability, (iv) solubility recombinant production. By aligning with respective scoring functions, we aim guide researchers, particularly those new methods, appropriate design protein campaigns. De novo design, involving creation novel proteins, is beyond this review’s scope. Instead, focus practical strategies fine-tuning enzymatic performance within an established reference framework natural proteins.

Language: Английский

Citations

1
Established and Emerging Computational Tools to Study Homogeneous Catalysis—From Quantum Mechanics to Machine Learning DOI Creative Commons
Ignacio Funes‐Ardoiz, Franziska Schoenebeck

Chem, Journal Year: 2020, Volume and Issue: 6(8), P. 1904 - 1913

Published: Aug. 1, 2020

Language: Английский

Citations

64
Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions DOI Creative Commons
Michael G. Taylor, Tzuhsiung Yang,

Sean Lin

et al.

The Journal of Physical Chemistry A, Journal Year: 2020, Volume and Issue: 124(16), P. 3286 - 3299

Published: March 28, 2020

Determination of ground-state spins open-shell transition-metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how alone can be used guide assignment spin from experimentally determined crystal structures complexes. We first identify the limits distance-based heuristics distributions metal-ligand bond lengths over 2000 unique mononuclear Fe(II)/Fe(III) To overcome these limits, employ artificial neural networks (ANNs) predict spin-state-dependent classify experimental based on agreement ANN predictions. Although trained hybrid density functional theory data, exploit method-insensitivity geometric enable ground states for majority (ca. 80-90%) structures. utility by data-mining literature spin-crossover (SCO) complexes, which have observed temperature-dependent changes, correctly assigning almost all (>95%) in 46 Fe(II) SCO complex set. This approach represents a promising complement more conventional energy-based spin-state at low cost machine learning model.

Language: Английский

Citations

61